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Analysis of impacts of climate change policies on energy security

Final Report

OUR MISSION: A SUST AIN ABLE ENERGY SUPPLY FOR EVERYONE

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Authors:

Ecofys: James Greenleaf, Robert Harmsen, Tana Angelini

ERAS: David Green, Amanda Williams

Redpoint Energy: Oliver Rix

Nicolas Lefevre

William Blyth

November 2009

© Ecofys 2009

Analysis of impacts of climate change policies on energy security

Final report

by order of:

European Commission DG Environment

07.0307/2008/515198/SER/C.5

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Executive Summary

Background

The use of energy is a prerequisite for virtually all economic activity, and it is crucial to be able to access sufficient amounts of energy at acceptable cost (both from an economic and environmental perspective), which gives rise to the notion of ‘energy security’. The current EU energy system has its own inherent energy security risks. However, the introduction of a number of existing and proposed climate change-related policies (such as those of the EU’s Climate and Energy Package adopted in December 2008) will change the structure of the energy system significantly in the future (e.g. via greater use of renewables to meet emissions targets), thus altering these associated risks.

This study is focused on the interaction between achieving a sustainable energy system and improving energy security, as well as aiming to develop a methodology to identify and assess (quantitatively where possible) the impact of (and interactions between) such climate policies on energy security. This will help guide policy making by identifying areas, and the extent to which, climate policy can reinforce energy security objectives. These areas will be analysed in 2020 and 2030 (at both the EU and global level where appropriate) with respect to political, technical, economic and extreme weather-related issues.

To do so, we have taken a step-wise approach as detailed below:

1. Review of the concept of energy security and development of an overarching framework to be used throughout this study

2. Qualitative assessment of the interactions between climate policy and energy security

3. Review of existing indicators used to assess energy security

4. Development of a specific quantitative approach to assessing the impacts of climate change policy on energy security

5. Development of a spreadsheet tool based on these indicators to analyse the impact of a number of the recently proposed climate policies on future energy security (using existing energy system modelling work undertaken to support development of these policies)

6. Case-study analysis of other policies in various Member States to test the robustness of the framework

7. Conclusions and recommendations

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Concept of energy security

Definitions of energy security in the literature can be grouped into two broad categories: the first focuses on economic principles and the second is more policy oriented.

1. From an economic perspective, energy insecurity is the loss of welfare resulting from a change in the price or physical availability of energy.

2. Policy oriented definitions typically highlight basic requirements of a secure energy system. Often these stress the need for accessibly to, and affordability of energy. Other examples include availability and acceptability.

While perfectly sound in their own right, neither definition is suitable to describe fully the effects of climate change policies on energy security. In defining a suitable framework for the purpose of this study, additional information is therefore needed. The dilemma is in finding a suitable middle ground between uncertain aggregate welfare estimates on the one hand and unstructured expert judgment on the other.

To tackle this problem we used a bottom-up approach, starting from broad definitions of energy security and then looking at specific cases based on actual experience. This experience was used to establish a simple classification of root causes of energy insecurity. Specifically, three broad, independent categories of energy security root causes were defined: (1) Extreme events, (2) Inadequate market structures and (3) Resource concentration

These three categories of supply-side energy insecurity can be broken down to provide a typology of the main root causes of energy insecurity, as shown in the table below.

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Table 0 - 1 Classification of energy insecurity root causes

Category Type Brief description Extreme weather

Extreme weather events can temporarily disable energy infrastructures and the supply of energy. A recent example is the impact of Hurricane Katrina, which hit the Gulf of Mexico in 2005, disabling a significant portion of the US oil and gas production and processing capacity. There are however many other possible extreme weather events with potential energy security consequences including those which impact on the demand side (e.g. exceptionally cold or hot days) or on the supply side (e.g. reduced cooling water availability).

Large scale accidents

Much like extreme weather events, accidents can lead to unplanned outages of key energy infrastructures.

Acts of terrorism Acts of terrorism against key infrastructures (e.g. refineries or pipelines) or bottlenecks along specific energy trade routes (e.g. the straight of Hormuz) can cause disruptions to energy systems.

Extreme events

Strikes Due to the strategic nature of energy, strikes or other forms of social unrest may specifically target the operation of key energy system components.

Insufficient investments in new capacity

Market structures which fail to generate timely investments in key energy system infrastructures can contribute to making the system more vulnerable and ultimately generate energy insecurity.

Inadequate market structure

Load balancing failure in electricity markets

Because electricity is not storable in any meaningful volumes system operators must effectively balance supply and demand in real time to ensure system reliability. The task is challenging and requires that certain technical characteristics be met. When this is not the case systems sometime fail or do not operate in an efficient manner causing a loss of welfare for users.

Supply shortfall associated with resource concentration

Due to the concentration of resources in certain regions of the world, exploration and production as well as transport of fuels are also concentrated. This generates a certain degree of market power which can adversely affect energy systems.

This is not an exhaustive list of all possible causes of energy insecurity but is one which is practical and which focuses on the main types of concern based on actual experience.

There are other potential areas of energy insecurity which have not translated into price or physical availability concerns to-date and which are therefore not included in the quantitative analysis undertaken in this report. Examples are negative effects of market power on energy prices (i.e. monopolistic, oligopolistic structures), and depletion of fossil fuels reserves.

Each cause of energy insecurity listed above eventually translates into the welfare impacts through specific causal links as illustrated below. The stages of the causal mechanism have been developed to capture all the possible effects on the energy system. The magnitude of the impacts is then analysed via the specific indicators identified for each stage.

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Illustration 0 - 1 Generic causal mechanisms of energy insecurity

Linkages between EU climate change policy and energy security

The key link between climate change policies and energy security is through the impact of the climate change policies on the energy system. Climate change policies affect the overall level of consumption of a specific fuel – for example, by fuel switching or demand reduction. In doing so, it affects the fuel and technology mix of a country and as such may interact with energy security. Changes at the end of the energy supply chain induced by climate change policies potentially affect the energy security impacts to the EU at all earlier stages of the chain back to international imports. For the analysis the following stepwise approach was followed:

i ) Identify the generic options for climate change mitigation

ii ) Link the generic options to the climate change policies

iii ) Identify the likely short and long term impacts on the energy system of each of the generic options

iv ) Systematically link the impacts on the energy system to likely changes in the risk and magnitude of energy security impacts

v ) Summarize the impact of the climate change policies on energy security

The results of the analysis are summarised in the table below. Illustrated are the effects of generic climate change mitigation options on the risks/magnitude of energy security impacts of the different root causes. Due to the effects of fuel switching and demand reduction for primary fuels (either in end-use demand or indirectly through improved conversion efficiencies), climate change effects interact with the majority of energy security issues.

In some cases the interactions are clear, for example, demand reduction leads to positive effects for all root causes with the exception of insufficient investment in new capacity. In other cases the link is less clear with a mix of positive and negative effects.

Stage I Energy

insecurity root

cause

Stage II Effect on sector of supply chain

Stage III Knock-on effects on

other sectors of supply chain

Stage IV Effect on demand sector

Stage V Welfare Effects

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It is therefore important to interpret the table with some caution as the scoring is some cases is not straightforward and may be misleading. Details of the analysis can be found in chapter 3.

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Table 0 - 2 Summary of interactions between generic EU climate mitigation options and energy security

Extreme events Inadequate market structures Climate change mitigation

options Extreme weather


Acts of terrorism

Strikes Insufficient investments in new

capacity

Load balancing

failure

Supply shortfall associated with resource

concentration*

Energy efficiency improvement

+ + + + - (st) / ≈ (lt) + ++

Reducing overall levels of activities

+ + + + - (st) / ≈ (lt) + ++

Dem

and

Moving towards a less energy intensive economy

+ + + + - (st) / ≈ (lt) + ++

Fuel switching to RES (for electricity)

++/- ++/- ++/- ++ - (st) / ≈ (lt) / ? -- ++

Fuel switching to RES (for heat)

++/- ++ ++ ++ ? ≈ ++

Fuel switching to nuclear (electricity)

++ (lt) ++/-- (lt) ++/-- (lt) ++ (lt) - (st) / ≈ (lt) - (lt) ++/- (lt)

Fuel switching to RES (biogas)

++/- ++ ++ ++ - (st) / ≈ (lt) / ? ≈ +

Fuel switching to RES (transport)

++/- ++/- ++/- ++/- - (st) / ≈ (lt) ? ++/-/?

Fuel switching from high-carbon to low carbon fuels

-/-- -/-- -/-- ≈ ? ≈ --

Improvement conversion efficiencies

++ ++ ++ ++ - (st) / ≈ (lt) ? +/-

Improvement efficiency transmission and distribution

+(lt) +(lt) +(lt) +(lt) - (st) / ≈ (lt) ≈ + (lt)

Sup

ply

Capture and storage of CO2 emissions

+/- (lt) +/- (lt) +/- (lt) +/- (lt) - (st) / ≈ (lt) ≈ ++ / - (lt)

Note: ≈ negligible effect; ? uncertain effect; ++/+/?/-/-- mix of effects; + weak positive effect; ++ strong positive effect; - weak negative effect; -- strong negative effect; (lt) long term / (st) short term. * Focused on EU mitigation efforts, as mentioned above, a concerted global mitigation effort could lead to increased resource amongst the remaining suppliers where it is still economical to produce fossil fuels.

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Review of existing indicators of energy security

Once the interactions between climate change policies and energy security root causes were identified we have carried out an extensive literature review of potentially relevant indicators. A full list is available in Appendix B 1. The review has identified two main types of energy security indicators:

• Vulnerability-based indicators: which measure inputs that can be considered a proxy for the potential risk and/or magnitude of an energy security impact, should it actually occur. For example, import dependence provides a proxy for the vulnerability of the energy system to a physical interruption to energy imports rather than a measure of the actual disruption to imports.

• Outcome-based indicators: by contrast, these indicators aim to measure the actual outcome of energy insecurity. In an ideal world an outcome-based indicator would measure the actual welfare impact of energy insecurity. However, given the inherent uncertainties in estimating this, an estimate of the level of physical unavailability of energy is normally used. Only those in Appendix B - B 3 can really be considered outcome-based indicators, and include: expected energy unserved; a security of supply function for the MERGE model, and cost of failure of the electricity system.

The indicators can be related back to the Stages in the generic causal mechanism discussed earlier. An outcome-based indicator targeted at a particular stage will capture the impacts of each of the preceding steps leading up to this point. By contrast, a vulnerability-based indicator will only provide a proxy for the potential risk/magnitude of the specific stage it is targeted at – see diagram below. It is possible for the vulnerability-indicator to have multiple components targeted at different stages.

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Illustration 0 - 2 Indicator type and link to causal mechanisms of energy security

It is useful to note that no aggregate indicator provides an adequate measure of all the relevant root causes of energy insecurity and current attempts to do so lead to a strong trade-off in transparency.

Development of a specific quantitative approach

The key focus of this study is on developing a quantitative approach that can draw independently on the results of modelling work of the impact of climate change policy on the energy system. Given the complexity of outcome-based approaches the focus of our approach is therefore upon the development of the most relevant and applicable vulnerability indicators.

By using the climate policy modelling results under a baseline scenario (i.e. without the effect of the policy) and a with policy scenario, the difference between the evolution of the energy security indicator in each case can be used to determine whether, and to what extent, the policy has increased or decreased the ‘vulnerability of the EU’ to previously identified energy security risks (see figure below).

The overall approach is based on a number of underlying principles drawn from the evaluation criteria used for existing indicators. These principles are: suitability, transparency, data availability and ability to forecast.

Stage I Energy

insecurity root

cause





Example of outcome based

indicator up to stage III

& & &

Example of vulnerability based indicator with

multiple components targeted at each stage

Stage I

proxy Stage II

proxy Stage III

proxy Stage IV

proxy

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Illustration 0 - 3 Basic approach to assessing impact of climate policy on energy security

Baseline scenario

Time

Vulnerability to energy security issue - indicator X

+ve impact onenergy security from

policy

2020

Base year policy introduced

Pre-policy “vulnerability”

With climate policy scenario scenario A

2030

-ve impact onenergy security

from policy

With climate policy scenario scenario B

Historic trend

In addition to these, there is also number of practical issues that have been considered in the application of the indicators. In particular, the assessment of different energy sources: when the importance of a specific fuel is greater than all other energy sources only that fuel is analysed. Also, the vulnerability of a member state is dependent on the level of integration of its network with neighbouring countries. This is especially true for the gas and electricity network, thus, when the networks are integrated, the analysis is made at regional level rather the country level for greater accuracy. Finally, the issue of stage IV proxies: the Stage IV component of the indicator should ideally try to capture the overall importance of the energy source in economy and potential options to mitigate against a supply disruption. As there are several proxies to measure this, it was decided to use a simple share of energy in total primary or final consumption as a default.

To improve the application of the Stage IV component, two possible alternatives have been identified: (i) Minimum demand for energy where the energy consumed could be adjusted to reflect fuel substitution possibilities; and (ii) Adjusted share of primary fuel in final energy consumption where the efficiency of transformation of a given primary fuel into electricity or heat is taken into account. This is to reflect the fact that efficient plants are less vulnerable than inefficient plants.

A summary of the indicators used in the quantitative analysis is given in the table below.

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Table 0 - 3 Summary of energy security indicators under quantitative approach

Root cause Indicator name Description Overall Short-Run availability of primary fuel (gas)

• If DPSS ≤ 0 then SR Availability (days) = ⎟⎟⎠

⎞⎜⎜⎝

⎛−

fuelsall

fuelX

TPESTPES

1*365

• else, SR Availability (days) = ⎟⎟⎠

⎞⎜⎜⎝

⎛−⎟

⎠⎞

⎜⎝⎛

− fuelsall

fuelX

TPESTPES

LSSPRDPSSNetStorage 1*

Where

- DPSS (ktoe per day) = Daily Peak Supply Shortfall = Peak daily demand for fuel - Average daily (Production + Net Imports) for fuel

Peak demand will increase under an extreme weather event. - TPES (ktoe) = Total Primary Energy Supply - NetStorage = maximum available storage capacity (ktoe) - net of the largest supplier if considering the

impact of other extreme events on supply and if storage is the largest supplier. - LSSPR (ktoe) = supply from largest supplier, plant or route (excluding storage) if considering the impact

of other extreme events on supply (otherwise this is set = 0)

Extreme events (covers both extreme weather and other extreme events within same formulae)

Overall short-Run availability of primary fuel (oil)

• SR Availability (ktoe) =

( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛−−

fuelsall

fuelX

TPESTPES

requirederageofdaysBenchmarkLSSPRDPSS 1*cov*

Where


Peak demand will increase under an extreme weather event. - TPES (ktoe) = Total Primary Energy Supply - LSSPR (ktoe) = supply from largest supplier, plant or route if considering the impact of other extreme

events on supply (otherwise this is set = 0)

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Root cause Indicator name Description Further De-rated electricity peak capacity margin (adjusted for loss of plant and/or further de-rating for primary fuel shortage)

• DEPCM (%) = ⎟⎟⎠

⎞⎜⎜⎝

⎛−

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛ −−−∑

all

yelectricitiestechnoAll

FECFEC

DemandPeak

DemandPeakADLoPDcapacity1*log

Where:

- DCapacity (MW) = de-rated capacity of electricity plant - AD (MW) = Additional De-rating (due to primary fuel shortage) - PeakDemand (MW) = peak electricity demand (this will increase if considering the situation under an

extreme weather event) - FEC (ktoe) = final energy consumption - LoP (MW) = Loss of Plant capacity (MW) due to shut down of largest individual plant, loss of

transmission line connecting plant(s), etc – if considering the impact of other extreme events on supply (otherwise this is set = 0)

Capital Intensity

• Capital intensity (%)= ⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟

⎠

⎞

⎜⎜

⎝

⎛

∑∑

=

=

all

yelectricitt

i

t

i

FECFEC

tstotal

tscapital*

cos

cos

1

1

Where

- ∑=

t

i 1= cumulative sum of capital costs or total costs of electricity generation from Year 1 to t in €M.

- FEC (ktoe) = Final energy consumption Average Load Factor • Average annual load factor (%) = ⎟⎟

⎠

⎞⎜⎜⎝

⎛−⎟⎟

⎠

⎞⎜⎜⎝

⎛

all

yelectricit

FECFEC

generationpossibleMaximumgenerationyElectricit 1*

Where - FEC (ktoe) = Final energy consumption

Inadequate Market Structure – Insufficient investments in new capacity

Cumulative required new capacity • Required new capacity (MW or €M)= ( ) ⎟⎟

⎠

⎞⎜⎜⎝

⎛∑ =

all

yelectricitt

i FECFEC

capacitynew *1

Where

- ∑=

t

i 1= sum of new generation capacity (total cost in €M or nameplate capacity in MW) from Year 1 to

t. - FEC (ktoe) = final electricity consumption

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Root cause Indicator name Description De-rated electricity peak capacity margin

• DEPCM (%)( ) ( ) ( )

( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛−

−⎟⎟⎠

⎞⎜⎜⎝

⎛

=∑

all

yelectricitsechnologietAll

FECFEC

demandPeak

demandPeakcreditcapacitycapacitynameplate1*

*

Where - Name plate capacity (MW) = total nameplate capacity of each technology type - Capacity credit (%) = annual output as a % of nameplate capacity for each technology type given

planned outages, intermittent generation, etc. - PeakDemand (MW) = peak electricity demand - FEC (ktoe) = Final energy consumption

Inadequate market structure: Load balancing failure

Flexibility Margin • FM (%)

=

( )

)(

)()(*)(log

needyflexibilitMax

needyflexibilitMaxCapacitytyAvailabiliraterampMaxiestechnoleControllab

−⎟⎟⎠

⎞⎜⎜⎝

⎛∗∑

⎟⎟⎠

⎞⎜⎜⎝

⎛−

all

yelectricit

FECFEC

1*

Where - Max flexibility need (MW) = Max rate of intermittent output fall in 1 hour period + Max rate of possible

demand increase in 1 hour period. - Capacity (MW) = total nameplate capacity of each dispatchable plant type - Availability is the % of the capacity that is available for use at the time that availability is needed - Max ramp rate (%) = % of nameplate capacity from each dispatchable plant type available in 1 hour. - FEC (ktoe) = Final energy consumption


Resource Concentration Price Indicator (markets characterized by an effective price mechanism)

• RCPI = ∑⎥⎥⎦

⎤

⎢⎢⎣

⎡ −− TPES

EK

ESMC f

ffpolf

min*1*

Where - ESMCpol-f = Σi (Sif

2)= Energy Security Market Concentration measure for fuel f. Sif = the % share of each supplier i in the international market for fuel f defined by its net export potential (Sif varies from 0 to 100).

- Ef-min (ktoe) = minimum primary energy demand for fuel f. - TPES (ktoe) = Total primary energy supply - Kf (%) = fuel input flexibility parameter

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Root cause Indicator name Description Resource Concentration Physical Availability Indicator (markets with no or limited price mechanism)

• RCPAI = ⎟⎟⎠

⎞⎜⎜⎝

⎛ −−− TPES

GasESIC impreg

polgasmin*

Where - ESICgas-pol = Energy Security Import Concentration = Σi ((Gasimp-regulated-I * ri )/ GASimp-regulated)2 . Where

Gasimp-regulated-i is the supply of gas imports based on regulated contracts from country i and GASimp-

regulated is total regulated gas imports. - Where GASreg-imp-min is the country’s minimum gas imports met by purchases on regulated terms (in this

case minimum demand is estimated as annual net imports purchased under regulated contracts NOT covered by available storage capacity.

- TPES = Total primary energy supply - ri = political risk rating for country i.

N/A Alternative Stage IV

indicator - Adjusted Share of Primary Fuel in FEC (Applies to any non-electricity related Stage IV)

• Adjusted share of primary fuel in FEC =FECTotal

EffEffEHCDFEC

all

fuelfuelfuel ⎟

⎠⎞

⎜⎝⎛ + *

Where: - DFECfuel = Direct Final Energy Consumption of fuel - EHCfuel = Electricity and heat generated by fuel* - Total FEC = Total Final Energy Consumption - Effall = efficiency of all heat and power generation (including CHP and district heating) = total inputs to

thermal plant (including nuclear) / sum of total heat and electricity generation* - Efffuel = efficiency of heat and power generation from fuel (including CHP and district heating) = total fuel

input to thermal plant / sum of electricity and heat generation from fuel* * = net of energy branch consumption and transmission and distribution losses (these are apportioned across each fuel depending on its share in net generation).

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Quantitative analysis of the energy security impact of key new climate policies

The results were analysed under a baseline scenario and two alternative scenarios:

1. a climate package containing the impact of the 2020 GHG and RES targets

2. a CCS scenario out to 2030.

Both the climate change package and the CCS scenario are quite similar as the CCS scenario also contains the 2020 GHG and RES targets, but they differ more markedly in ~2030 when a sizeable amount of CCS is estimated.

The results need to be considered as illustrative to some extent, given the nature of some of the other input data and assumptions (including those made by the PRIMES team), which are currently fixed across the scenarios1.

An excel-based spreadsheet tool was developed as part of this project to analyse the indicators. The indicators have been constructed based on publicly available annual historic energy balance data from 1990 to 2006/7 (where available), with the first projection results starting in 2010, and the other input data/assumptions as described in section 6.2. Projections are then provided in 5-yearly intervals to 2030 from PRIMES. Intermediate years have been linearly interpolated. The model was structured to allow easy updating of the PRIMES projections as well as other input data. In particular, the user can select the combination of indicators (also by stages and fuel type), scenarios and MSs to be displayed; and quickly alter a small number of variable factors and see the resulting impacts.

In addition to the analysis of the overarching policy scenarios, three case-studies were undertaken to further test the use of the quantitative indicator approach and explore the impact of other policies on energy security.

The results for these case-studies are provided in Appendix D and include:

i ) Impact of renewable energy support policies in both the UK (Renewable Obligation) and Spain (Feed in tariffs) from 1990 to 2007

ii ) Impact of policies to promote CHP in the Netherlands from 1990 to 2000

iii ) Impact of the Large Combustion Plant Directive (LCPD) in the UK and Poland from 2010 to 2020

The case studies were designed so to give a representation of a variety of Member States, to carry out ex-post and ex-ante analysis, and to examine a non climate change policy (iii), which leads to impacts on the energy system.

The results are qualitatively summarised in the next table.

1 For more details on fixed assumptions see section 6.2

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Table 0 - 4 Summary of EU-27 vulnerability (direction of trend and ranking of each overall indicator) for each policy scenario

Baseline Climate Package CCS Root cause Indicator

2010 2020 2030 2010 2020 2030 2010 2020 2030 Overall short-Run Availability of primary fuel (Oil) (3) (3) (3) (2) (2) (2) (1) (1) (1)

Overall short-Run Availability of primary fuel (Gas) (3) (3) (3) (2) (2) (2) (1) (1) (1)

Extreme events

Further DEPCM (de-rated electricity peak capacity margin) (3) (3) (3) (2) (2) (2) (1) (1) (1)

Average Load Factor (1) (1) (1) (2) (2) (3) (3) (2) (2)

Cumulative required new capacity (MW) (3) (3) (1) (2) (2) (3) (1) (1) (2)

Cumulative required new capacity (€M) n/a


Capital Intensity n/a

DEPCM (De-rated electricity peak capacity margin) (3) (3) (3) (2) (2) (1) (1) (1) (2) Inadequate market

structure: Load balancing failure Flexibility Margin

(3) (1) (1) (2) (2) (2) (1) (3) (3)

RCPI (Resource Concentration Price Indicator) (3) (3) (3) (2) (2) (2) (1) (1) (1) Supply shortfall

associated with resource concentration RCPAI (Resource Concentration Physical Unavailability

Indicator) (3) (3) (3) (2) (2) (1) (1) (2) (2)

Key:

= Trend towards increasing vulnerability, Trend towards decreasing vulnerability, No significant change in vulnerability.

(3) = Highest/worst vulnerability of all scenarios

(1) = Lowest/best vulnerability of all scenarios

(2) = Vulnerability in between other two scenarios

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An alternative presentation has been given in a radar-diagram. The figures below show the results for the EU-27 2020 and 2030. Each of the calculated indicators is normalised individually on a scale of 0 to 1, such that they all move in the same direction, with values closer to zero indicating lower vulnerability.

The current normalisation intentionally does not specify the relative importance of each indicator. It should be noted that values are a reflection of the extremes seen within the current three scenarios. Incorporating a wider range of (plausible) worst / best case scenarios alongside the policy(ies) of interest would help in the analysis, as the vulnerability values would be better set within the upper and lower limits.

Illustration 0 - 4 Normalised indicator results for EU-27 in 2020

(lower values indicate decreasing vulnerability)

0.00.1

0.2

0.30.4

0.5

0.60.7

0.80.9

RCPAI (Resource Concentration PhysicalUnavailability Indicator)

RCPI (Resource Concentration Price Indicator)

(II) Average Load Factor

(II) Cumulative required new capacity (MW)

(LBF) DEPCM (De-rated electricity peak capacitymargin)(LBF) Flexibility Margin

(EE) Further DEPCM (de-rated electricity peakcapacity margin)

EE) Short-Run Availability of primary fuel (Gas)

(EE) Short-Run Availability of primary fuel (Oil)

Baseline Package CCS

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Illustration 0 - 5 Normalised indicator results for EU-27 in 2030

(lower values indicate decreasing vulnerability)

0.00.10.20.30.40.50.60.70.80.91.0










Conclusions and recommendations

Conclusions

The main objectives of this project were to explore the impact of climate change policies on energy security and to establish a methodology that would serve as a base ground for further analysis, and which is able to quantify which policy measures are effective under shifting conditions.

The result of our quantitative analysis shows that, in general, both the climate package and CCS policy lead to an overall improvement (i.e. decrease in vulnerability) in energy security at the EU-27 level across the indicators relative to the baseline, with a small number of exceptions. These are focused around the electricity sector (as shown by the average load factor, flexibility margin and cumulative required new capacity indicators) – hence there is an overall shift away from primary-fuel related vulnerabilities towards more electricity-system related vulnerabilities.

A key underlying driver in the improvements seen under the non-baseline scenario is related to energy efficiency (both end-use and transformation). This leads to a reduction in energy consumption (absolute in some cases) out to 2030 compared to the baseline scenario (even for gas). If this is not realised then the resulting energy security vulnerability is considerably worse.

Even though the climate policies improve the situation relative to the baseline the long-term trend in vulnerability is still worsening in a number of cases – primarily for the electricity-related vulnerability indicators.

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• Although a more limited growth in electricity demand in ~2015-2020 compared to the baseline helps to improve indicators such as the DEPCM, this then rises rapidly after this point and is combined with greater levels of intermittent renewables. Demand-side policy impetus therefore needs to be maintained and even strengthened beyond 2020.

• It was not possible to calculate the capital intensity or required new capacity (in €M terms) indicators due to data confidentiality issues. However, the increasing reliance on electricity in all scenarios indicates that it would be important to try to calculate them – to provide a more complete set of information on all vulnerabilities and root causes of energy insecurity.

Trends and relative levels of vulnerabilities under each scenario at the individual Member State level, or country group level (where their infrastructure is more integrated) can look very different to those at the EU-27 level for the different indicators.

Although there are a number of issues to take into account when interpreting the overall results and/or the single indicators, the model remains, in our opinion, a useful starting point for policy makers to better grasp energy security dynamics. The model is not intended to replace expert judgment but rather to complement and enhance it, by providing more objective information.

Recommendations

Although, the model was developed at the best of our possibilities within the given timeframe some areas could benefit from additional efforts. In particular we recommend that future work is done in:

1. Refining non-PRIMES data

2. Testing of additional components

3. Other secondary improvements:

− normalising the indicators by incorporating a more extreme range of high/low scenarios,

− carrying out additional sensitivity analysis

− developing benchmark values to help in the interpretation of the DEPCM-based indicators and the flexibility margin indicator.

− expanding the geographic scope of the indicators to cover countries with infrastructure that has some integration to existing EU MSs.

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Table of contents

1 Introduction ........................................................................................... 35

1.1 Background ....................................................................................... 35

1.2 Objectives ......................................................................................... 36

1.3 Approach........................................................................................... 37

1.4 Structure of this report ........................................................................ 38

1.5 Acknowledgements ............................................................................. 39

2 Concept of energy security ..................................................................... 40

2.1 Existing concepts of energy security in literature ..................................... 40

2.2 From root cause to energy security impact: a bottom-up approach ............ 42

2.3 Energy security implications of extreme events ....................................... 49

2.4 Energy security implications of inadequate market structure ..................... 51

2.5 Energy security implications of resource concentration ............................. 54

2.6 Supply chain assessment ..................................................................... 56

3 Linkages between EU climate change policy and energy security ........... 57

3.1 Introduction....................................................................................... 57

3.2 Generic options for climate change mitigation ......................................... 57

3.3 Key effects of climate change mitigation on energy systems ..................... 62

3.4 Effects of climate change mitigation on energy security ............................ 66

3.5 Summary .......................................................................................... 81

4 Review of existing energy security indicators......................................... 88

4.1 Introduction....................................................................................... 88

4.2 Summary .......................................................................................... 91

5 Development of specific quantitative approach ...................................... 99

5.1 Introduction....................................................................................... 99

5.2 Principles for indicator design ..............................................................100

5.3 Overarching issues in application of the indicators ..................................105

5.4 Extreme Events .................................................................................111

5.5 Inadequate Market Structure – Insufficient investments in new capacity ....125

26


5.6 Inadequate market structure: Load balancing failure...............................134

5.7 Supply shortfall associated with resource concentration...........................139

5.8 Summary .........................................................................................153

6 Quantitative analysis of the energy security impact of key new climate policies 158

6.1 Introduction......................................................................................158

6.2 Other input data ................................................................................159

6.3 Development of spreadsheet tool .........................................................166

6.4 Results – Extreme events....................................................................169

6.5 Results - Insufficient investments in new capacity ..................................186

6.6 Results - Load balancing failure ...........................................................192

6.7 Results - Supply shortfall associated with resource concentration..............200

6.8 Summary .........................................................................................213

7 Case study analysis of other policies .................................................... 221

8 Conclusions and recommendations....................................................... 222

8.1 Introduction......................................................................................222

8.2 Implications for current EU climate policy on future energy security ..........223

8.3 Recommendations for policy makers in using the quantitative approach.....224

8.4 Areas for further work ........................................................................230

References ........................................................................................... 233

Appendix A Supply chain assessment tables.......................................... 237

Appendix B Review of existing indicators of energy security................. 251

B 1 Vulnerability indicators – focusing on specific energy security issues .........251

B 1.1 Infrastructure capacity and reserve indicators (Stage III) ........................251

B.1.1.1 Storage capacity and critical stocks of fuels ...........................................251

B.1.1.2 Load duration of back-up fuel supplies ..................................................252

B.1.1.3 Pipeline capacity and utilisation............................................................254

B.1.1.4 Refining capacity and utilisation ...........................................................254

B 1.2 Measures of the importance of energy in the economy (Stage IV).............255

B 1.3 Dependence on non-domestic production ..............................................257

27


B.1.3.1 Net Import Dependence......................................................................258

B.1.3.2 Net energy import bill.........................................................................260

B.1.3.3 Domestic production to consumption ....................................................261

B 1.4 Indicators of investment in adequate supply ..........................................262

B.1.4.1 General business environment .............................................................262

B.1.4.2 Patents in energy technology sector .....................................................263

B.1.4.3 Ratio of investments to turnover ..........................................................264

B.1.4.4 Market price signals ...........................................................................265

B 1.5 Measures of diversity .........................................................................267

B.1.5.1 Measuring diversity in a context of ignorance.........................................267

B.1.5.2 Probabilistic measures of diversity – Mean Variance Portfolio theory..........272

B 1.6 Other vulnerability indicators...............................................................274

B.1.6.1 Market liquidity .................................................................................274

B.1.6.2 Political stability ................................................................................276

B.1.6.3 Resource/reserve to production ratios (RPRs) ........................................277

B.1.6.4 Non-carbon share and CO2 content of energy ........................................278

B.1.6.5 Crisis Capability index ........................................................................279

B 2 Vulnerability indicators - overall system and hybrid approaches................280

B 2.1 Adequacy of energy supply to demand..................................................280

B.2.1.1 Peak capacity margin .........................................................................280

B.2.1.2 Peak de-rated capacity margin.............................................................281

B.2.1.3 Energy margin ..................................................................................283

B 2.2 Net import dependence and diversity in a market ...................................284

B 2.3 Measuring diversity in both supply to a market and within the market .......286

B 2.4 Long-term energy security indicator .....................................................287

B 2.5 IEA Energy Security Index ..................................................................290

B.2.5.1 Original 2004 study............................................................................290

B.2.5.2 Development of indicators under the 2007 study....................................291

B 2.6 Supply / Demand index ......................................................................294

B 3 Outcome-based indicators...................................................................297

B 3.1 Expected energy unserved ..................................................................297

B 3.2 A Security of supply function for the MERGE model .................................299

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B 3.3 Cost failure of the electricity system .....................................................300

Appendix C Overview of Impact Assessments for new climate change policies ........................................................................................... 302

Appendix D Case-studies ....................................................................... 309

D 1 Renewable electricity support schemes (UK and Spain) ...........................309

D 1.1 Background ......................................................................................309

D 1.2 Counterfactual (without policy) scenario................................................310

D 1.3 Results ...........................................................................................311

D 1.4 Summary .........................................................................................325

D 2 Expansion of Combined Heat and Power (CHP) (Netherlands)...................326

D 2.1 Background ......................................................................................326

D 2.2 Counterfactual (without policy) scenario................................................327

D 2.3 Results 329

D 2.4 Summary .........................................................................................342

D 3 Large Combustion Plant Directive (LCPD) implementation (UK and Poland) 343

D 3.1 Background ......................................................................................343

D 3.2 With and without policy scenarios.........................................................343

D 3.3 Results 344

D.3.3.1 Extreme weather ...............................................................................345

D.3.3.2 Other extreme events ........................................................................346

D.3.3.3 Insufficient investment .......................................................................347

D.3.3.4 Load balancing failure.........................................................................348

D.3.3.5 Resource concentration (effective price mechanism) ...............................349

D.3.3.6 Resource concentration (no effective price mechanism)...........................350

D 3.4 Summary .........................................................................................350

29


List of Illustrations

Illustration 2 - 1 Generic causal mechanisms of energy insecurity........................ 46 Illustration 3 - 2 Simplified example of short-term impact of energy efficiency on

energy security in the electricity sector.................................... 82 Illustration 4 - 3 Indicator type and link to causal mechanisms of energy security.. 89 Illustration 5 - 4 Basic approach to assessing impact of climate policy on energy

security............................................................................. 100 Illustration 5 - 5 Example of normalised spider diagram for 5 indicators ............. 103 Illustration 5 - 6 Overview of gas infrastructure in Europe (IEA, 2007) ............... 108 Illustration 6 - 7 SRA (primary fuels) indicator – Oil – Stage II - Extreme winter . 169 Illustration 6 - 8 SRA (primary fuels) indicator – Oil – Stage III - Extreme winter 170 Illustration 6 - 9 SRA (primary fuels) indicator – Oil – Stage III – Typical winter and

loss of largest supply route .................................................. 171 Illustration 6 - 10 SRA (primary fuels) indicator – Oil – Stage IV ....................... 172 Illustration 6 - 11 SRA (primary fuels) indicator – Oil – overall indicator – Extreme

winter ............................................................................... 173 Illustration 6 - 12 SRA (primary fuels) indicator – Gas – Stage II – Extreme winter....

....................................................................................... 174 Illustration 6 - 13 SRA (primary fuels) indicator – Gas – Stage III – Extreme winter...

....................................................................................... 175 Illustration 6 - 14 SRA (primary fuels) indicator – Gas – Stage III – Typical winter –

loss of largest supply route .................................................. 176 Illustration 6 - 15 SRA (primary fuels) indicator – Gas – Stage III – Extreme winter –

various country groupings.................................................... 177 Illustration 6 - 16 SRA (primary fuels) indicator – Gas – Stage IV...................... 178 Illustration 6 - 17 SRA (primary fuels) indicator – Gas – Alternative Stage IV...... 179 Illustration 6 - 18 SRA (primary fuels) indicator – Gas – overall indicator – Extreme

winter ............................................................................... 180 Illustration 6 - 19 De-rated Electricity Peak Capacity Margin (DEPCM) indicator –

Stage II – Extreme winter.................................................... 181 Illustration 6 - 20 De-rated Electricity Peak Capacity Margin (DEPCM) indicator –

Stage II – Extreme winter and loss of largest single plant......... 183 Illustration 6 - 21 De-rated Electricity Peak Capacity Margin (DEPCM) indicator –

Stage IV............................................................................ 185 Illustration 6 - 22 (Further) De-rated Electricity Peak Capacity Margin (DEPCM)

indicator – Overall indicator ................................................. 186 Illustration 6 - 23 Insufficient investment in new capacity - Average load factor –

Stage I ............................................................................. 187 Illustration 6 - 24 Average load factor – Stage I – Country groupings................. 188 Illustration 6 - 25 Insufficient investment in new capacity - Average load factor –

Overall indicator ................................................................. 189 Illustration 6 - 26 Insufficient investment in new capacity – required new capacity

(MW) – Stage I .................................................................. 190

30


Illustration 6 - 27 Insufficient investment in new capacity – required new capacity (MW) – Stage IV ................................................................ 191

Illustration 6 - 28 Insufficient investment in new capacity – required new capacity (MW) – Overall indicator...................................................... 192

Illustration 6 - 29 De-rated Electricity Peak Capacity Margin (DEPCM) – Stage I .. 193 Illustration 6 - 30 De-rated Electricity Peak Capacity Margin (DEPCM) – Stage I –

various country groupings.................................................... 194 Illustration 6 - 31 De-rated Electricity Peak Capacity Margin (DEPCM) – Stage I – ex-

UCTE EU Members .............................................................. 194 Illustration 6 - 32 De-rated Electricity Peak Capacity Margin (DEPCM) – Overall

indicator............................................................................ 195 Illustration 6 - 33 Flexibility margin – Stage I – typical loss of wind generation and

spinning reserve ramp rates................................................. 196 Illustration 6 - 34 Flexibility margin – Stage I – maximum likely loss of wind

generation and cold start ramp rates..................................... 197 Illustration 6 - 35 Flexibility margin – Stage I – typical loss of wind generation and

spinning reserve ramp rates – various country groups ............. 198 Illustration 6 - 36 Flexibility margin – Stage I – typical loss of wind generation and

spinning reserve ramp rates – various groups - ex-UCTE EU Members ........................................................................... 199

Illustration 6 - 37 Flexibility margin – Overall indicator – typical loss of wind generation and spinning reserve ramp rates ........................... 200

Illustration 6 - 38 Resource Concentration Price Indicator – ESMC – Stage I – OPEC as a single supplier, no political risk ratings............................ 202

Illustration 6 - 39 Resource Concentration Price Indicator – ESMC – Stage I – OPEC as a single supplier, political risk ratings included.................... 203

Illustration 6 - 40 Resource Concentration Price Indicator – Stage IV - Coal ........ 204 Illustration 6 - 41 Resource Concentration Price Indicator – Alternative Stage IV -

Coal.................................................................................. 204 Illustration 6 - 42 Resource Concentration Price Indicator – Stage IV - Oil .......... 205 Illustration 6 - 43 Resource Concentration Price Indicator – Alternative Stage IV - Oil

....................................................................................... 205 Illustration 6 - 44 Resource Concentration Price Indicator – Stage IV – gas, all

consumption ...................................................................... 206 Illustration 6 - 45 Resource Concentration Price Indicator – Stage IV – gas,

purchased on spot or spot derived markets ............................ 206 Illustration 6 - 46 Resource Concentration Price Indicator – Alternative Stage IV –

gas, all consumption ........................................................... 207 Illustration 6 - 47 Resource Concentration Price Indicator – Overall indicator – OPEC

as a single supplier, no political risk ratings – all primary fuels .. 208 Illustration 6 - 48 Resource Concentration Physical Availability Indicator – Stage I –

ESIC – 2008 values ............................................................ 209 Illustration 6 - 49 Resource Concentration Physical Availability Indicator – Stage III –

Gas Storage Capacity.......................................................... 210

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Illustration 6 - 50 Resource Concentration Physical Availability Indicator – Stage IV.......................................................................................... 211

Illustration 6 - 51 Resource Concentration Physical Availability Indicator – Stage IV – various country groupings.................................................... 212

Illustration 6 - 52 Resource Concentration Physical Availability Indicator – Overall indicator – no political risk ratings......................................... 213

Illustration 6 - 53 Normalised indicator results for EU-27 in 2010 (lower value indicates decreasing vulnerability) ........................................ 218



Illustration B - 56 Estimate of load duration of UK CCGT back-up fuel supplies assuming full output ........................................................... 253

Illustration B - 57 Relationship between concentration risk and liquidity ............. 275 Illustration B - 58 Modelled capacity credit function for diverse UK onshore wind

resource with 27% capacity factor ........................................ 282 Illustration B - 59 Example of UK gas supply versus demand under a given scenario ..

....................................................................................... 283 Illustration B - 60 Overview of IEA approach to quantifying the energy security

implications of resource concentration ................................... 293 Illustration B - 61 SDI model structure .......................................................... 295 Illustration D - 62 SRA (primary fuels) indicator – Gas – Stage II – Typical winter 312 Illustration D - 63 SRA (primary fuels) indicator – Gas – Stage II – Extreme winter ...

....................................................................................... 312 Illustration D - 64 SRA (primary fuels) indicator – Gas – Stage III – Typical winter ....

....................................................................................... 313 Illustration D - 65 SRA (primary fuels) indicator – Gas – Stage IV ..................... 314 Illustration D - 66 SRA – De-rated Electricity Peak Capacity Margin (DEPCM) indicator

– Stage II – Typical winter and loss of largest plant................. 315 Illustration D - 67 SRA – De-rated Electricity Peak Capacity Margin (DEPCM) indicator

– Stage II – Extreme winter and loss of largest plant ............... 316 Illustration D - 68 SRA – De-rated Electricity Peak Capacity Margin (DEPCM) indicator

– Stage III– Extreme winter and loss of largest plant............... 317 Illustration D - 69 Average Load Factor – Stage I – all electricity plants.............. 318 Illustration D - 70 Average Load Factor – Stage I – gas plant only ..................... 319 Illustration D - 71 Cumulative required new capacity (MW) – Stage I ................. 320 Illustration D - 72 De-rated Electricity Peak Capacity Margin (DEPCM) – Stage I .. 321 Illustration D - 73 Flexibility margin – Stage I – maximum likely loss of wind

generation and spinning reserve ramp rates ........................... 322 Illustration D - 74 Flexibility margin – Overall indicator – maximum likely loss of wind

generation and spinning reserve ramp rates ........................... 323 Illustration D - 75 Resource Concentration Price Indicator – Overall indicator - Gas –

no political risk ratings ........................................................ 324

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Illustration D - 76 Resource Concentration Physical Availability Indicator – Overall indicator – no political risk ratings......................................... 325

Illustration D - 78 trend in the development of small scale (<2MWe) CHP capacity 327 Illustration D - 79 Coal and natural gas consumption trend in the actual (with CHP)

and counterfactual scenarios. ............................................... 329 Illustration D - 80 SRA (primary fuels) indicator – Gas – Stage II – Typical winter 330 Illustration D - 81 SRA (primary fuels) indicator – Gas – Stage II – Extreme winter ...

.............................................................................................

....................................................................................... 331 Illustration D - 82 SRA (primary fuels) indicator – Gas – Stage II – Extreme winter

without main gas supplier .................................................... 331 Illustration D - 83 SRA (primary fuels) indicator – Gas – Stage III – Typical winter ....

....................................................................................... 332 Illustration D - 84 SRA (primary fuels) indicator – Gas – Stage IV ..................... 333 Illustration D - 85 SRA (primary fuels) indicator – Gas – All stages .................... 333 Illustration D - 86 SRA – De-rated Electricity Peak Capacity Margin (DEPCM) indicator

– Stage II – Typical winter and loss of largest plant................. 334 Illustration D - 87 SRA – De-rated Electricity Peak Capacity Margin (DEPCM) indicator

– Stage II – Extreme winter and loss of largest plant ............... 335 Illustration D - 88 SRA – De-rated Electricity Peak Capacity Margin (DEPCM) indicator

– All Stages ....................................................................... 336 Illustration D - 89 Average Load Factor – Stage I – gas plant only ..................... 337 Illustration D - 11 De-rated Electricity Peak Capacity Margin (DEPCM) – Stage I .. 338 Illustration D - 91 Flexibility margin – Stage I................................................. 338 Illustration D - 92 Flexibility margin – Overall indicator – maximum likely loss of wind

generation and spinning reserve ramp rates ........................... 339 Illustration D - 93 Resource Concentration Price Indicator – Overall indicator - Coal –

no political risk ratings ........................................................ 340 Illustration D - 94 Resource Concentration Price Indicator – Overall indicator - Gas –

no political risk ratings ........................................................ 341 Illustration D - 95 Resource Concentration Price Indicator – Overall indicator – Primary

fuels – no political risk ratings .............................................. 341 Table D - 26 Key assumed differences in electricity capacity under LCPD vs no-

LCPD scenarios (GW) .......................................................... 344 Illustration D - 77 SR Availability - Stage III - Primary gas and oil ..................... 345 Illustration D - 97 DEPCM – Extreme winter peak - Electricity ........................... 346 Illustration D - 98 DEPCM – typical winter peak – but with Loss of Largest Electricity

Plant................................................................................. 346 Illustration D - 99 Insufficient investment metrics (UK).................................... 347 Illustration D - 100 Derated Capacity Margin .................................................. 348 Illustration D - 101 Flexibility Margin............................................................. 349 Illustration D-102 Resource Concentration Price Indicator (Effective Price Mechanism)

....................................................................................... 349 Illustration D - 103 Resource Concentration Physical Availability Indicator (No

Effective Price Mechanism)................................................... 350

33


List of Tables

Table 2 - 1 Example of APERC (2007) - policy orientated classification of energy insecurity ............................................................. 41

Table 2 - 2 Classification of energy insecurity root causes ..................... 44 Table 3 - 3 Summary of policies considered......................................... 59 Table 3 - 4 Summary of generic climate mitigation options that apply to

policies under consideration ............................................... 61 Table 3 - 5 Effect of generic mitigation options on the energy system ..... 62 Table 3 - 6 Effects of climate mitigation options on energy security impacts

of extreme events ............................................................ 67 Table 3 - 7 Effects of climate mitigation options on energy security impacts

of inadequate market structures - Insufficient investments in new capacity ................................................................... 71

Table 3 - 8 Effects of climate mitigation options on energy security impacts of inadequate market structures - Load balancing failure in electricity markets............................................................ 76

Table 3 - 9 Effects of climate mitigation options on energy security impacts from supply shortfall associated with resource concentration .. 78

Table 3 - 10 Summary of interactions between generic EU climate mitigation options and energy security ............................................... 87

Table 4 - 11 Summary of vulnerability indicators – focusing on specific energy security issues....................................................... 94

Table 4 - 12 Summary of vulnerability indicators - overall system and hybrid approaches ..................................................................... 97

Table 4 - 13 Summary of outcome based indicators ............................... 98 Table 5 - 14 High-level integration of gas, electricity and refinery

infrastructure in the EU ................................................... 106 Table 5 - 15 Known recoverable reserves of Uranium in 2007................ 140 Table 5 - 16 Summary of energy security indicators under quantitative

approach ...................................................................... 154 Table 6 - 17 Projections of installed nameplate capacity and total de-rated

capacity under each scenario – EU-27 ............................... 182 Table 6 - 18 Summary of EU-27 vulnerability (direction of trend and ranking

of each overall indicator) for each policy scenario................ 214 Table A - 19 Supply chain assessment of energy security issues for oil..... 238 Table A - 20 Supply chain assessment of energy security issues for natural

gas .............................................................................. 240 Table A - 21 Supply chain assessment of energy security issues for coal . 244 Table A - 22 Supply chain assessment of energy security issues for uranium .

.................................................................................. 246 Table A - 23 Supply chain assessment of energy security issues for biofuels

(including biomass) ........................................................ 247

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Table A - 24 Supply chain assessment of energy security impacts for electricity ...................................................................... 249

Table C - 25 Approach to analysis of impact of climate policy on energy system in EC Impact Assessments .................................... 303

35


1 Introduction

This document is the final report produced under European Commission DG Environment contract 07.0307/2008/515198/SER/C.5 - Analysis of impacts of climate change policies on energy security.

1.1 Background

EU energy policy is driven by three interrelated objectives2:

• Achieving a sustainable energy system – with a focus on minimising anthropogenic climate change (around 80% of all greenhouse gas emissions are energy-related).

• Improving energy security

• Maintaining the international competitiveness of the EU, with respect to both energy prices and investment in energy technology and infrastructure.

The use of energy is a prerequisite for virtually all economic activity, and it is crucial to be able to access sufficient amounts of energy at acceptable3 cost (both from an economic and environmental perspective), which gives rise to the notion of ‘energy security’.

Historically, physical supply interruptions have been a core driver of interest in energy security, leading to increased development of storage facilities and coordinated use of reserves during supply disruptions (e.g. via the creation of the International Energy Agency). Interest has increased again more recently, due in part to the sizeable spike in fuel prices in 2008.

Moreover, conventional energy resources are becoming increasingly concentrated in a few countries, some of which are subject to concerns over their political stability. Since there is great spatial discrepancy between those regions with high production of fossil energy carriers and those consuming them, most consuming countries are projected to become increasingly dependent on imported energy to meet their demands. Trade in energy in and of itself is not issue, however, this can expose a country to external risks over which they have less control in comparison to those internal risks within the country.

The current EU energy system has its own inherent energy security risks. However, the introduction of a number of existing and proposed climate change-related

2 Communication from the commission to the European council and the European parliament; An energy

policy for Europe. SEC (2007) 12 3 Or cost-reflective prices, including environmental externalities

36


policies (such as those of the EU’s Climate and Energy Package4 adopted in December 2008) will change the structure of the energy system significantly in the future (e.g. via greater use of renewables to meet emissions targets), thus altering these associated risks.

It is also important to note that the overall impact on energy security depends not only on the direct impact of these policies on the energy system but other indirect factors, which may mitigate or accentuate these risks. For example, a higher dependence on imported natural gas may matter less under a more integrated global market (via greater use of LNG – liquefied natural gas) compared to the situation where pipeline imports from potentially unstable regions continue to dominate.

This study is therefore focused on the interaction between the first two elements of EU energy policy – to develop a methodology to identify and assess (quantitatively where possible) the impact of (and interactions between) such climate policies on energy security. This will help guide policy making by identifying areas, and the extent to which, climate policy can reinforce energy security objectives.

1.2 Objectives

The specific objectives of this study outlined in the original terms of references are as follows:

• To explore the impact of environmental policy initiatives and regulations recently adopted and currently proposed by the European Commission on the energy security in the European Union and Europe (mainly - but not limited to - the Climate and Energy Package).

• To establish a methodology that would serve as a base ground for further analysis of impacts of climate change policies on energy security issues and help to guide policy-making towards policies that achieve both energy security and climate change mitigation objectives as efficiently as possible.

• To propose an analytical approach to quantify which environmental measures are effective under shifting conditions in the European and world energy situation.

4 http://ec.europa.eu/environment/climat/climate_action.htm

37


It also states that the impact of climate change policies on energy security issues should be assessed with respect to opportunities, challenges and risks in the following areas:

• Energy supply disruption for a range of fuels5

- imports into the European Union

- domestic supply/production

• Energy distribution problems (electricity, pipeline, alternative fuels)

These areas will be analysed in 2020 and 2030 (at both the EU and global level where appropriate) with respect to political, technical, economic and extreme weather-related issues.

1.3 Approach

The concept of ‘energy security’ is multi-faceted, covering a range of disparate factors which can mean different things to different stakeholders, and has also evolved over time – all of which makes formal quantification difficult. Many assessments of ‘energy security’ to-date have not been undertaken in a systematic manner, have only focused on limited aspects of the energy system and/or have relied extensively on expert judgment.

Without a consistent and objective framework for analysis it becomes difficult, from an ‘energy security’ perspective, to say whether we are better-off, worse-off, or the same following the impact of climate change policies on the energy system. This can reduce the usefulness of these assessments, resulting in an approach that tends to be guided by policy makers as opposed to being an objective guide for policy making.

The quantitative approach in this study focuses on the use of indicators to assess the impact of climate policy on energy security, and is driven by two main concerns:

• The need for pragmatism in quantification. Data availability is a key limiting factor in what can be quantified. However, not all energy security issues are of equal importance and attention is focused on the most relevant energy security issues as defined by our analytical framework. Where quantification is not feasible the reasons for this are discussed and qualitative assessment is used instead.

• The need for strong theoretical foundations and an emphasis on transparency and objectivity. The indicators should be good proxies for the area of energy security that they are assessing and be consistent in their overall approach to the assessment. Many studies have used extensive lists of indicators without adequately explaining the specific aspects of energy

5 Primary fossil fuels (oil, gas and coal), uranium, refined products (mainly transport fuels) and biofuels

(both refined and feedstocks)

38


security they are trying to measure or how the indicators link together. In addition, there is an inherent trade-off in the construction of more aggregated indicators, which aim to be more comprehensive in their assessment, but which can introduce subjectivity in the weighting of the different components against each other, and reduce the meaningfulness and transparency of the final results.

In addition, the approach outlined in this study aims to be flexible and easily updatable given that:

• The current modelling assessments of the impact of climate policies on the energy system6 (which serve as an input to many of the indicators of energy security) will undoubtedly need to be updated in the future.

• The approach can be replicated to quantify the impacts of a wider range of environmental policies that may also have significant effects on the energy system (e.g. the Large Combustion Plant Directive on power generation).

This project is comprised of the following tasks:

i ) Review of the concept of energy security and development of an overarching framework to be used throughout this study.

ii ) Qualitative assessment of the interactions between climate policy and energy security

iii ) Review of existing indicators used to assess energy security

iv ) Development of a specific quantitative approach to assessing the impacts of climate change policy on energy security.

v ) Development of a spreadsheet tool based on these indicators to analyse the impact of a number of the recently proposed climate policies on future energy security (using existing energy system modelling work undertaken to support development of these policies).

vi ) Case-study analysis of other policies in various Member States to test the robustness of the framework.

1.4 Structure of this report

The following sections of this report are structured as follows:

• Section 2: describes the concept of energy security and the framework used throughout this study

• Section 3: qualitatively assesses the key linkages between the key climate change policies and energy security

6 E.g. from the PRIMES model used to support the Impact Assessments of the Climate Package

http://ec.europa.eu/energy/climate_actions/doc/2008_res_ia_en.pdf

39


• Section 4: reviews the use of existing energy security indicators in the literature.

• Section 5: describes the quantitative approach developed in this study, drawing on the analysis in the previous sections.

• Section 6: outlines the development of a spreadsheet tool and initial analysis of the impact of the new climate package policies on energy security using this quantitative approach.

• Section 7: describes a number of case-studies implementing the quantitative approach.

• Section 8: concludes, outlines recommendations for policy makers and potential areas of further work.

1.5 Acknowledgements

The project team would like to thank the following individuals for their input and feedback throughout the project (including at the meeting held at DG Environment on 12 May 2009).

• DG ENV: Marek Sturc (responsible project officer), Mihai Tomescu, Piotr Jaroslaw Tulej, Stefaan Vergote, Maria Rosa Virdis.

• JRC-PETTEN: Henryk Faas, Flavia Gangale, Evangelos Tzimas.

• DG ECFIN: Joan Canton, Asa Johannesson Linden.

• DG TREN: Giordano Rigon, Adam Szolyak, Helen Donoghue.

• External: Richard Baron (IEA), Arno Behrens (CEPS), Jaap Jansen (ECN).

40


2 Concept of energy security

2.1 Existing concepts of energy security in literature

Definitions of energy security in the literature can be grouped into two broad categories: the first focuses on economic principles and the second is more policy oriented.

• From an economic perspective, energy insecurity is the loss of welfare resulting from a change in the price or physical availability of energy (e.g. Bohi and Toman, 1996). Energy security may then be defined as avoidance of the loss of welfare resulting from a change in price7 or physical availability.

• Policy oriented definitions typically highlight basic requirements of a secure energy system. Most often these stress the need for accessibly to, and affordability of energy. Some go further, such as the work of the Asia Pacific Energy Research Center (APERC, 2007) and include other dimensions, in this case availability and acceptability (Table 2 - 1).

7 Volatile, rapid and significant changes in price as opposed to price movements more generally

41


Table 2 - 1 Example of APERC (2007) - policy orientated classification of energy insecurity

Dimension Description Relevant factors

Availability Whether the energy available is

sufficient to meet demand

- The overall level of demand for energy

- The physical existence of resources

- The ability (economical and technical)

to produce these resources

Accessibility (And geopolitical elements)

even if resources are available

they may not be accessible

- The level of import dependence on

foreign supply

- The diversity of supply

- Market concentration of suppliers

- Political stability

- Types of energy transport

infrastructure.

Affordability Despite energy being available

and accessible its affordability

may have significant

economic/social impacts

- The impact of high/volatile prices on

the economy and those on lower

incomes.

- The impact of energy prices on

(timely) investments in production,

refining and infrastructure

Acceptability Finally, even if the above three

dimensions are favourable,

environmental or societal

elements may also impact on

energy security

- GHG emission constraints that limit

the choice of energy sources

- Constraints on local air pollution

- Constraints on the development and

use of certain technologies, nuclear or

carbon capture and storage

While perfectly sound in their own rights, the suitability of these definitions depends on their use. The objective of this project is to define quantitative tools to help policy makers and analysts assess the effects of climate change mitigation policies on energy security. In this light, both definitions have important limitations.

In the first case, while comprehensive from a purely economic perspective, such definitions raise important practical issues. Most importantly, measuring welfare effects is far from simple. Energy production and use is ubiquitous and many of the underlying processes are complex. A change in the price or availability of energy may also have important macroeconomic implications. This renders economic assessments of energy insecurity inherently uncertain (this is notably highlighted by Huntington (2005) within the scope of oil price fluctuations).The result is that efforts to measure overall welfare effects associated with energy insecurity typically rely on highly stylized models (e.g. IMF (2000), Markandya and Hunt (2004), or Leiby (2007)). Whilst these may be instructive from a broad planning perspective or for academic research, they are of only limited guidance to the design and assessment of policy.

42


In the case of more policy-oriented definitions, the primary concern is that the dimensions identified are typically ill defined. In particular, it is left to expert elicitation as to what constitutes an appropriate level for each (e.g. when is energy deemed sufficiently ‘accessible’ or ‘affordable’?). This may be suitable for qualitative descriptions of energy security but is problematic when working on a quantitative assessment.

In defining a suitable framework for the purpose of this study, additional information is therefore needed. The dilemma is in finding a suitable middle ground between uncertain aggregate welfare estimates on the one hand and unstructured expert judgment on the other.

2.2 From root cause to energy security impact: a bottom-up approach

2.2.1 Typology of energy security root causes

It is useful to depart from broad definitions of energy security and look at specific cases based on actual experience (IEA, 2004; IEA, 2007; Lefevre, 2009). All countries around the world have had experience with events which have led to energy insecurity and these have been the focus of much analysis within and outside governments. This experience can be used to establish a simple classification of root causes of energy insecurity. Specifically, three broad, independent categories of energy security root causes can be defined:

• Extreme events. These are events that put exceptional strain on energy systems by creating an often sudden imbalance between supply and demand. They are so rare and so severe that it is difficult for private agents to account for them appropriately and they may therefore lead to energy insecurity.

• Inadequate market structures. Energy markets are complex. Infrastructures often span several countries and therefore encapsulate different regulatory systems. They are also characterized by large and long-lived capital investment cycles. Many markets have also only recently shifted to deregulated structures. This transition is tedious and involves an important learning process for all market participants. Energy market structures are therefore continually evolving and may at times themselves be the cause of energy insecurity.

• Resource concentration. Historically this has primarily concerned fossil fuels. The Middle East accounts for around 62% of global proven oil reserves. In contrast OECD countries hold only 7% yet account for 58% of world consumption. Similarly, 56% of global proven reserves of gas are found in three countries: the Russian Federation (26%), Iran (16%) and Qatar (14%). OECD countries hold only 9% of this total while accounting for 50% of global consumption (BP, 2007). The resulting concentration of fossil

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fuel supplies combined with the sensitive political climate in many exporting countries has fuelled much political concern in OECD countries and in other resource poor countries. Important supply shortfalls have occurred in the past and most were politically charged. Resource concentration may also be a concern for other fuels. Uranium, the primary feedstock for nuclear energy, is also characterised by a certain degree of resource concentration. Similarly, if demand for biofuels continues on a rapid demand growth trajectory, developing countries may become important suppliers to OECD countries. Depending on how the market develops this may cause new energy security concerns linked to resource concentration.

These three categories of supply-side energy insecurity can be broken down to provide a typology of the main root causes of energy insecurity, as shown in the table below.

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Table 2 - 2 Classification of energy insecurity root causes

Category Type Brief description Extreme

weather

Extreme weather events can temporarily disable energy

infrastructures and the supply of energy. A recent example is

the impact of Hurricane Katrina, which hit the Gulf of Mexico in

2005, disabling a significant portion of the US oil and gas

production and processing capacity. There are however many

other possible extreme weather events with potential energy

security consequences including those which impact on the

demand side (e.g. exceptionally cold or hot days) or on the

supply side (e.g. reduced cooling water availability).

Large scale

accidents

Much like extreme weather events, accidents can lead to

unplanned outages of key energy infrastructures.

Acts of terrorism Acts of terrorism against key infrastructures (e.g. refineries or

pipelines) or bottlenecks along specific energy trade routes

(e.g. the straight of Hormuz) can cause disruptions to energy

systems.

Extreme

events

Strikes Due to the strategic nature of energy, strikes or other forms of

social unrest may specifically target the operation of key

energy system components.

Insufficient

investments in

new capacity

Market structures which fail to generate timely investments in

key energy system infrastructures can contribute to making the

system more vulnerable and ultimately generate energy

insecurity.

Inadequate

market

structure

Load balancing

failure in

electricity

markets

Because electricity is not storable in any meaningful volumes

system operators must effectively balance supply and demand

in real time to ensure system reliability. The task is challenging

and requires that certain technical characteristics be met. When

this is not the case systems sometime fail or do not operate in

an efficient manner causing a loss of welfare for users.

Supply shortfall associated with

resource concentration

Due to the concentration of resources in certain regions of the

world, exploration and production as well as transport of fuels

are also concentrated. This generates a certain degree of

market power8 which can adversely affect energy systems.

8 Defined as the “ability to set prices above competitive levels and for this to be profitable” (IEA, 2007)

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It is important to stress that the typology presented here may not be an exhaustive list of all possible causes of energy insecurity but rather one, which focuses on the main types of concern based on experience. There are a range of other potential areas of energy insecurity which have not translated into notable price or physical availability concerns to-date and which are therefore not included in the quantitative analysis undertaken in this report.

One example is the possible negative effects of market power (i.e. monopolistic, oligopolistic structures) on energy prices, which would constitute another type under the root cause of inadequate market structure. This may be of concern within EU energy markets, particularly in the scope of the transition from regulated monopoly to competition or even within a nominally competitive market with extensive consolidation amongst participants. Similarly, market power may also be of concern at the international level. A relatively small number of large multinational coal mining and marketing companies notably dominate the international coal market. Such concerns are potentially important, and should be monitored. However, they have so far not translated into clear energy security impacts and as such are not considered in this study.

Another example is depletion of fossil reserves. Whilst this is a key driver of long-term concerns, it is actually the resulting resource concentration rather than the level of remaining reserves per se that has led to price or physical availability energy security concerns. Without concerns about concentration, depletion would be gradual and dynamic, with price increases spurring greater exploration as well as R&D in alternative energy sources, leading to an extended time period for depletion over which an economy could gradually respond. In addition, future estimates of resource concentration will implicitly need to account for the long-term availability of resources, so depletion is accounted for indirectly. Even where there is greater concern that depletion may not be as gradual9, the direct root cause of energy insecurity is again not the level of reserves per se, but the ability to develop new and alternative forms of energy – which is captured by the separate ‘insufficient investments in new capacity’ root cause.

Focusing on more established causes of energy security avoids the risk of measuring every potential cause regardless of the strength of its linkages to energy security impacts. This raises question as what should be included or not and calls for an iterative process with the possible inclusion of other root causes as more information is gathered. This is an integral part of the bottom up approach proposed here.

9 E.g. due to faster than expected growth and demand for energy in key emerging economies such as

China and India.

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2.2.2 Causal mechanisms

Each cause of energy insecurity listed in Table 2 - 2 eventually translates into the welfare impacts through specific causal links. These causal mechanisms are reviewed in detail in sections 2.3 to 2.5. It is useful, however, to first generically characterise these energy insecurity causal mechanisms.

As shown in the illustration below, the first stage is one of the energy insecurity root causes identified in Table 2 - 2. This affects a given sector of the supply chain (stage 2) and has knock-on effects on the remainder of the supply chain (stage III). Depending on the energy market and the infrastructures that characterise it, these knock-on effects may exacerbate the energy insecurity impacts or help to mitigate them. Energy consumption is ultimately affected, either through a change in the price or a change in physical unavailability of energy (stage IV). This chain of events ultimately affects individual and national welfare (stage V).

Illustration 2 - 1 Generic causal mechanisms of energy insecurity

2.2.3 The role of demand

The causes mentioned in Table 2 - 2 typically affect the supply of energy but, as reflected in the above illustration, the level and structure of demand for energy, stage IV, plays an important role in defining the magnitude of the resulting energy security impact. It is only through the interaction of supply and demand that impacts materialize and, as such, the demand sector is a key element of energy insecurity causal mechanisms.

In its simplest form the possible price / physical unavailability impacts of energy security depend on the absolute level of demand for the affected energy source. However, the linkage between supply and demand is more subtle and governed by two main factors.

Stage I Energy

insecurityRoot cause





Stage V Welfare Effects

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The first is the level of demand side participation. Concerns over the lack of demand participation in the market are technical and economic in scope, but also relate to the legacy of regulated market structures where much of the emphasis was on the supply side. Demand-side participation falls into two broad categories:

• Over the short term the primary concern is whether end-use demand is sufficiently responsive to price signals to mitigate short-term price effects and potentially prevent physical unavailability. This is of key concern in the electricity markets, where technologies and processes are not yet widely available at the end use level to allow broad participation in the market. This reduces the flexibility of system operators, and in the worst cases may lead to physical unavailability (e.g. via load shedding).

• Over the medium to longer term the price mechanism should help stimulate demand reduction, via conservation or improvements in energy efficiency. This helps to mitigate against price effects (by limiting the total demand for energy) and physical unavailability impacts (e.g. increasing the level of energy services that can continue to be delivered with a given level of energy storage). Where this improvement does not take place, and where overall demand continues to grow this increases the vulnerability of the system to energy security impacts.

In both cases, a reduction in demand can itself be associated with a loss in welfare. However, this will likely be far lower where this reduction is managed rather than forced (e.g. via the role of interruptible supply contracts) and where it prevents a price impact becoming a, generally more severe, physically unavailability impact.

The second factor is the level of substitutability among energy sources. In addition to the level of demand side participation, the vulnerability of energy systems depends on the capacity of end-use demand to switch to other energy sources in case of an energy security threat. For example, electric space heating could be used temporarily in case of a natural gas shortage. Similarly, some electricity generation plants have a dual fuel capability, or can co-fire with other fuels. The potential for substitution depends largely on the current technological capability and supporting infrastructure, as well as how this develops in future. An extreme case is that of road vehicles which currently offer limited substitution possibilities away from petrol and diesel. On the longer term the wide-spread roll-out of electric cars / plug-in hybrids will improve this significantly.

Ultimately, the final impact on welfare will depend on the energy services that are provided (lighting, heating, mobility, etc), as opposed to the energy itself - and the impact on the cost of, or disruption to, these services from energy insecurity. This can complicate the assessment of the impact of energy insecurity as not all forms of energy or energy carriers are necessarily equivalent. In particular, electricity is highly flexible in terms of how it can be produced and the range of energy services it

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can potentially provide, as well as unique services that only it can provide (appliances, access to ICT, etc).

2.2.4 Supply flexibility and market liquidity

A number of factors are not themselves causes of energy insecurity yet play an important role in determining the nature (price or physical availability) and magnitude of energy security impacts. This is notably the case of supply flexibility and market liquidity, which can both contribute to exacerbate energy security impacts along the causal mechanism described in section 2.2.2, and in particular at stage III.

Supply flexibility is the physical ability of a given energy market to compensate for the supply shortfall resulting from a given energy insecurity root cause (stage I). The more flexibility, the less the event is likely to result in significant energy security welfare impacts (stage V). Similarly, inflexibility may also contribute to worsen insecurity impacts.

The nature of the fuel and associated infrastructure are key determinants of supply flexibility. Fuels that are easier to handle and transport tend to provide greater flexibility in case of a supply shortfall. For example, coal and oil tend to be relatively easy to handle. Over land, they can be transported through a variety of modes including rail, road, and pipe. They can also readily be stocked. In contrast, natural gas is mostly transported by pipe and it is both costly and more complex to stock. At sea, the shipping of coal and oil is also relatively straightforward while gas requires liquefaction at the point of departure and re-gasification upon arrival, both complex and costly steps. Finally, electricity offers even less flexibility. It cannot be stored cost-effectively and requires careful quality control along transmission and distribution lines to ensure safe transport. These characteristics affect the economics of each fuel and their inherent flexibility is largely reflected in each market.

Within a given market, liquidity characterizes the ability of buyers and sellers to undertake transactions. A liquid market therefore requires that sufficient buyers and sellers are available and willing to trade. The more liquid the market is, therefore, the faster an energy security supply shortfall will translate into the appropriate price signal. In contrast, an illiquid market may exacerbate the energy security impacts.

Both supply flexibility and liquidity also contribute to determine whether the energy security impact manifests itself as a price concern or a physical unavailability concern. The more flexible and liquid the market is the less likely a supply shortfall from a given cause (stage I) is likely to lead to physical unavailability for end user (stage IV).

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2.2.5 Price, physical availability, and time

In one way or the other the energy insecurity root causes identified above generate imbalances between supply and demand in the market. Whether the resulting energy security impacts take the form of price or physical unavailability effects depends on the type of energy insecurity root cause and the energy market in question. The faster the initial imbalance can translate into a price signal the less likely physical unavailability will be of concern and the more the emphasis will be on energy security price effects. Time is therefore an essential dimension of energy security.

Some root causes, such as extreme events or load balancing failures in electricity markets, generate sudden imbalances. This puts greater pressure on the system compared to more long term imbalances, such as those generated by resource concentration. Sudden imbalances are therefore more likely to generate both physical unavailability effects and price effects.

As discussed above, supply flexibility and market liquidity also affect how well initial imbalances translate into price effects. Some forms of energy, such as electricity, are inherently less flexible than others and are therefore more prone to generate physical unavailability.

In some cases the design of the market is such that the price signal is removed. This is notably the case of regulated markets or of markets where prices are pegged to another commodity. This means that the initial imbalance in supply and demand cannot translate into a price signal. In such cases physical unavailability concerns are large and often play a preponderant role.

2.3 Energy security implications of extreme events

2.3.1 Weather events and large scale accidents

There are meteorological parameters to take into account and certain regions are more prone to extreme weather events than others, but generally speaking, extreme weather events can affect any sector of an energy supply chain (e.g. by disrupting transmission lines or in hot weather reducing the availability of cooling water such that power plants must run at reduced capacity). Similarly, large scale accidents (i.e. accidents which fall outside the scope of tolerance levels typically accounted for by industry) can also affect any sector of the supply chain.

A key difference, however, between extreme weather and all other extreme events is that it can also impact directly on the demand for energy (e.g. via increased heating and cooling demand).

Aside from the severity of the weather event or accident, if the impact is on supply operations, one important determinant of the magnitude of the resulting energy security impact is the level of market concentration of the sector(s) affected. For

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example, in the case of oil, if a refinery with large market share is made unavailable this will lead to a more severe impact than if it provided a much smaller share.

Where extreme weather impacts affect demand for energy the key factor is by how much the weather event increases short-term peak demand (as over shorter periods supply is less likely to be able to adapt to meet this).

Another important parameter in determining the resulting energy security impacts is the flexibility (see section 2.2.4) of the remaining sectors of the supply chain both upstream and downstream from the sector affected, to find alternative input sources or reduce input demand while the problem lasts. With the refinery example mentioned above, if oil production facilities are bound by pipeline to the refinery affected, their flexibility to divert deliveries to other refineries will typically be limited and the energy security impact of the disruption is likely to be more severe. Similarly, if the distribution of oil products occurs via fixed transport means, the energy security impact is also likely to be more severe than if undertaken by road, in which case distributors can get fuel supplies from other sources. In the case of extreme weather effecting demand, flexibility in the supply chain is also important in terms of the ability to rapidly increase short-term supply – e.g. via the use of reserves and storage facilities.

The nature of the energy security impact (whether a price or physical availability concern, or a combination of the two) also depends on flexibility along the supply chain. Assuming prices are set competitively, the more flexibility in the supply chain (particularly in sectors immediately up and downstream from the sector affected) the less likely physical unavailability is to occur. This also applies to extreme weather when it impacts on the demand side.

In reference to the energy insecurity causal mechanism defined in section 2.2.2, in the case of extreme weather events and large scale accidents the stages are defined as follows:

• Stage I – Random event, whether linked to extreme weather or an accident, temporarily affecting the energy supply chain or leading to a significant increase in energy demand due to extreme weather.

• Stage II – The magnitude of the disruption caused to the sector(s) of the energy supply chain affected will typically depend on the level of market concentration. In the case where impacts are on the demand sector, the main issue is the effect on peak demand and any supply shortfall.

• Stage III – The severity of knock-on effects depends on the flexibility of supply, storage and transport infrastructures. More rigid transportation, e.g. pipelines or electricity transmission and distribution lines, typically lead to more severe knock-on impacts.

• Stage IV – Impact on demand depends on the severity of the event (stage I) and implications of stages II and III as well as demand side participation

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potential and fuel substitutability capacity. Given the relatively short-term and temporary nature of most extreme events10, the emphasis will then also be on short-term participation and substitution possibilities to limit the magnitude of the impact. Where the extreme weather event leads to increased demand this naturally negates the ability for short-term demand side participation from the sectors/end-uses affected11.

• Stage V – Welfare loss due to change in price or physical availability of energy.

Whilst the focus of this project is on the impact of climate change policies on energy security (via their impact on the energy system) it is important to note that climate change itself will likely have an impact on energy security on both the demand and supply of energy. For example, it may lead to more severe hot weather peaks, lower precipitation and availability of hydropower or an increased frequency of other extreme weather events.

2.3.2 Acts of terrorism and strikes

The energy security implication of acts of terrorism as well as strikes or other forms of social unrest targeting energy infrastructures and operations are different from extreme events and accidents to the extent that disruptions are premeditated. The disruption is therefore likely to be targeted specificity at infrastructure or trade routes of strategic importance, i.e. which are part of a market sector characterized by a high level of infrastructural concentration. The causal mechanisms are therefore roughly the same as for random events with the exception that Stage I consists of targeted events rather than random events. The remaining causal mechanisms are however similar.

2.4 Energy security implications of inadequate market structure

2.4.1 Insufficient investments in new capacity

To a large extent, adequate, timely investments in new capacity within a given supply chain are a regulatory matter. It is therefore important to keep in mind that the transition to deregulated market structures is often recent. This is notably the case of the electricity and gas sectors in the EU, which only officially started their transition to deregulated structures in 2003. As a result of this shift the nature of investments in new capacity also changes. Private investment decisions may notably favour smaller increments than was the case under regulated structures. In the case of the power generation sector, for example, investments in smaller, gas-based units are likely to be more forthcoming than investments in larger scale projects. This, 10 Although some may have longer lasting consequences. 11 However, this may still occur in other sectors to ensure that any supply / demand imbalance is

minimised and that the risk of physical unavailability (as opposed to price impacts) is minimised.

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however, is not an energy security concern in itself, but simply the result of this regulatory shift.

From an energy security perspective, the main concern is that the flow of investment is hindered by problems within the regulatory structure governing energy markets and that this will ultimately affect economic welfare. Regulations may notably directly affect the revenue stream of projects. For example, in the electricity sector, while price caps are seen as a way to minimise short-term price disruptions, the drawback is that they directly affect the price signal and therefore the flow of investments in new capacity. Regulatory processes may also be overly burdensome. The administrative process involved for planning, sitting, and ultimately construction of new refineries or power plants, for example, requires numerous checks and approvals often from different branches of government. Whilst these may be addressing legitimate concerns they can create a barrier to the flow of investments, particularly in terms of the timeliness of investments.

The regulatory environment may also be perceived by investors to lack certainty. If regulations affecting revenue streams are perceived as uncertain over the lifetime of the investment this may affect investment decisions. Uncertainty about the stringency, scope and timeframe of future climate change mitigation agreements, for example, is likely to affect investment decisions throughout the energy system.

Insufficient investment results in reduced capacity margins and therefore affects the ability of energy systems to cope with fluctuations in both demand and supply. Depending on the magnitude of the investment shortfall, both price changes and physical unavailability of energy may result. Over the longer term rising prices will spur new investment. But, the key issue is then the ‘friction’ or delay in the system to determining and driving through required (and sufficient) investments in new capacity.

With respect to the energy insecurity causal mechanism defined in section 1.2.2, the stages are defined as follows:

• Stage I – Lack of investment in new capacity

• Stage II – Sector affected characterized by shrinking capacity margins.

• Stage III – Depending on the sector and fuel this may be compensated for by growing imports, displacing the investment burden to other countries and sectors of the supply chain. Knock-on effects depend on investment linkages and compatibility of investment timeframes between sectors. E.g. investment timeframe in power generation sector different from investment timeframe of a distribution / transmission sector yet both are connected.

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Delays in one sector may therefore have important effect on the flow of investment in the other sector12.

• Stage IV – Impact on demand depends of severity of event (stage 1) and implications of stages II and III as well as demand side participation potential and fuel substitutability capacity.

• Stage V – Welfare loss due to change in price or physical availability of energy.

2.4.2 Load balancing failure in electricity markets

System operators are responsible for the balancing of supply and demand in real time and for ensuring a given level of electricity quality and reliability is met. They have a variety of tools to undertake this task, including ancillary services, reliability criteria, and privileged information channels to communicate to market participants in real time. Yet the nature of the task depends largely on the characteristics of the generation mix. In particular, generation predictability is an important characteristic from a load balancing perspective. The less predictability the more technically challenging balancing becomes. Similarly, the availability of generation units which are able to ramp up power in the very short term also contributes to facilitating load balancing.

In any event, the task is complex and risks of energy insecurity are real13. Cross-border trade, for example, can contribute to improve the reliability of the system yet requires significant coordination among system operators. Miscommunication may generate unreliability and ultimately energy security concerns. The issue of miscommunication in coordinating cross-border trade of electricity was notably highlighted as one of the causes of the blackouts that hit North America and Europe in 2003/2004 (IEA, 2005).

Similarly, the recent rise in the contribution of intermittent renewable energy sources to the energy mix of many OECD countries has also raised concerns over system reliability. Arguably, all power generation sources are intermittent and therefore renewables do not fundamentally change the nature of the load balancing task or cause a specific energy security concern. Yet the process of load balancing requires careful monitoring and changes in the nature of generation technologies may raise new challenges for system operators.

12 For example, new wind generation and the proposed strengthening of the grid infrastructure in Scotland

to accommodate this. 13 A recent example includes the UK in May 2008 when near simultaneous outages at two large power

stations led to widespread but short-lived interruptions to electricity supply in some parts of the UK

http://www.nationalgrid.com/NR/rdonlyres/D680C70A-F73D-4484-BA54-

95656534B52D/26917/PublicReportIssue1.pdf

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The nature of the energy security concerns associated with load balancing is that a failure will lead to sudden price rises (e.g. due to inefficient use of plant for load balancing), or in extreme cases physical unavailability of electricity (load shedding) as system operators are unable to balance short term supply and demand.

With respect to the energy insecurity causal mechanism defined in section 1.2.2 and depicted in illustration 1-1, the stages in the case of load balancing failure are the following:

• Stage I – Supply/demand imbalance in the market which the system operator fails to compensate for.

• Stage II – This leads system components to trip (transmission lines or generation units)

• Stage III – Removal of system components increases load on other components, which, depending on circumstances and operation limits, may lead to further failures. Knock on-effects can be rapid and widespread and lead to load shedding.

• Stage IV – Impact on demand depends of severity of event (stage I) and implications of stages II and III as well as demand side participation potential and fuel substitutability capacity. However, in this case, as the event is temporary and occurs over the very short-term the focus is therefore on immediate demand-side participation options (e.g. disconnecting those consumers on interruptible contracts) and substitution options, which are generally more limited in the case of electricity supply, such as availability of on-site diesel back-up generators.

• Stage V – Welfare loss due to change in price and physical availability of energy.

2.5 Energy security implications of resource concentration

The concentration of energy resources in certain regions of the world provides a form of market power to the countries where the resources are concentrated in. If countries with high concentration of resources collude to further enhance their position in the market, the possible energy security threats might be even greater. For example, the role of OPEC in coordinating production quotas and the impact this can have on oil (and indirectly gas) prices.

The nature of the energy security impacts depends on the market in question. In the case of the international oil and coal markets, for example, market structures are well developed and the price mechanism minimizes physical unavailability risks. The main concern is therefore that market power leads to uncompetitive behaviour, and in particular that prices will be set above the competitive levels. The magnitude of the energy security impact for a given country then depends on its exposure to the fuel market risk in question.

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In the case of markets with effective price mechanisms, therefore, the stages of the energy insecurity causal mechanism defined in section 2.2.2 can be characterised as follows:

• Stage I – Uncompetitive behaviour by market participant(s), e.g. through supply restrictions.

• Stage II – shortfall in international production sector, compensated to some extent by increases in production from other countries.

• Stage III – Remaining supply shortfall passed through to other sectors in the supply chain. This may be exacerbated depending on market characteristics.

• Stage IV – Impact on demand depends of severity of event (stage I) and implications of stages II and III as well as demand side participation potential and fuel substitutability capacity.

• Stage V – Welfare loss due to change in price of energy.

In contrast, the international gas market is fragmented and in many cases dominated by long-term, bilateral, oil-indexed contracts. The price mechanism can therefore not contribute to balance gas supply and demand and physical unavailability becomes an important concern (IEA, 2007a). For a given country, the likelihood of physical unavailability occurring depends on the rigidity of the actual fuel supply infrastructure. For example, in the case where a country relies solely on imports from one country through one pipeline, if a supply shortfall occurs it will lead to the physical unavailability of imports14. In contrast, if a country imports from a variety of countries and through a variety of transport means (namely pipeline and tanker), a supply shortfall from one of its trade partners may more readily be covered by increased exports from others and physical unavailability in the importing country may be avoided.

With respect to the energy insecurity causal mechanism defined in section 1.2.2, the stages in the case of natural gas markets with limited or inexistent price mechanisms can be defined as follows:

• Stage I – Imbalance between natural gas supply and demand within a bilateral import contract setting (whether due to unexpectedly high demand or a shortfall in supply).

• Stage II – Natural gas supply shortfall in the importing country compensated to the extent possible by increased domestic production or increased imports from other countries when supply infrastructures permit it.

14 For example, a number of ongoing disputes about gas debts and transit payments have occurred

between Russia and Ukraine. These led to occasions (e.g. in January 2006 and 2009) where supplies to

Ukraine were reduced or cut-off, leading to a drop in supply to EU countries further down the transit

network.

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• Stage III – Remaining supply shortfall passed through to other sectors in the supply chain. This may be exacerbated by constraints on the domestic gas transport network.

• Stage IV – Impact on demand depends of severity of event (stage I) and implications of stages II and III as well as demand side participation potential and fuel substitutability capacity.

• Stage V – Welfare loss due to change in physical availability of energy.

Importantly, such contractual arrangements between a single supplier and user do not necessarily eliminate energy security price concerns. As through oil price indexation, much of the price risk is in fact transferred to what happens on the oil market. The welfare effects of stage V may therefore also include price changes. This is discussed further in section 5.7.

2.6 Supply chain assessment

The bottom up description of the various causal mechanisms of energy insecurity discussed above can be used to systematically assess areas of concern along the energy chain. These are shown from an EU country perspective in the tables in Appendix A. Oil, natural gas, coal, uranium, biofuels (including biomass), and electricity are considered individually.

Heat production has not been considered separately as part of the supply chain assessment, as it does not have significant energy security concerns in the same manner as electricity. For example, the same issues do not exist in terms of the ability to balance load or the vulnerability of heat distribution infrastructure given that much of it is highly localised to the point of consumption. The key concerns for energy security are to do with the supply of primary fuels for heat production, which are already considered.

The text in light red highlights the key areas of concern along the various energy chains for each type of energy insecurity and the light blue text are the areas of the supply chain (Stage IV in most cases, Stage 3 in the case of load balancing) and end-use demand (Stage I) which can contribute to aggravate the energy security impacts. In some cases, boxes are left blank. This indicates that the sector does not affect the energy security concern in question.

While there is no doubt that causes of energy insecurity can interact and that this may exacerbate energy security impacts, for clarity in the tables in Appendix A each cause of energy insecurity is considered independently

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3 Linkages between EU climate change policy and energy security

3.1 Introduction

The key link between climate change policies and energy security is through the impact of the climate change policies on the energy system. Climate change policies affect the overall level of consumption of a specific fuel – for example, by fuel switching or demand reduction. In doing so, it affects the fuel and technology mix of a country and as such may interact with energy security. Changes at the end of the energy supply chain induced by climate change policies potentially affect the energy security impacts to the EU at all earlier stages of the chain back to international imports.

This chapter qualitatively analyses the linkages between climate change policy and energy security. For the analysis the following stepwise approach is followed:

i ) Identify the generic options for climate change mitigation

ii ) Link the generic options to the climate change policies

iii ) Identify the likely short and long term impacts on the energy system of each of the generic options

iv ) Systematically link the impacts on the energy system to likely changes in the risk and magnitude of energy security impacts

v ) Summarize the impact of the climate change policies on energy security

3.2 Generic options for climate change mitigation

The overall aim of climate change policies is to reduce emissions of greenhouse gases. Such reductions can be achieved through both demand side and supply side measures.

• Reduction of energy demand (consequently leading to reduction of emissions) can be achieved by:

- Energy efficiency improvements in activities associated with greenhouse gas emissions (technological / operational changes)

- Reducing overall levels of activities associated with greenhouse gas emissions (volume)

- Moving towards a less energy-intensive economy e.g. from industry to services (structural change)

• At the supply side a number of options are available for reducing emissions:

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- Fuel switching from GHG emitting fuels to renewable energy sources for electricity production (primarily wind, solar, biomass and geothermal energy)

- Fuel switching from GHG emitting fuels to renewable energy sources for heating (primarily biomass, solar and geothermal energy)

- Fuel switching from GHG emitting fuels to renewable energy sources for transport (primarily biofuels and electricity)

- Fuel switching from GHG emitting fuels to nuclear energy for electricity production

- Fuel switching from high to low carbon fuels (e.g. from coal to natural gas)

- Improvement of conversion efficiencies (boilers, process equipment, power plants)

- Efficiency improvement of transmission and distribution (electricity, fuels, heat)

- Carbon Capture and Storage (CCS) for CO2 emissions from burning fossil fuels

Another option for climate change mitigation (which use is highly dependent on the costs of the measures and the carbon price on the market) is to buy credits on the carbon market (related to Clean Development Mechanism and Joint Implementation projects). For example, within the sector under the EU Emissions Trading Scheme (EU ETS) or within the non-traded sector under the Effort Sharing Directive (see table below). Whilst this does not impact directly on energy security there is indirect link provided by the flexibility of the mechanism, in terms of the affect on the EU energy system. Greater use of CDM credits effectively means a lower emissions reduction target from activity undertaken directly within the EU – hence there is greater flexibility (up to given limits) in terms of the changes imposed on the energy system. For example, use of CDM may allow continued use of more carbon intensive coal generation than would otherwise be the case.

Eight policies that have an impact on climate change mitigation (either directly or indirectly) are included in the analysis. These are described in the table below.

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Table 3 - 3 Summary of policies considered15

Policy Title Climate change related objectives of

the policy

ESD / 20% GHG target

DECISION No 406/2009/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 23 April 2009 on the effort of Member States to reduce their greenhouse gas emissions to meet the Community’s greenhouse gas emission reduction commitments up to 2020

The EU’s objective is to reduce GHG emissions by 20% in 2020 compared to 1990 (or 30% in the event of a new international agreement).

This is comprised of a reduction in emission within the traded sector reflected by the revisions to the EU ETS cap (see entry further below) and separate MS emissions reduction targets in the non-traded sector under the Effort Sharing Directive.

EU ETS Phase III

Directive 2009/29/EC of the European Parliament and of the Council of 23 April 2009 amending Directive 2003/87/EC so as to improve and extend the greenhouse gas emission allowance trading scheme of the Community

The objective of the EU ETS is to reduce GHG emissions by at least 20% in 2020 compared to 1990. It is supposed that for a cost-effective achievement of the 20% reduction target participants should reduce their emissions with 21% below their 2005 emission levels by 2020.

RES 20% Target

Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC

The renewable energy directive aims to achieve a 20% renewable energy share in total final energy consumption and a 10% renewable energy share in land transport. The directive targets electricity, heat and transport.

CCS Directive 2009/31/EC of the European Parliament and of the Council of 23 April 2009 on the geological storage of carbon dioxide and amending Council Directive 85/337/EEC, European Parliament and Council Directives 2000/60/EC, 2001/80/EC, 2004/35/EC, 2006/12/EC, 2008/1/EC and Regulation (EC) No 1013/2006

The CCS directive aims to enable environmentally-safe capture and geological storage of carbon dioxide in the EU as part of the Climate Action package. The directive targets industrial installations (power, heat, process) that emit CO2. It is proposed to help implement CCS as a bridging technology, with the aim of not increasing the overall share of fossil plants in electricity generation.

CCS will be incentivised via the recognition of stored CO2 under the EUETS.

In addition, up to 300 million allowances in the EU ETS new entrants reserve will be set aside to help stimulate the construction and operation of up to 12 commercial demonstration CCS projects as well as innovative renewable technology projects.

The analysis of the impact of CCS for this section is undertaken in a more general manner – i.e. assuming wide-spread roll out of the technology.

Aviation in EUETS

Directive 2008/101/EC of the European Parliament and of the Council of 19 November 2008 amending Directive 2003/87/EC so as to include aviation activities in the scheme for greenhouse gas emission allowance trading within the

The objective of including aviation in the EU ETS is to reduce climate change impacts attributable to aviation.

15 Policies reflect those adopted at the sitting of the European Parliament on 17 December 2008 - P6_TA-

PROV(2008)12-17

60


Policy Title Climate change related objectives of

the policy Community

CO2 LDVs Regulation (EC) No 443/2009 of the European Parliament and of the Council of 23 April 2009 setting emission performance standards for new passenger cars as part of the Community's integrated approach to reduce CO2 emissions from light-duty vehicles

The proposed regulation on emission performance standards for new passenger cars aims to reduce CO2 emissions from cars to 120 gCO2/km in 2012 and 95 g CO2/km in 2020. This should be realized via improvements in vehicle motor technology and innovative technologies. For 2012 the objective of 120 g CO2/km should be achieved by 130 g CO2/km for the average new car fleet by means of improvements in vehicle motor technology, and a further reduction of 10 g CO2/km, or equivalent if technically necessary, by other technological improvements and by an increased use of sustainable biofuels.

Fuel Quality

Directive 2009/30/EC of the European Parliament and of the Council of 23 April 2009 amending Directive 98/70/EC as regards the specification of petrol, diesel and gas-oil and introducing a mechanism to monitor and reduce greenhouse gas emissions and amending Council Directive 1999/32/EC as regards the specification of fuel used by inland waterway vessels and repealing Directive 93/12/EEC

One of the key measures is that by 2020, suppliers should gradually reduce life cycle greenhouse gas emissions by up to 10 % per unit of energy from fuel and energy supplied. This reduction should amount to at least 6 % by 2020, compared to the EU-average level of life cycle greenhouse gas emissions per unit of energy from fossil fuels in 2010, obtained through the use of biofuels, alternative fuels and reductions in flaring and venting at production sites. Subject to a review, it should comprise a further 2 % reduction obtained through the use of environmentally friendly carbon capture and storage technologies and electric vehicles and an additional further 2 % reduction obtained through the purchase of credits under the Clean Development Mechanism of the Kyoto Protocol.

Taxation of road fuels

Proposal for a Directive amending Directive 2003/96/EC as regards the adjustment of special tax arrangements for gas oil used as motor fuel for commercial purposes and the coordination of taxation of unleaded petrol and gas oil used as motor fuel {SEC(2007) 170} {SEC(2007) 171}

Primary objective of the proposed directive on taxation of road fuels is not to reduce GHG emissions. However, the directive does have an impact on GHG emissions as it proposes to level minimum tax rates of diesel and petrol. Currently minimum tax rates for diesel are lower than for petrol. Although end-use efficiency of diesel is higher than the efficiency of petrol CO2 emissions over the supply chain of diesel are higher.

The following table summarises for each of the policies listed above which of the generic climate change mitigation options apply. The -20% GHG target provides the combined effect of the RES 20% target, EU ETS Phase III and the targets for effort sharing in the non-ETS sectors.

61


Table 3 - 4 Summary of generic climate mitigation options that apply to policies under consideration

Mitigation option ESD / 20% GHG target

RES 20% target

CCS EU ETS Phase III

Aviation in EU ETS

CO2 LDVs

Fuel quality


Energy efficiency improvement* Ѵ Ѵ V Ѵ Ѵ Ѵ Ѵ

Reducing overall levels of activities* V V V V

Dem

and

Moving towards a less energy intensive economy V V

Fuel switching from gas and oil to RES (for electricity) Ѵ Ѵ V

Fuel switching from coal to RES (for electricity) Ѵ Ѵ V

Fuel switching to RES (for heat) Ѵ Ѵ V

Fuel switching to RES (biogas) V V

Fuel switching to RES (for transport) Ѵ Ѵ V V Ѵ

Fuel switching to nuclear (for electricity) Ѵ Ѵ

Fuel switching from high-carbon to low carbon fuels Ѵ Ѵ

Improved conversion efficiencies Ѵ Ѵ Ѵ Ѵ

Improved efficiency of transmission and distribution Ѵ Ѵ

Sup

ply

Capture and storage of CO2 emissions Ѵ

Note: *energy price rises may also spur end-use efficiency improvements and reductions in demand

62


3.3 Key effects of climate change mitigation on energy systems

In the table below, for each of the generic climate change mitigation options the expected impacts on the energy system up to 2020 and beyond are analysed. Although the period over which the energy system is being examined can be considered ‘long-term’, both short and long-term energy security risks are still considered in the subsequent sections (for example, changes to the electricity system occurring over the period to 2020 could increase the risk of a short-term energy security impact at this point due to load balancing failure).

Table 3 - 5 Effect of generic mitigation options on the energy system

Generic options

for climate

change mitigation

Impact on the energy system up to 2020 Impact on the energy system beyond 2020


- Reduced end-use electricity demand (and consequently primary fuels)

- Reduced end-use heat demand (and consequently primary fuels)

- Reduced demand for transport fuels (and consequently crude oil)

- Reinforcement of 2020 impacts


- As above - As above

Dem

and


- As above (within there may also be a degree of fuel switching – for example, away from coal towards gas as part of greater electrification under a more service-orientated economy. The effect of this is considered more explicitly as part of the fuel switching options below)

- As above

63


Generic options

for climate

change mitigation



- Increasing share of intermittent RES-E in fuel mix for electricity production

- Increasing share of dispatchable RES-E in fuel mix for electricity production

- Decreasing share of fossil fuels in mix for electricity production

- Increasing share of distributed generation

- Increasing share of end-use electricity supply (e.g. PV)

- Potential price rises for electricity might effect consumption and end-use efficiency, particularly in the household sector.

- A need for extending and strengthening of existing transmission capacity (given geographic location of RES), predictability of dispatch, interconnectors and integration of load management of different grid networks

- A need for better cooperation among transmission operators to maintain grid stability

- A stronger need for balancing power (gas-fired) or storage capacity for intermittent RES-E to back up erratic supplies from e.g. wind farms.

- A need for new biomass distribution infrastructures

- Increasing biomass use may lead to increased fossil fuel demand in cultivation (when biomass cultivation is additional and not replacing other or low-energy intensive cultivation activities)


- Potential need for storage capacity for intermittent RES-E

- In the long term the role of gas as balancing power might be taken over by dispatchable RES-E

Sup

ply


- Increasing share of RES sources in meeting heat demand

- Decreasing share of fossil sources in heat production

- Increase in demand for electricity related to use of heat pumps


- Increasing biomass use may lead to a slight increase in fossil fuel demand in cultivation (when biomass cultivation is additional and not replacing other or low-energy intensive cultivation activities)


64


Generic options

for climate

change mitigation


Fuel switching to nuclear (for electricity)

- Limited (long lead times for new plants) - Increasing share of nuclear in fuel mix for power production

- Increasing uranium requirements and fuel reprocessing facilities

- Lower share of other primary fossil fuels in mix for power production


- Increasing share of biogas in total gas supply

- Decreasing share of natural gas in total gas supply



- Large scale introduction of biogas in natural gas pipeline infrastructure requires modification of existing pipelines

Fuel switching to RES (for transport)

- Increasing share of biofuel in transport

- Increasing share of electricity in transport

- Decreasing share of oil products in transport

- Introduction of biofuel would require changes in processing facilities to enable blending with oil

- Depending on the type of biofuel new distribution structures may be needed

- Increasing biomass use may lead to a slight increase in fossil fuel demand in cultivation (when biomass cultivation is additional and not replacing other or low-energy intensive cultivation activities)


- (Potential increasing share of hydrogen in transport)

- Significant shift to electric vehicles would alter the load profile for power generation. Combined with smart grids, electric vehicles could provide opportunity for smoothing the load profile, and possibility for batteries to be used as storage on the system.


- Increased use of gas at the expense of coal (and oil) in power and heat production


- Need for sufficient excess capacity in the gas supply chain (e.g. to meet peak demand)

Improved conversion efficiencies

- Overall decrease of primary fuel use in power and heat production

- Increased use of combined heat and power plants

- Potential restructuring of refinery processes as part of fuel switching from diesel to petrol


Improved efficiency transmission and distribution

- Reduced losses will lead to an overall reduction of fossil fuel use - Reinforcement of 2020 impacts

65


Generic options

for climate

change mitigation



- Limited (technology needs to be demonstrated first) - Energy system probably more coal-based than without CCS policy

- A lower conversion efficiency of CCS plants compared to non-CCS plants leading to increased demand for primary fuel

- Due to Enhanced Oil Recovery (EOR) and to a lesser extent Enhanced Gas Recovery (EGR) domestic oil and gas reserves increase

- CCS may incentivise the use of hydrogen in the energy system

- Need for CO2 transport infrastructure

66


3.4 Effects of climate change mitigation on energy security

The changes in the EU energy system as a result of climate change mitigation policies may affect the risk and magnitude of energy security impacts. This section qualitatively assesses this by taking each effect on the energy system (from Table 3 - 5) in turn and assessing the impact (where relevant16) on energy security related to the root causes in section 2.2. The following scores are used to judge the impact:

• ≈ negligible effect; • ? uncertain effect; • + weak positive effect (i.e. potential risk/impact of energy insecurity

reduced); • ++ strong positive effect; • - weak negative effect; • -- strong negative effect; • (st) short term / (lt) long term – i.e. the period over which the energy

security impact is likely to be affected by the climate policy (and not the time period of the energy security impact itself).

All mitigation impacts on the energy system affect in one way or another the fuel and technology mix of the EU and as such may interact with each other and energy security. For example, where fuel switching takes place, the EU’s potential risks or impacts from energy insecurity across the energy supply chain for both fuels may change – i.e. the demand for one fuel has gone up at the expense of the other.

Such scoring is inherently subjective, but the key purpose of this exercise is to highlight key areas of interaction and where there are likely to be clear positive or clear negative effects, and also other areas where a mix of effects come into play. A more objective and quantitative framework is therefore necessary to gauge and compare interactions we know are either positive or negative, as well as to better understand the overall effect when the underlying effects are mixed.

In parallel to the qualitative assessments, it is recommended that the reader refers back to the supply chain assessment tables for each primary fuel and electricity (in section 2.6 and Appendix A), for more detail on the practical areas of concern for energy security that may be reduced or exacerbated by climate change policy.

The supply chain assessments also serve to highlight the internal and external risks (the latter being areas more outside of EU control). For example, efficiency improvements that reduce demand will help to reduce energy security risks and the magnitude of the impacts both within the EU and externally if they also reduce import requirements.

16 Non-relevant effects are excluded from the tables

67


Table 3 - 6 Effects of climate mitigation options on energy security impacts of extreme events

Generic options

Energy system impacts most

relevant to root cause

category/type

Effect on energy security impacts from extreme events Impact



Dem

and


- Reduced end-use electricity, heat and transport fuel demand (and consequently primary fuel demand)

- End-use energy demand (for lighting, driving, etc) remains dependent on the specific fuels used. Any supply disruption due to an extreme event will be passed through the supply chain and will affect end-use demand in case the supply chain is not able to account for the disruption. However, a reduction in demand will lower the final impact, by limiting the level of disruption - leading to positive overall impact on energy security.

- For example, energy security impacts are likely to be lowered where reserves or storage is able to absorb supply disruptions for an extended period as a result of a reduction in overall demand. Another example is the ability to cope with fluctuations in extreme weather (both hot and cold spells) which can affect both demand (e.g. for space heating/air conditioning) and supply (extreme hot weather reduces output from thermal plants due to lack of cooling or leads to low wind output). However, the reduction in end-use should lower the peak in demand for energy reducing the potential impacts from the extreme event.

- For electricity the situation is slightly different in that an absence of power means that the service is disrupted regardless of the efficiency of the end-use. However, overall gains efficiency or reductions in demand will still lead to overall improvements in two respects: a) the reliability of the overall system given greater spare capacity (i.e. less likely that a single disruption will lead to widespread failure), and b) the ability to continue services for more extended periods from backup or localised supply (e.g. battery or on-site diesel generation).

+


- Reduces all potential energy security impacts from extreme events for fossil fuels – at all stages of the energy supply chain.

++

Sup

ply



- Increases potential impacts from extreme weather due to impact on electricity generation, primarily from wind (e.g. turbines need to shut down in high winds). However, system operators take into the account the variable nature of the RES as part of their planning processes, extreme weather conditions are more likely to be more localised (limiting the effect on generating capacity to e.g. a region containing a number of offshore wind-farms) and technological improvements are increasing the tolerance/operating range of renewable systems. Hence, the overall effect is likely to be small.

- The effect of this in relation to the other extreme events is likely to be negligible.

-/≈

68


Generic options



category/type



- Increases potential impacts from extreme weather as production of biomass crops (both domestic and imports) are more prone to extreme weather events such as drought, floods and storms – the risk of which may increase in future given climate change impacts. However, existing (and possible future) production of crops is widely distributed geographically reducing the potential scale of impacts from extreme weather and there is the potential for short-term for reverse-substitution with fossil products in the event that significant disruptions to supply occur.

- The impact of fuel switching on energy security risks related to the other extreme events is likely to be negligible.

-/≈



- Decreases impact from all extreme events as infrastructure is less concentrated and disruption to a single generating plant or production facility will have less of an impact on the wider energy system.

+

- A need for extending and strengthening of existing transmission capacity (given geographic location of RES).

- An increase in the level of electricity transmission / distribution capacity to connect new geographically dispersed RES means the system may be more susceptible to impacts from extreme weather, accidents or acts of terrorism (e.g. power lines knocked out due to extreme weather or accidents). Options exist to, for example, put transmission lines underground and reduce the vulnerability, but are considerably more expensive than above ground lines.

-



++


- As per fuel switching to RES (for electricity) - Increasing share of dispatchable RES-E in fuel mix for electricity production

-/≈



- Increase in risk of impacts from all extreme events associated with electricity supply and transmission.

-




++ (lt)

69


Generic options



category/type


- Increasing share of nuclear in fuel mix for power production

- A fuel switch from fossil fuels to nuclear energy for electricity generation will have a negative impact on energy security impacts related to accidents and acts of terrorism. The impact of a nuclear accident is more severe than an accident with conventional power plants which makes nuclear power plants a more attractive target for terrorists. The risks will differ from country to country and will mainly be a result of a country’s position in the political arena.

- Energy security impacts related to extreme weather and strikes are unlikely to be different in comparison to conventional fossil plant (although disruptions from extreme events related to uranium supply are likely to be slightly lower than primary fossil fuel due to higher energy density).

-- (lt)


- Reduces all potential energy security impacts from extreme events for natural gas – at all stages of the energy supply chain.

++ Fuel switching to RES (biogas)


- Increases potential impacts from extreme weather as production of biomass crops (both domestic and imports) are more prone to extreme weather events such as drought, floods and storms – the risk of which may increase in future given climate change impacts. However, production of crops is widely distributed geographically reducing the potential for widespread impacts from extreme weather. The impact of fuel switching on energy security risks related to the other extreme events is negligible.

-


- Reduces all potential energy security impacts from extreme events for oil – at all stages of the energy supply chain.

++


- Increases all potential energy security impacts from extreme events for electricity generation and transmission.

-



- As per fuel switching to RES (biogas) -> increasing share of biogas in total gas supply. -



- Fuel switching, primarily from coal to gas will have a negative impact on energy security risks related to extreme events, primarily due to the greater susceptibility of pipeline imports, transport and distribution to impacts from extreme weather, accidents and acts of terrorism. However, given that many EU countries have already completed significant shifts to gas, the negative impacts of further shifting may be more limited.

- Impacts from strikes are unlikely to differ significantly across fossil fuels. The ability to organise strikes at coal mines is possibly easier than primary oil or gas extraction sites, but there are possibly easier options further up the supply chain (e.g. onshore refineries).

- A fuel shift from diesel to petrol will likely have limited impact on energy security risks related to extreme events.

-/--

70


Generic options



category/type




- Reduces all potential energy security impacts from extreme events for fossil fuels – at all stages of the energy supply chain for primary fuels

++


- Reduced losses will lead to an overall reduction of fossil fuel use

- Reduces all potential energy security impacts from extreme events for fossil fuels – at all stages of the energy supply chain for primary fuels. However, the maximum effect of this is likely to be limited and only occur over the longer term.

+ (lt)

- Energy system probably more coal-based than without CCS policy

- Coal is generally less susceptible to impacts from extreme weather, accidents and acts of terrorism compared to gas and oil as it is not transported by pipeline.

+ (lt)


- Increases all potential energy security impacts from extreme events for coal and gas – at all stages of the energy supply chain.

- (lt)

- CCS may incentivise the use of hydrogen in the energy system

- Reduces all potential energy security impacts from extreme events for fossil fuels – at all stages of the primary energy supply chain. However, this scale of hydrogen production is likely to be unclear. In addition, storage of hydrogen may increase exposure to the risk of some extreme events and so the overall long-term effect is unclear.

? (lt)


- Need for CO2 transport infrastructure

- Pipeline infrastructure for CO2 transport is likely to increase the potential impacts to accidents. The impact is likely to be negligible in relation to extreme weather, strikes or acts of terrorism as CO2 transport and storage is unlikely to be considered a high value ‘target’ in comparison to oil/gas pipelines, nuclear plants, etc.

- (lt)

71


Table 3 - 7 Effects of climate mitigation options on energy security impacts of inadequate market structures - Insufficient investments in new

capacity

Generic options Energy system impacts most relevant

to root cause category/type

Effect on energy security impacts from inadequate market

structures - insufficient investments in new capacity Impact



Dem

and



- The impact from changes in demand is difficult to assess directly as the effect is dynamic (see section 3.5.2 for further details). Over the longer-term is likely that the dynamic effects of demand and supply will dominate bringing the system back to an equilibrium level of security – i.e. demand changes will have negligible long-term effects.

- In the short-term there is the potential difficulty of attracting new investment to shrinking energy industries, as efficiency improvements may lead to overcapacity of existing capacity.

- The impact may also need to be differentiated across sectors that are more explicitly affected by climate policy in other ways. The electricity network will require substantial overall change and investment as part of the incorporation of new distributed generation, so the impact of decreasing electricity demand alone will be masked to some extent. By contrast the gas network is not impacted as directly in other ways by climate policy – hence the effect of demand reduction may be more explicit on the incentives for investment in e.g. new alternative pipeline routes which would add flexibility to the system.

- However, whether or not sufficient investment is attracted depends on the robustness of the regulatory structures and the level of policy uncertainty facing potential investors.

- It is also important to note that the discussion above is focused on the energy supply side. Climate policy that improves energy efficiency at the demand side is in effect attracting new investment – particularly with respect to the buildings sector, where energy demand is closely related to the overall building infrastructure.

- (st)

≈ (lt)

Sup

ply



- The large-scale introduction of intermittent generation is likely (depending on the trading arrangements in different MSs) to change the shape of wholesale electricity prices, with more frequent periods of low or even negative prices. This impacts on the economics of plant that would normally run baseload (such as nuclear) and hence may negatively change the profile of investment in other technologies. It is therefore possible that uncertainty over returns on investment, because of the difficulty of knowing how often plant will get the opportunity to run and the technical challenge of running plants more flexibly than they were designed for, will discourage or delay investment in new conventional capacity – or speed up the closure of existing capacity – and hence increase the risk of occasional capacity shortfalls.

-

72






- Increasing share of intermittent and dispatchable RES-E in fuel mix for electricity production

- Given the change in generation mix required to 2020 and beyond a significant level of additional investment is likely to be required compared to a business as usual situation (e.g. where existing assets can be run as standard to end of life). The scale of the investments may make financing problematic, particularly as some RES-E are more capital intensive compared to conventional gas plant. There can also be other significant barriers to investment, such as national or local planning issues, delays with regard to grid connection and access rights, and public acceptance. On the other hand, the more modular and scalable capacity of RES-E, coupled with shorter construction periods may help improve financing.

- However, the key overarching issue is policy uncertainty and the risk faced by investors. Policy-makers have to segment the market, creating a protected market niche for the RES investments – e.g. via subsidy support schemes such as feed-in-tariffs and indirect support from the carbon price under the EU-ETS.

- The main risk factor is then the reliability of the policy mechanisms supporting the investments, and the likelihood that these will occur. There are two elements to this risk factor:

o Risk to RES investments themselves – i.e. how profitable the RES projects will be

o Risk to the rest of the generating market – the remainder (non-RES) portion of the market has to pick up the residual risk (e.g. relating to demand uncertainty), and these residuals become magnified as the share of the protected RES market increases.

o The risks have the potential to lead to delays and a lack of sufficient investment in new capacity.

?


- In a similar manner to reduced demand, this might act as a disincentive, in the short term for further investments in existing fossil fuel supply infrastructure as well as the development of additional infrastructure flexibility such as gas pipelines, as investors will be wary of asset stranding.

- Fuel switching by itself should limited impact on incentives for fossil fuel electricity capacity, which will be affected more by the impact on overall electricity prices (see - Increasing share of intermittent RES-E in fuel mix for electricity production)

- (st)

≈ (lt)

73









- Investments in transmission infrastructure may have longer lead times than generation capacity (e.g. due to extended planning and approval periods), it can be necessary to plan prior to commitments on generation build.

- To the extent that planning is based on incorrect assumptions (or that these change over time), for example the expected levels of new capacity connected directly to the distribution network rather than the transmission network.

- It may therefore not be possible to connect new generation capacity on a firm basis (at least in the short-term) and hence the amount of effective capacity is reduced with a negative effect on energy security.

- (st)

≈ (lt)


- Given the dynamic nature of investment decisions based on supply and demand and the corresponding price signals, there may insufficient incentives in the short-term to install balancing power or storage capacity. Given the time delay in installing new balancing capacity this may reduce the level of excess capacity on the system to cope with fluctuations in demand and supply.

- However, this impact of this will depend directly on the robustness of the supporting regulatory framework and policy uncertainty (which is mitigated, for example, where capacity payment mechanisms exist)

?




- As per RES-E. However, the impact is likely to be negligible overall as RES-H technologies generally have comparable capital intensity to other heating technology, are smaller scale and more distributed in nature and will less likely to be affected by delays in planning consent, etc.

≈


- As per increasing share of intermittent and dispatchable RES-E in fuel mix for electricity production. In addition new nuclear capacity is highly capital intensive, with long lead times for construction and potential difficulties in obtaining planning consent. Focus on rapid expansion of this technology also carries the risk that other infrastructure investment is deterred, at least in the short term, but problems prevent the new nuclear capacity being developed in a timely manner – and leading to increased energy security impacts from lower supply capacity. However, the key overarching issue is policy uncertainty and the risk faced by investors.

- (st)

≈ (lt)



- In a similar manner to reduced demand, this might act a disincentive, in the short term for further investments in existing infrastructure as well as the development of additional infrastructure flexibility such as alternative gas pipelines, as investors will be wary of asset stranding.

- (st)

≈ (lt)



- As above - (st)

≈ (lt)

74







- The impact on energy security is likely to be negligible as infrastructure is on a smaller scale and more distributed and for example, is less likely to be affected by delays in planning consent, etc.

≈

- Large scale introduction of biogas in natural gas pipeline infrastructure requires modification of existing pipelines

- Insufficient incentives for distribution asset owners to invest in modifications to infrastructure may lead to an inability to utilize biogas, reducing supply capacity and increasing potential energy security impacts.

- However, the key overarching issue is policy uncertainty and the risk faced by investors (see above - Increasing share of intermittent and dispatchable RES-E in fuel mix for electricity production).

?

- Decreasing share of oil products in transport - In a similar manner to reduced demand, this might act a disincentive, in the short term for further investments in existing infrastructure, particularly in refining capacity

- (st)

≈ (lt)


- Introduction of biofuel would require changes in processing facilities to enable blending with oil

- Depending on the type of biofuel new distribution structures may be needed

- The impact on insufficient investment is likely to be negligible as infrastructure issues are likely to be comparable to, or even lower than, existing oil infrastructure.

≈



- Need for sufficient excess capacity in the gas supply chain (e.g. to meet peak demand)

- As per a decrease in demand a lower share of coal (and oil) may lead to a disincentive for further investment in existing infrastructure over the short-term, given the potential for asset stranding.

- Lack of incentives for operators to invest in supply capacity may lead to increasing potential energy security impacts, due to lower peak supply margin. But conversely greater dependence on gas may encourage further development (over the longer-term) in the flexibility of the gas supply chain, which may act to offset some of the potential impacts of a supply shortfall from a single supplier.

?




- As per changes in demand - (st)

≈ (lt)



- As per changes in demand. - (st)

≈ (lt)

75







- Need for CO2 transport/storage infrastructure - As per increasing share of intermittent and dispatchable RES-E in fuel mix for electricity production. In addition new strongly incentivising CCS prior to proving it under full-scale deployment, there is a risk that other investment in new capacity is deterred, as least in the short-term, but technical problems limit the ability to introduce CCS when expected. This is also linked to the development of CO2 transport/storage infrastructure as it will be highly capital intensive and insufficient incentives for operators and long-lead times for its development (e.g. given potential planning delays) mean that investment is locked in to expensive CO2 capture technology without the ability to ultimately store the CO2.

- Existing electricity generation can still take place from the plant once built, albeit at reduced efficiencies compared to non-CCS plant, so reduced electricity generation capacity is only a minor issue.

- (st)

≈ (lt)

76


Table 3 - 8 Effects of climate mitigation options on energy security impacts of inadequate market structures - Load balancing failure in

electricity markets

Generic options Energy system impacts most relevant to

root cause category/type


structures - Load balancing failure in electricity markets Impact



Dem

and



- Reductions in electricity demand, particularly at peak times, will reduce the potential for energy security impacts from load balancing failures, by providing a greater peak capacity margin between peak demand and supply.

+



- A need for better cooperation among transmission operators to maintain grid stability


- In the long term the role of gas as balancing power might be taken over by dispatchable RES-E

- Fuel switching to more intermittent renewable energy sources for producing electricity will have a negative impact on the risk of energy security impacts related to load balancing failures. However, the level of risk is highly complex and dependent on several factors such as, the availability of operating reserve cooperative efforts to maintain grid stability, efforts to better predict dispatch and investment in interconnection capacity.

-- Fuel switching to RES (for electricity)



- Greater distributed generation may lead to reduced demands on the central electricity network, but the effect on the risk of load balancing failure is complex and will depend on the interconnection and capacity of the local distribution networks. Where a local network is effectively separate from the main electricity transmission system it would still require sufficient operating reserves to cover shortfalls in generation from intermittent supply.

?

Sup

ply



- Depending on the load profile, this may increase the peak demand for electricity and potentially the risk of load balancing failure, although if this offsets some of the demand for electric space heating the overall effect may be small.

?

77




- As baseload nuclear supply is inflexible (very low ramp rate), an increasing share of it on the system will make it more difficult to respond to rapid fluctuations in supply and demand, potentially leading to increased impacts from load balancing failure.

- lt


- None - Negligible effect ≈



- Significant shift to electric vehicles would alter the load profile for power generation. Combined with smart grids, electric vehicles could provide opportunity for smoothing the load profile, and possibility for batteries to be used as storage on the system.

- Depending on the load profile for electric vehicles (in particular plug-in electric road vehicles) this could reduce or aggravate the energy security impacts associated with a load balancing failure. For example, daytime loading could increase peak load requirements and reduce the available capacity margin – increasing the potential risk of a load balancing failure. Conversely off-peak demand (e.g. overnight charging) could smooth out the difference in peak and off-peak demand that needs to be managed with respect to available baseload plant.

?


- None - Negligible effect ≈


- Increased use of combined heat and power plants - The effect of increased CHP on the risk of load balancing failure will depend on the flexibility of the different types of CHP to help respond to short-term fluctuations in demand and supply. Some, such as year-round baseload supply for industrial processes or district heating is relatively inflexible whereas others are less so.

?



- Effect on risk of load balancing failure is likely to be negligible ≈



- Effect on risk of load balancing failure is likely to be negligible ≈

78


Table 3 - 9 Effects of climate mitigation options on energy security impacts from supply shortfall associated with resource concentration

Generic options



category/type

Effect on energy security impacts from supply shortfall associated with

resource concentration Impact



Dem

and



- A reduction in energy demand is likely to have a limited short-term impact on energy security impacts from a supply shortfall (from imported supplies) associated with resource concentration. Any supply disruption associated with resource concentration will be passed through the supply chain and will affect end-use demand in case the supply chain is not able to account for the disruption. However, over the longer term a reduction in demand will help reduce the impact of energy security from this root cause by minimizing the disruption caused. For example, domestic (strategic) reserves would be able to absorb supply disruptions for an extended period as a result of demand reduction.

++


- Fuel switching to domestically produced renewable energy sources for producing electricity will have a strong positive effect on energy security risks from supply shortfalls associated with resource concentration, by helping to reduce dependence on fossil imports. The energy security risk may differ from country to country and is e.g. dependent on the short term ability and flexibility to switch to another supplier.

++



- As fuel switching to renewable energy sources may increase the share of imported biomass, energy security risks with respect to resource concentration may increase as biomass producing countries may also be subject to geo-political risks over the longer-term. However, imports of biomass for electricity are likely to be very low (imports of biofuels for transport are likely to be higher) given domestic production, and the potential range of import countries is likely to be more geographically dispersed compared to fossil fuels, reducing dependence on a single supplier. Hence the overall effect impact on energy security is likely to be negligible

≈


- As per switching to RES-E above ++

Sup

ply

Fuel switching to

RES (for heat) - Increasing share of RES sources in

meeting heat demand - As per switching to RES-E above ≈

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Generic options



category/type




- As per switching to RES-E above (but over the longer-term) ++ (lt)


- Increasing uranium requirements and fuel reprocessing facilities

- Increased nuclear electricity production may lead to increasing requirements for imported uranium and hence increasing impacts from a supply shortfall associated with resource concentration. However, the effect is likely to be smaller given the lower fuel requirements per unit of energy production (compared to fossil fuels) and the potential for reprocessing or nuclear fuel substitution to mitigate against import requirements.

- (lt)


- As per switching to RES-E above – for natural gas only +



- As per switching to RES-E above ≈


- Fuel switching to domestically produced renewable energy sources for producing electricity will have a strong positive effect on energy security risks from supply shortfalls associated with resource concentration, by helping to reduce dependence on oil imports. The energy security risk may differ from country to country and is e.g. dependent on the short term ability and flexibility to switch to another supplier.

++


- As fuel switching to renewable energy sources will increase the share of imported biofuels, energy security risks with respect to resource concentration may increase as biofuel producing countries may also be subject to geo-political risks over the much longer-term. Imports for biofuels for transport (as opposed to biomass for heating and electricity) are likely to be higher in the EU. However, the significance of this issue is likely to be negligible until the share of biofuels reaches a ‘substantial’ level. The new target for 2020 is only for a 10% share in transport fuels, some of which will still be met by domestic production.

≈/- (lt)

Fuel switching to

RES (for transport)


- The energy security impacts associated with resource concentration may increase or decrease depending on what energy source is used to generate the electricity (e.g. RES versus fossil fuels)

?



- Fuel switching from coal to gas will have a strong negative impact on energy security risks from supply disruptions associated with resource concentration as coal is considered a more energy secure fuel than gas with greater remaining domestic coal supplies. A fuel shift from diesel to petrol will have limited impact on energy security risks related to resource concentration.

--

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Generic options



category/type




- Increased use of combined heat and power plants

- As per reduced end-use, electricity, heat and transport fuel demand ++ Improved conversion efficiencies


- Switching towards greater petrol production in refinery processes may lead to a preference for lighter sweeter grades of crude oil, over heavier sourer grades. This may lead to potentially higher impacts from a supply shortfall associated with resource concentration over the long-term, as supplies of different grades are also clustered geographically.

- (lt)



- As per reduced end-use, electricity, heat and transport fuel demand – however the reduction in primary fossil fuel use is likely to be more modest than potential reductions in end-use demand or from improvements in conversion efficiency, and also occur over the longer term.

+ (lt)


- If more coal is used under CCS policy at the expense of gas, this will likely have a strong positive effect as greater domestic coal reserves exist within the EU, and imported coal is subject to fewer resource concentration risks than imported gas.

++ (lt)


- Lower conversion efficiency will increase primary fuel requirements, potentially leading to increased imports (and energy security impacts associated with a supply shortfall from resource concentration) over the longer term. However, the negative impact is likely to be lower for coal CCS than gas CCS for the reasons above.

- (lt)

Capture and storage

of CO2 emissions

- Due to Enhanced Oil Recovery (EOR) and to a lesser extent Enhanced Gas Recovery (EGR) domestic oil and gas reserves increase

- As captured carbon can be used for enhanced oil and gas recovery (EOR / EGR), domestic oil and gas reserves increase having a weak positive impact on energy security related to resource concentration, by reducing import requirements.

+ (lt)

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3.5 Summary

The table below summarizes the effect of generic climate change mitigation options on the risks/magnitude of energy security impacts of the different root causes. Due to the effects of fuel switching and demand reduction for primary fuels (either in end-use demand or indirectly through improved conversion efficiencies), climate change effects interact with the majority of energy security issues.

In some cases the interactions are clear, for example, demand reduction leads to positive effects for all root causes with the exception of insufficient investment in new capacity. In other cases the linkage is less clear with a mix of positive and negative effects.

3.5.1 Extreme events

The main linkages between generic climate policy options and ES impacts regarding extreme events are:

• A reduction in demand (direct or indirect) or switching away from fossil fuel leads to reduced impacts across all fossil fuels.

• A shift to smaller and decentralised RES generation for both heat and electricity is likely to reduce the potential impact of extreme events; however, the expansion of larger-scale RES-E is likely to require an expansion of transmission and distribution networks, which may slightly increase the vulnerability to extreme events.

• Fuel switching, primarily from coal to gas, will lead to increased risk of impacts from extreme events (primarily accidents and acts of terrorism), particularly those outside of the EU due to increased imports and reliability on pipeline supply.

3.5.2 Inadequate market structures

The most important linkages between climate policy and ES impacts regarding inadequate market structures are:

• Increased use of intermittent renewable electricity may lead to load balancing failures in the electricity market.

• The overall scale of the required investment in some technologies (e.g. renewables, CCS or nuclear) and infrastructure is significant. These technologies are more capital intensive and may suffer from other barriers or constraints (such as delays in planning permission or grid connections) compared with standard fossil fuel technologies – particularly gas plant. This may make financing of investment – e.g. in new renewables (primarily for electricity, but likely to be less so for RES-H) more difficult and lead to impacts from insufficient investment in new capacity in the short-term.

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• Increased use of renewable energy and a decrease in fossil fuels (either from fuel switching or efficiency improvements) may lead to a disincentive in investments in the fossil fuel supply infrastructure, at least in the short-term, particularly with respect to new and alternative pipeline routes. These investments would add significant flexibility to the system and reduce the risk and magnitude of potential impacts further down the supply chain.

However, the potential risk of insufficient investment in new capacity is a complex issue and difficult to assess in terms of generic climate mitigation options, due to two important and interlinked issues are:

• The dynamic nature of investments.

• The robustness of the regulatory framework and the level of policy uncertainty.

Dynamic versus static assessments

An important issue is that static assessments of energy security impacts may be misleading. In particular, there can be a dynamic relationship between a change such as improved energy efficiency and the final equilibrium impact on energy security via the intermediary effects on demand, prices, new investment and spare capacity17 as outlined in the figure below.

Illustration 3 - 2 Simplified example of short-term impact of energy efficiency on energy security in the electricity sector

Source: BERR (2007)

Note: Impact on prices from energy efficiency changes will be affected by rebound effects. 17 It is also noted that in reality, outcomes for energy security may depend as much on market

participants’ expectations as actual outcomes (BERR, 2007).

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In the short-term improved efficiency may lead to an increase in spare existing capacity (temporarily improving energy security in cases such as available coverage of storage capacity), but act as a disincentive for investment in new or replacement capacity. However, over the longer term the price mechanism should act in a dynamic way to reach an equilibrium level, such that the overall impact of the climate policy on the energy system, in terms of insufficient investment, is neutral. I.e. in this case if demand starts to rise again this will lead to increasing prices and investment in capacity18.

A key issue is then the ‘friction’ or delay in the system to determining and driving through required (and sufficient) investments in new capacity, for example, due to distortions in the price mechanism or high levels of investment risk. In severe cases this may lead to a shortfall in overall energy supply versus demand.

Policy uncertainty

Policy uncertainty is focused primarily on the ability to deliver the sufficient additional investments to the energy system infrastructure required by climate policy. For example, the scale of new investment in renewables is only an issue insofar as the incentives provided by the market structure are not adequate. At present many Member States have specific RES support policies and indirect incentives from the carbon price under the EU ETS.

However, the incentives from existing schemes may not be sufficient to deliver the substantial additional capacity required and differences in their implementation can lead to various levels of uncertainty and risk for investors (for example, greater revenue certainty under a feed-in-tariff versus a tradable certificate scheme).

Importantly, these policy risks are not confined just to investments in RES (or other new) technologies. Delivering investment in RES requires a segmentation of the electricity market to create a protected market niche (at least in the short-medium term). The remainder of the electricity market (i.e. non-RES technology investments) have to pick up the risks associated with total electricity demand uncertainty as well as potential non-delivery of RES targets. As the market niche for RES expands, the risks for the remainder of the market are magnified.

This links to the issue of dynamic investment and ‘friction’ in the system, as greater investment risk for the remainder of the market, coupled with any under-delivery of

18 It should be noted that although dynamic effects will tend to smooth out the perturbations caused by

climate policy, leading to neutral effects on energy security, the static analysis nevertheless indicates the

direction and potential magnitude in which the system will try to adjust. Broadly speaking, the sign of the

impact will indicate whether these adjustments incur a positive or negative price adjustment (i.e. positive

or negative net economic impact). Therefore, although it is clearly a simplification, the static analysis is

still an appropriate way to analyse the interactions.

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renewables may lead to a shortfall in supply versus demand. In addition, there are also, various other barriers, such as delays in planning permission, that may also need to be addressed and which can exacerbate the effects of friction in the system19.

Looking forward, a number of the EU climate policy proposals are still under negotiation and following finalisation at the EU level, there will then be further uncertainty as MSs determine implementation specifics on a country-by-country basis (e.g. adjusting existing support schemes upwards to deliver the higher RES targets). Substantial overall levels of investment will be required to transform the energy system, but this uncertainty could deter investment in the short-medium term.

The other main interaction between climate policy and investment is the potential reduction in demand for a specific fuel or use of network infrastructure, either due to efficiency improvements fuel switching or greater use of distributed generation. This may lead to reduced incentives for further investment in capacity, however, it can again be addressed by ensuring the right incentives/regulatory approach is put in place – for example, to reduce the risk of potential asset stranding20. The issue of insufficient investment is discussed further as part of section 5.5. It is also proposed to examine the issue of insufficient investment further more qualitatively as part of the case-studies in section 7.

The assessment scores the impact of generic climate change mitigation options on insufficient investment under the broad assumption that sufficient incentives are not in place. The scoring may therefore appear slightly negative, but it is more within the EU and Member States power to address this root cause of energy insecurity (in contrast to issues which are external to the EU)21.

19 I.e. even where signals for new investment occur they may lengthen the time before the new capacity is

developed. 20 For example, the UK electricity and gas markets regulator is currently undertaking an overarching

review of the current approach to regulating the transmission and distribution networks in light of the

substantial changes and investments needed as part of future climate change objectives (Ofgem, 2009). 21 The power to address this does also need to be set in contrast with their ability to address the negative

effects of market power within the EU described in section 2.2.1, which has purposefully been excluded

from the list of energy security root causes. In the latter case the EU/MSs are acting to stop an activity

(e.g. anti-competitive behaviour) that is already occurring at an individual company or group of companies

(e.g. via fines or splitting up monopolies/oligopolies). By comparison, in the framework of a competitive

market individual companies cannot be forced to expand production / capacity as this would discriminate

against different participants. Incentives / obligations are then imposed on the market as a whole and

whether the objective is reached will therefore depend on the type of mechanism and the level of policy

uncertainty.

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3.5.3 Resource concentration

The most important linkages between climate policy and ES impacts regarding resource concentration are:

• An increased use of renewable energy sources on the expense of fossil fuels decreases the vulnerability to energy impacts related to resource concentration

• An increased use of imported gas at the expense of domestic and imported coal increases the vulnerability to energy risks related to resource concentration.

3.5.4 Other issues

Areas where the linkage is unclear and needs closer examination include:

• The effect of increasing levels of distributed generation (CHP / RES technologies) and new demand loads (e.g. electricity for transport) on the potential impacts of load balancing failure.

• CCS, where a mix of positive and negative effects are seen. However, a broad shift of the system towards coal would lead to sizeable reduction in energy security impacts, particularly with respect to resource concentration and extreme events.

Whilst the analysis has focused on the impacts of generic climate policy options on individual root causes there is a clearly an overlap in impacts across root causes, in particular:

• Insufficient investment may lead to increased risk/impacts of load balancing failure (due to reduced capacity margins), or increased risks of large-scale accidents. In addition, lack of investment may exacerbate potential risks from resource concentration by reducing the flexibility to source alternative supplies.

• Extreme events will also interact with load balancing failure, where they affect generating capacity, demand for energy or the transmission infrastructure.

There are also a number of factors that could lead to significant differences in impacts on energy security (from climate policy) across Member States.

• The current demand and fuel mix used across sectors in different Member States

• The infrastructure used for final energy delivery in member states – particularly gas network and Member States’ position on this.

• (Immediate) access to alternative fuels, or storage facilities.

• The level of reliance on the carbon market in meeting national targets, thus reducing the level of direct EU energy system effects.

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In terms of the split between internal energy security impacts that the EU can more directly influence and external impacts where there they have less influence, this is clearly split by the changing dependence on imported fuels and the route by which such fuels enter the EU. Hence, where climate policy reduces such import dependence it will reduce the risk and magnitude of energy security impacts on the EU associated with resource concentration and extreme events. The focus of this analysis is on EU climate policy, however, in the situation of a global agreement on climate mitigation this may also have a significant impact on resource concentration. For example, oil production (and particularly future exploration) may decline more rapidly in some countries leading to further resource concentration in the remaining suppliers where it is still economical to produce - and hence lead to an increased risk of energy insecurity.

Whilst there are some clear areas where the impact of climate policy leads to broadly positive improvements (particularly demand reduction) or negative deteriorations (fuel switching to gas) in energy security, there are others where the impact is more complex and difficult to evaluate adequately via a purely qualitative assessment - due to the mix of positive and negative interactions.

Some generic climate policy options can lead to opposing effects across the root causes, and even multiple positive and negative effects within each root cause. On top of this many of the actual climate policies in Table 3 - 4 effectively implement multiple combinations of these generic options. A final complication is that the scale of the actual energy security impacts will ultimately depend on the scale of the climate policy’s impact on different parts of the energy system (e.g. the actual change in demand for a fuel versus the degree of fuel switching). All of which reinforces the need for a more objective, quantitative framework for assessment.

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Table 3 - 10 Summary of interactions between generic EU climate mitigation options and energy security

Extreme events Inadequate market structures Climate change mitigation

options Extreme weather


Acts of terrorism

Strikes Insufficient investments in new

capacity

Load balancing

failure


concentration*


+ + + + - (st) / ≈ (lt) + ++


+ + + + - (st) / ≈ (lt) + ++

Dem

and


+ + + + - (st) / ≈ (lt) + ++


++/- ++/- ++/- ++ - (st) / ≈ (lt) / ? -- ++


++/- ++ ++ ++ ? ≈ ++

Fuel switching to nuclear (electricity)

++ (lt) ++/-- (lt) ++/-- (lt) ++ (lt) - (st) / ≈ (lt) - (lt) ++/- (lt)


++/- ++ ++ ++ - (st) / ≈ (lt) / ? ≈ +

Fuel switching to RES (transport)

++/- ++/- ++/- ++/- - (st) / ≈ (lt) ? ++/-/?


-/-- -/-- -/-- ≈ ? ≈ --

Improvement conversion efficiencies

++ ++ ++ ++ - (st) / ≈ (lt) ? +/-

Improvement efficiency transmission and distribution

+(lt) +(lt) +(lt) +(lt) - (st) / ≈ (lt) ≈ + (lt)

Sup

ply


+/- (lt) +/- (lt) +/- (lt) +/- (lt) - (st) / ≈ (lt) ≈ ++ / - (lt)

Note: ≈ negligible effect; ? uncertain effect; ++/+/?/-/-- mix of effects; + weak positive effect; ++ strong positive effect; - weak negative effect; -- strong negative effect; (lt) long term / (st) short term. * Focused on EU mitigation efforts, as mentioned above, a concerted global mitigation effort could lead to increased resource amongst the remaining suppliers where it is still economical to produce fossil fuels.

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4 Review of existing energy security indicators

4.1 Introduction

The previous sections have outlined the framework for energy security that is being used throughout this study and the most important linkages between climate change policy and energy security. This section reviews existing indicators that have been used to ‘measure’ energy security. An important distinction first needs to be made between:

• Vulnerability-based indicators: the majority of indicators in this section only measure inputs that can be considered a proxy for the potential risk and/or magnitude of an energy security impact should it actually occur. For example, import dependence provides a proxy for the vulnerability of the energy system to a physical interruption to energy imports rather than a measure of the actual disruption to imports.

• Outcome-based indicators: by contrast, these indicators aim to measure the actual outcome of energy insecurity. In an ideal world an outcome-based indicator would measure the actual welfare impact of energy insecurity. However, given the inherent uncertainties in estimating this, an estimate of the level of physical unavailability of energy is normally used. Outcome-based indicators are considerably more difficult to apply as they generally rely on probabilistic assessments or are integrated within the assessments provided by complex modelling approaches. Only those in Appendix B - B 3 can really be considered outcome-based indicators, and include: expected energy unserved; a security of supply function for the MERGE model, and cost of failure of the electricity system.

This can be related back to the Stages in the generic causal mechanism in section 2.2.2. An outcome-based indicator targeted at a particular stage will capture the impacts of each of the preceding steps leading up to this point. By contrast, a vulnerability-based indicator will only provide a proxy for the potential risk/magnitude of the specific stage it is targeted at – see diagram below. It is possible, as illustrated in many of the examples in the sections below, for the vulnerability-indicator to have multiple components targeted at different stages.

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Illustration 4 - 3 Indicator type and link to causal mechanisms of energy security

4.1.1 Approach

Appendix B describes each of the indicators in turn and then assess which aspect(s) of energy security they are, at least in theory, trying to measure. This mapping has been undertaken in relation to our framework (in section 2) focusing on the following aspects:

• The root cause (category / type) of energy insecurity that the indicator is trying to measure. The supply chain assessment of energy security impacts in 2.6 also helps to highlight how the practical focus of the indicator relates to specific root causes.

• The stage(s) of the causal mechanisms that the indicator is targeting for each root cause – see sections 2.3 to 2.5. Where a vulnerability indicator only targets the later stages, III and IV, it is likely to be generic across all relevant root causes – for example, a measure of the flexibility of gas infrastructure at Stage III provides a proxy for the final magnitude of a disruption earlier on in the supply chain22.

• The physical elements of the energy supply chain (e.g. extra-EU imports or intra-EU domestic production) that the indicator is targeting and the energy sources it is applicable to.

22 I.e. where the infrastructure is more flexible (an ability to switch to LNG supplies or draw on storage) the

final magnitude of the impact from a disruption to pipeline supply will be lower than the case where there is

more limited flexibility.

Stage IEnergy

insecurityRoot cause





Example of outcome based

indicator up to stage III

& & &

Example of vulnerability based indicator with

multiple components targeted at each stage

Stage I

proxy Stage II

proxy Stage III

proxy Stage IV

proxy

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The indicators are then evaluated against the following criteria (the assessment is summarised in section 4.2):

i. Suitability. How well does the indicator measure the relevant aspect(s) of the ES framework? For example, in an ideal situation the indicator would be outcome based and directly quantify the welfare loss of a price and/or physical unavailability impact due to a root cause of energy insecurity. However, the vulnerability indicators are based on various proxies, which may not be a particularly suitable measure of the specific aspect of the ES framework.

ii. Transparency. How transparent and objective is the indicator and to what extent is expert judgment required when applying it, for example in terms of weighting different components of an aggregate indicator against each other?

iii. Availability of data. Is sufficient data available to compile the indicator at both the EU and individual Member State level? Is the quality of the data needed for the indicator robust or uncertain?

iv. Ability to forecast. Projection data is naturally more uncertain compared to historic data. However, given that this study is focused on the period to 2020 and 2030 there may be particular data requirements that are even more uncertain when projected into the future and which do not form part of standard EU modelling assessments.

The mapping and evaluation are considered important, as many previous studies have tended to use seemingly arbitrary lists of indicators, without providing sufficient justification of why they have been chosen and how the element they are measuring overlaps with the other indicators selected. This is necessary to support the development of specific quantitative approach to the assessment of climate policy on energy security in section 5 by determining how much of this can be based on the application or modification of existing indicators.

It should also be noted that the indicators are discussed in a generic context – e.g. in terms of their application to a country. The study is focused on the impacts at both an EU and Member State level, however, in the latter case a distinction may need to be made in the application of some indicators between

4.1.2 Structure of indicators reviewed in Appendix B

The following groups of indicators are examined in Appendix B:

• Vulnerability indicators – focusing on specific energy security issues (section B 1)

- Infrastructure capacity and reserve indicators

- Measures of the importance of energy in the economy

- Dependence on non-domestic production

- Indicators of investment in adequate supply

- Measures of diversity

- Other vulnerability indicators

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• Vulnerability indicators - overall system and hybrid approaches (section B 2)

- Adequacy of energy supply to demand

- Net import dependence and diversity in a market

- Diversity in both supply to and within a market

- Long-term energy security indicator

- IEA Energy Security Index

- Supply / Demand Index

• Outcome based indicators (section B 3)

- Expected energy unserved

- Security of supply function for the MERGE model

- Cost failure of the electricity system.

4.2 Summary

A wide range of energy security indicators exist in the current literature, however, the vast majority of these are vulnerability-based, as opposed to outcome based. The latter often draw directly on detailed, situation specific modelling, whereas the main purpose of this project is to draw on existing outputs from energy system modelling to assess the impacts on energy security. This naturally focuses us on the use of vulnerability indicators.

The vulnerability indicators cover the full spectrum from relatively simple indicators, targeting a specific aspect/component of energy security, to attempts to provide a system wide assessment of energy security – often combining the individual components into an aggregate or hybrid approach.

The indicators have been assessed in relation to our energy security framework outlined in section 2.2 and this is summarised in the tables below. It is instructive to note that no aggregate indicator provides an adequate measure of all the relevant root causes of energy insecurity and current attempts to do so lead to a strong trade-off in transparency. In addition, the relevance of a number of indicators to energy security issue (i.e. the identified root cause) is unclear or somewhat spurious.

Net import dependence is a case in point, providing a proxy for the upper bound vulnerability to physical unavailability impacts from all relevant root causes at the import stage. However, the indicator provides no information on the relative contribution/likelihood of different root causes and is less relevant in markets such as oil where price, rather than physical unavailability, impacts dominate.

In terms of the potential applicability of existing indicators to this study, the IEA energy security index (price and volume) provide a useful starting point upon which to further develop an indicator addressing the resource concentration root cause. Similarly, peak de-rated capacity margin offers a useful proxy for the load balancing root cause type and potentially others depending upon how it is applied. There are, however, a number of gaps in existing indicators. In particular those which provide an

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adequate proxy of the vulnerability of the energy system to extreme events specifically – which is to some extent dependent on the geographic location and concentration of infrastructure.

Similarly, existing indicators offer only limited proxies to assess insufficient investment. This would ideally be an outcome based indicator contrasting required versus actual investment, but it is difficult to forecast the latter forward within the framework of existing models. Vulnerability-based indicators, such as the general business environment, offer a limited proxy, but are quite far removed from the specific issue of energy infrastructure investment and are again difficult to forecast.

As highlighted in section 3, the shift away from fossil fuels to renewables (with a key exception of gas) helps to reduce potential energy security impacts associated with resource concentration, but at the expense of increasing exposure to extreme event and inadequate market structure issues. Without indicators covering the range of root causes an assessment of energy security impacts from climate policy could prove somewhat misleading.

There is also generally limited analysis of the vulnerability of the more ‘generic’ parts of the energy system at Stage III and Stage IV – i.e. the flexibility of the rest of the supply system for a fuel and the demand side interaction. Vulnerability indicator components targeted at these stages could be combined with components more explicitly targeted at the individual root causes at Stage I/II to provide a better indication of the overall vulnerability of the system (and hence a better proxy for the final impact on welfare). For example, even where the vulnerability to resource concentration related impacts increases, changes in the vulnerability of the rest of the system (e.g. a greater number of alternative supply routes or the ability for consumers to more easily switch to substitutes) may help counteract this.

Few existing indicators apply a vulnerability metric at Stage III and where they do this is relatively simple – e.g. ESI volume uses the share of pipeline gas supply which can be offset directly by increasing the use of LNG in gas imports, energy margin shows the maximum annual supply from various alternative sources (including storage). The SDI provides a more detailed assessment, but is based primarily on expert judgements and weightings.

Similarly, there is limited analysis at stage IV in terms of the importance of the energy source in demand. Most indicators use a simple proxy of the share of the fuel in primary energy, or supply versus peak demand to indicate the end-use vulnerability of the energy system. Even measures such as the diversity of supply within a market using an SWI offer limited additional guidance, as this approach contains no measure of disparity between energy types. Increasing diversity, for example, offers a limited linkage to real world factors such as the ability for consumers to substitute for alternative fuels over the short or long-term. The SDI again provides a slightly more detailed approach, using a measure of energy intensity versus a benchmark as a guide to the demand-side efficiency response to energy security impacts. But, the linkage between supply and demand in this approach is not direct.

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Table 4 - 11 Summary of vulnerability indicators – focusing on specific energy security issues

Indicator Elements of

energy supply chain

Root causes (category / types)

Causal Stages

Suitability TransparencyAvailability

of data Ability to forecast

Storage capacity and stocks of critical fuels

Storage – all energy sources excluding electricity

Not a direct proxy – affects magnitude of impact

III / / /

Load duration of back-up fuel supplies

Storage - electricity Not a direct proxy – affects magnitude of impact

III /

Pipeline capacity Import, transport and distribution – oil and gas.


III / / /

Refining capacity and utilisation

International production / processing and domestic processing - oil


III / /

Measures of importance of energy in the economy

End-use demand – all energy source


IV / /

Net Import Dependence

International production / processing, import and end-use – all energy sources.

All - proxy for all root causes leading to physical unavailability impacts

II, IV / / / /

Net energy import bill

International production / processing, import and end-use – all energy sources.

All - proxy for all root causes leading to both physical and price unavailability impacts

II, IV

Domestic production to consumption

Domestic production, end-use – all energy sources

Extreme events and inadequate market structure – proxy for physical unavailability impacts

II, IV / / /

General business environment

None Insufficient investment I / /

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energy supply chain


Causal Stages



Patents in energy technology sector

Potentially all. Insufficient investment I /

Ratio of investments to turnover

Potentially all. Insufficient investment I / / /

Market price signals

International production / processing and imports, domestic production including electricity generation, and end-users.

Insufficient investment and resource concentration – proxy for price impacts

I / / /

Diversity of energy supply in a market / country

End-use - all energy sources


IV / / /

Diversity of energy supply to a market / country

International production / processing, imports – for all energy sources in liberalised markets – all energy sources

Resource concentration – proxy for price impacts

I

Mean Variance Portfolio Theory

Various: including International production / processing, domestic production – all energy sources

All – proxy for price impacts I, II, III / N/A

Market Liquidity Various: including International production / processing, domestic production – all energy sources


III / / N/A – just scaling factor in this case

N/A – just scaling factor in this case

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energy supply chain


Causal Stages



Political stability International processing / production, imports – all energy sources

Resource concentration, extreme events (acts of terrorism, strikes) and insufficient investment in new capacity – proxy for both price and physical unavailability impacts

I /

RPRs International processing / production, domestic production – all non-renewable primary fuels

None II N/A / /

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Table 4 - 12 Summary of vulnerability indicators - overall system and hybrid approaches


energy supply chain

Root causes (category /

types)

Causal Stages



Peak capacity margin

Domestic electricity generation (centralized and distributed)

Inadequate market structure – proxy for both price / physical unavailability impacts

I – for load balancing

II – for insufficient investment

IV – both root cause types

/ /

Peak de-rated capacity margin

Domestic electricity generation (centralized and distributed)

Inadequate market structure and extreme events – proxy for both price / physical unavailability impacts

I – for load balancing

II – for insufficient investment and extreme events

IV – both root cause types

/ / /

Energy margin International production /processing, imports, domestic production, storage, end-use – all energy sources.


II, III, IV / /

Net import dependence and diversity in a market

International production /processing, import, end-use – all energy sources.


II, IV / / /

Measuring diversity in both supply to a market and within the market

International production / processing, imports, end-use – for all energy sources.


I, IV /

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energy supply chain


types)

Causal Stages



Long-term energy security indicator

International production / processing, imports, end-use – for all energy sources

All – proxy for physical unavailability impacts


I, II, IV

/ / /

IEA energy security index (ESIprice and ESIvolume)

International production / processing, imports, end-use – oil, gas, coal

Resource concentration – ESIprice proxy for price impacts, and ESIvolume proxy for physical unavailability impacts

I, IV for ESIprice

II, III, IV for ESIvolume

/

Supply / Demand Index

All aspects Load balancing – proxy for price / physical unavailability impacts

Resource concentration – proxy for physical unavailability impacts

I for load balancing

II for resource concentration

Also III and IV

/ /

Table 4 - 13 Summary of outcome based indicators

Indicator Elements of energy

supply chain


types) Causal Stages Suitability Transparency

Availability of data

Ability to forecast

Expected energy unserved

Various: examples include international production /processing, imports, domestic production, storage, end-use – all energy sources.

All – proxy for physical unavailability impacts

Up to various stages depending upon detail of underlying modelling work

/

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5 Development of specific quantitative approach

5.1 Introduction

The previous section evaluated a wide-range of existing indicators and their ability to assess the specific energy security issues of interest, as defined by our framework in section 2. A distinction was made between outcome-based indicators that are a better assessment of the actual impact of energy security, but which rely on dedicated modelling and / or complex probabilistic assessments, and indicators which provide only a measure of the vulnerability of the system to energy security impacts (in terms of the risk and / or potential magnitude of the impact).

The key focus of this study is on developing a quantitative approach that can draw independently on the results of modelling work of the impact of climate change policy on the energy system – i.e. to use this as a source of input data for the indicators of energy security. Given the complexity of outcome-based approaches the focus of our approach is therefore upon the development of the most relevant and applicable vulnerability indicators.

By using the climate policy modelling results under a baseline scenario (i.e. without the effect of the policy) and a with policy scenario, the difference between the evolution of the energy security indicator in each case can be used to determine whether, and to what extent, the policy has increased or decreased the ‘vulnerability of the EU’ to previously identified energy security risks. This approach is outlined in the illustration below.

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Illustration 5 - 4 Basic approach to assessing impact of climate policy on energy security

Baseline scenario

Time

Vulnerability to energy security issue - indicator X

+ve impact onenergy security from

policy

2020

Base year policy introduced

Pre-policy “vulnerability”

With climate policy scenario scenario A

2030

-ve impact onenergy security

from policy

With climate policy scenario scenario B

Historic trend

The following sections outline the quantitative approach developed to assess the effect of climate policy on energy security impacts. This focuses on the key linkages between climate policy and energy security as examined in section 3 and drawing on the most useful elements of existing indicators.

5.2 Principles for indicator design

The overall approach is based on a number of underlying principles, drawn from the evaluation criteria used for existing indicators (in section 4.1.1):

5.2.1 Suitability

Many of the existing vulnerability indicators are not particularly suitable in terms of assessing the key root causes of energy insecurity identified in our framework in section 2. To improve this, separate indicators have been developed to examine each of the key categories and types of energy insecurity outlined in

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Table 2 - 2. This means that the components of the indicator must be suitable, targeted proxies for each individual energy security issue.

In an ideal world we would use an outcome-based measure of energy insecurity (e.g. in terms of physical unavailability as the impact on welfare is too uncertain), however, this is not possible given the complexity of the approach. We therefore need to develop a vulnerability-based indicator that provides the best proxy for the likely magnitude of the final impact on welfare. To do this it is therefore important to try and incorporate components into the indicator, which are suitable proxies for the various stages of the causal mechanisms as outlined in section 4.1.

For example, Stage III is primarily concerned with the flexibility of the rest of the supply-chain to cope with disruptions (price or physical unavailability at Stage I and II). Where there is significant flexibility the final welfare impact will be lower than the situation where there is less flexibility. Climate policy may impact on this stage of the energy supply chain so without a suitable proxy, the overall measure of vulnerability may be misleading in terms of the likely impact on welfare.

Similarly at Stage IV, the final impact on welfare will depend on the role of a particular energy source on the demand-side. Where the overall use of the energy source is lower, where there is greater ability for demand to respond or where there is greater possibility for substitution of energy sources, then the final welfare impact will be lower than when the reverse is true. Climate policy has a particularly significant effect at the demand side due to changes in efficiency and fuel switching.

5.2.2 Transparency

The overarching aim of the approach is to be as transparent as possible and there are two specific issues within this that need to be considered:

• Complexity. In general, the greater the complexity of the indicator the lower the transparency. However, there is a potential trade-off between the suitability of the indicator and its complexity. The goal has been to find the simplest indicator that is still a sufficiently good proxy for the energy security issue it is targeting – and always support the analysis of its quantitative evolution with combined qualitative analysis.

• Objectivity. The aim has been to remove, as far as possible, the need for subjective judgments and values within the design, input data and application of the indicator. Whilst we aim to do this throughout, there are three main areas that should be highlighted.

Weighting of components at each stage within an indicator

As mentioned above an, indicator with components at each stage of the causal mechanisms is likely to be a more suitable measure of the final impact on welfare. However, the combination of the different components at each stage (particularly III and IV as I/II are necessary to relate the indicator to a specific route cause) may introduce implicit or explicit weightings in terms of their relative importance on the indicator’s final value.

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The aim is therefore to try to design the interaction between the indicator’s components such that the implicit weighting and impact on the final value of the indicator is on the basis of a directly meaningful relationship. For example:

• A simple indicator at Stage IV may be the share of the energy source in total energy consumption (as a proxy for its importance in the economy). As this share decreases it is a reasonable simplifying assumption that the impact on welfare from any cause of energy insecurity will decrease in a similar manner – hence multiplying this share directly to the preceding components of the indicator can be considered meaningful.

• At Stage III a useful proxy for the flexibility of the energy system to cope with a potential shortfall in energy supply, estimated at Stage I/II, is the level of storage. In this case dividing the potential shortfall (e.g. as estimated by an energy margin) by the storage capacity converts this into a measure of the time period over which the critical stocks of fuel can cope with a physical disruption (under the overarching assumption that, in this case, physical unavailability is primarily a short-term effect). Again, there is a directly meaningful relationship between the stages.

It should be noted, that the combination of components within the indicator, particularly at Stage IV (to measure the vulnerability at the demand side) may move the final value of the indicator away from a directly meaningful physical value. For example, a daily supply shortfall measured in ktoe at an earlier Stage combined with the share of the fuel in total energy consumption as a % at Stage IV. The final value is effectively ktoe scaled by the %, and hence is no longer a direct measure of the possible shortfall – however, the overall aim is to provide a proxy of the overall welfare vulnerability. Without this, the effect of an increasing reliance on the fuel within the economy would not be captured when comparing two policy situations. It is, of course, still possible to compare individual stages of the indicator by themselves.

Aggregating of indicators across energy types

In general, most indicators are examined for each primary fuel (e.g. gas, oil and coal) separately and then aggregated on the basis of the share of each fuel in total primary consumption – to form a single indicator (this takes place at Stage IV). However, it is important to note that this implicitly assumes equivalence across the different types of energy source. Alternative approaches to aggregation may be more appropriate in some cases – for example, aggregating by expenditure on energy as mentioned in section Appendix B - B 1.2.

Another issue is aggregating an indicator for electricity with those focused on primary fuels. Whilst this would in theory be possible (e.g. by looking at the efficiency of transformation) in general we propose to keep these indicators separate.

Stage IV proxies are discussed further in section 5.3.3.

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Aggregating of separate indicators

The final, and potentially most important transparency issue, is a question of whether to aggregate across multiple indicators, in particular across those targeting different root causes, to create a single overarching ‘score’ for energy security.

If it were possible to create outcome-based indicators of expected energy unserved (or ideally welfare impacts) this would be less problematic, as there is a consistent metric to aggregate the impact of different types of energy insecurity.

However, with vulnerability measures, a subjective judgment is still needed regarding which are the most important - be it increasing exposure to resource concentration issues or the potential for load balancing failures. This is unavoidable, but could in theory be addressed by weighting the outputs of the different indicators.

We decline to do that in this approach as its primary purpose is to provide a more comprehensive and objective set of information upon which policy makers can then make decisions. As such, further aggregation can mask the underlying trends in each indicator. The results of the indicators can be presented together in terms of a normalized spider diagram.

Illustration 5 - 5 Example of normalised spider diagram for 5 indicators

5.2.3 Availability of data and ability to forecast

The feasibility of the proposed approach will then hinge on the availability of input data for the indicators and the ability to forecast such data with sufficient accuracy – as the main area of concern is the impact on energy security moving to 2020 and 2030.

However, as noted in section 1.2, a key objective within this project is “to establish a methodology that would serve as a base ground for further analysis of impacts of climate change policies on energy security issues”.

This means that the approach developed is not directly limited by the current availability of data (or at least the ability to access it within the timescales of this project). There is therefore a balance to be struck between the robustness of the overall approach and the ability to practically implement it, both now and in the

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future, given that new data or modelling work can in theory be undertaken to provide the necessary data.

As part of developing the approach in this section we have considered possible data sources directly; for example, based on both the existing application of energy security indicators and the outputs from the range of EU-funded models used specifically to assess the impact of climate policy on the energy system (see Appendix C for further details).

Consideration of data availability currently falls into two main categories:

• Availability of outputs from the models (in particular PRIMES) that have been used to assess the impact of climate policy on the energy system, as this is the key linkage between these policies and their impacts on energy security – (in particular the impact on overall energy balances).

• Data that does not form part of these models directly (either in terms of underlying assumptions or outputs), but which is needed to provide a more suitable understanding of the impact on energy security.

- For example, these models are generally bounded at the border of the EU and give estimates of the net demand for imported fuels such as gas. However, to more accurately assess the vulnerability to energy security impacts there is also a need to understand how and where this energy is imported from (e.g. a single pipeline, multiple pipelines and/or LNG). Without this you are left with a simple indicator of net import dependence, which as argued in section Appendix B - B.1.3.1 is not a particularly suitable indicator to measure the vulnerability of the EU to specific root causes of energy insecurity. In this case, an increased dependence on imported gas will matter less if the flexibility of the import options also improves.

Existing models could be extended to incorporate the issues in the second category directly, but this is not within the scope of this project. We have sought information from separate third party sources, where available, so that quantitative analysis of existing policies can be undertaken.

A key issue is then the consistency of the baseline and ‘with climate policy’ scenario results with any third party data that is incorporated. However, in the majority of cases it is possible to make the simplifying assumption that climate policy would have limited impact (e.g. on the development of the high pressure gas transmission network), at least in the short-medium term, and so its evolution would be the same in both scenarios.

In light of the above discussion, the data requirements for a number of components in the currently proposed indicators (in sections 5.4 to 5.7 below) sit at what may be considered a slightly ‘idealised’, but not impractical level.

In the subsequent analysis in section 6, it has not been possible to implement all elements of the indicators due to limited data availability (or at least access to data

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within the project timescales). Where possible illustrative values have been incorporated, which can be updated in future once better data is available.

5.3 Overarching issues in application of the indicators

In addition to the principles guiding the development of our proposed quantitative approach there is also a number of practical issues that must be considered in the application of the indicators.

5.3.1 Assessment of different energy sources

As outlined in the supply chain analysis in section 2.6, we are concerned with energy security issues related to primary fuels (oil, gas, coal, uranium and biofuels/biomass) and electricity. However, the number and importance of these issues is higher for some energy sources than for others (e.g. gas versus uranium). In some cases, as is argued in the sections below, it is not considered necessary to examine the indicator for all energy sources.

5.3.2 Member State regional groupings and network issues

A goal of the spreadsheet tool developed under this project was to be able to examine the impact of energy security issues at an overall EU-level as well as at a Member State level. Most of the models used in the assessment of the impacts of climate policy on the energy system provide a breakdown of energy balance data at the MS level.

However, in some cases it makes little sense to look at individual Member States in isolation, as their vulnerability to energy security impacts may depend directly on the situation in neighbouring Member States. This is particularly true in relation to gas and electricity networks (and to a lesser extent the distribution of refinery infrastructure), which are substantially integrated.

A high-level overview23 of these networks in the EU-27 leads to the following groupings in Table 5 - 14.

23 Note that for specific infrastructure issues these groupings will vary.

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Table 5 - 14 High-level integration of gas, electricity and refinery infrastructure in the EU

Grouping Heavily integrated systems 1 Ireland UK

2 Spain (peninsula) Portugal

3 Belgium Netherlands Luxembourg

4 Sweden Finland Denmark

5 Estonia Latvia Lithuania

6 Italy Slovenia

Moderately integrated systems 7 Germany Czech Republic Slovakia Poland

8 Greece (mainland) Bulgaria* Romania

Low level of integration 9 Austria

10 Hungary

11 France

Small islands** 12 Cyprus

13 Malta

Note: *including Serbia. ** includes Balearics and Aegean

Representation of energy networks

Whilst models such as PRIMES provide a geographic breakdown of energy balance information at the MS-level they do not contain information on the structure or geographical breakdown of key energy networks. As discussed in section 5.2.3, an understanding of such networks (e.g. in relation to flexibility of gas imports) is necessary to provide a better understanding of energy security impacts.

It is therefore necessary for some indicators (such as other extreme events in section 5.4.2 and resource concentration in section 5.7.4) to incorporate a ‘simplified’ representation of existing networks within the spreadsheet tool - in relation to each of the geographical regions above. For example, for the gas network (as shown in the illustration above24) it would be necessary to identify:

• Key import routes into the EU, their maximum capacity, region of initial import and (country of origin for pipeline).

• Key intra-EU pipeline transit routes, their maximum capacity and the regions they are connected to.

• Maximum draw down rates of gas in regions (e.g. in response to an extreme event shutting off a major importing pipeline regions closer to the import point would effectively have priority of gas use).

24 For a detailed map of the existing EU electricity network produced by the Union for the Coordination of

Transmission of Electricity (UTCE – now part of the ENTSOE) see:

http://www.tennet.org/english/images/UCTE-netkaart_tcm43-17471.pdf

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It should be noted that the network representation focuses on physical issues only, and not intra-EU contractual issues that may determine the flow of gas inside the EU network.

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Illustration 5 - 6 Overview of gas infrastructure in Europe (IEA, 2007)

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5.3.3 Stage IV proxies

As discussed in section 2.2.3 the level and structure of demand for an energy source will affect the magnitude of the final energy security impact. Given the significant effect of climate policy at the demand side on individual energy types (particularly in terms of efficiency improvements and fuel switching) capturing this within an indicator is important to gauge the overall vulnerability to energy security. For example, an increase in vulnerability to an energy security root cause will be offset to some extent if the importance of that fuel within the economy also declines.

The Stage IV component of the indicator should ideally try to capture the overall importance of the energy source in economy and potential options to mitigate against a supply disruption, such as the:

• Ability for short/longer term demand side participation (of the same energy source).

• Ability for short/long-term substitution (to an alternative energy source25).

The review of existing indicators in section 4 has highlighted a number of possible approaches, each with their own limitations as discussed:

• Share of an energy type in total consumption

• Energy intensity (potentially against a benchmark)

• Expenditure on energy

• Price elasticity of demand

• Diversity within the use of energy within a country.

Given the importance of the link to climate policies, the models assessing their effect on the energy system need to have sufficient data and levels of disaggregation to implement the Stage IV approach. As a default the majority of indicators proposed in the sections below currently use a simple share of energy in total primary or final consumption.

Of the list above, we have discounted:

• Price elasticity, as it is not possible to forecast this properly over time (most models incorporate these as fixed exogenous assumptions).

• Diversity within energy use, as there is no direct link to the potential for substitution between sources and so the metric adds little to a simple share in total consumption.

Alternative approaches

Two possible alternatives to improving the application of the Stage IV component include:

• Minimum demand for energy. Rather than looking at a simple share of consumption of energy in primary or final energy consumption, the energy

25 The application of storage for the same energy type is considered as part of the flexibility of the

remainder of the supply chain at Stage III.

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consumed could be adjusted to reflect substitution possibilities. This would then give a minimum share of energy (assuming all substitution is utilised) in total energy consumption. A key area where this could be applied is in relation to heat and power generation where dual / multi-firing capability exists. This is discussed further within 5.7.3.4, although, data is generally not sufficiently disaggregated in most model approaches (i.e. net capacity by fuel type is presented but not their dual / multi-firing capability).

• Adjusted share of primary fuel in final energy consumption. Ideally we would like to understand the importance of different energy types in delivering energy services within the economy. As a proxy, the share of consumption of a an energy type in total primary energy is the furthest removed, as it does not account for intermediate energy carriers such as electricity and heat, which more directly deliver such energy services. A share of energy consumption in final energy is a closer proxy, as it does account for these carriers, although not the final services themselves. However, for many of the root causes of energy insecurity we are concerned with an impact on primary energy supply. To reflect this as a share in final energy it is necessary to calculate the proportion of generation applicable to the primary fuel (e.g. the share of electricity and heat from coal). The efficiency with which primary energy is transformed to heat and power is also important as, all else being equal, a system with more efficient transformation will be less vulnerable to a disruption in primary energy than one with lower efficiency (i.e. as it uses less primary energy input per unit of useful output). It is particularly important to capture this given the significant impact of climate policy on heat and power generation efficiency. This is incorporated by calculating the efficiency of transformation of the given primary fuel and comparing it relative (i.e. benchmarking it) to the average efficiency of transformation. Where the efficiency of transformation from the fuel is lower than the average of the system this scales up the adjusted share of the fuel in FEC reflecting that the system is correspondingly more vulnerable to a disruption to the primary fuel

- Adjusted share of primary fuel in FEC

=FECTotal

EffEffEHCDFEC

all

fuelfuelfuel ⎟

⎠⎞

⎜⎝⎛ + *

- Where:

- DFECfuel = Direct Final Energy Consumption of fuel

- EHCfuel = Electricity and heat generated by fuel*

- Total FEC = Total Final Energy Consumption

- Effall = efficiency of all heat and power generation (including CHP and district heating) = total inputs to thermal plant (including nuclear) / sum of total heat and electricity generation*

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- Efffuel = efficiency of heat and power generation from fuel (including CHP and district heating) = total fuel input to thermal plant / sum of electricity and heat generation from fuel*

- * = net of energy branch consumption and transmission and distribution losses (these are apportioned across each fuel depending on its share in net generation).

5.4 Extreme Events

5.4.1 Extreme Weather

The EU’s primary energy sources – Russia and the CIS states, the MENA region and internal production – are relatively unaffected by extreme weather events. Cyclones, hurricanes and typhoons are active in the Pacific and the western Atlantic Oceans. Similarly, catastrophic tornadoes and earthquakes are rare events within the EU and its primary suppliers. Extreme weather events can still affect the global energy system even at a distance, as evidenced by a global rise in gas prices following Hurricane Katrina’s disruption of US gas production. However, price increases do not generally have as significant an effect as supply disruptions.

In the short to medium term, energy is highly price inelastic in the EU region. Principal reasons include a lack of alternatives (especially for gas, electricity, diesel and gasoline) and the relative low cost of energy in proportion to incomes. The limited substitutability of energy results in consumers being unable to easily switch to lower cost alternatives. In addition, the relatively low proportion of income spent on energy means that the impact of any price shocks can be absorbed, (though there will be distributional considerations for low-income consumers26).

Given the likely temporary nature of price shocks resulting from global extreme weather events, we consider that the severity of this impact on energy security would therefore be limited. Physical unavailability of fuel due to extreme weather is therefore considered a much greater threat to energy security.

5.4.1.1 Key linkages with climate policy

As discussed in section 3, climate change policy is likely to change the energy supply balance in favour of low-carbon sources as well as drive efficiency improvements. As the amount of fossil fuels being processed and imported reduces, the impact of extreme weather events both inside and outside of the EU diminishes. However, with less demand, there may be less flexibility, meaning that an extreme event could have more serious knock-on effects throughout the supply chain.

Climate change policy which favours renewable energy sources will have a mixed impact on intra-EU energy security. Some renewable energy technologies cannot cope with extreme weather. For instance, solar production would drop off in heavy storms and wind turbines would need to be shut down in heavy gusting windstorms. However,

26 Which could be addressed by targeted Government support schemes.

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as technology improves, there will be fewer reductions in output from renewables. With the expansion of off-shore wind farms, turbines are now being engineered to sustain high gusts. More importantly, new energy systems are designed to accommodate the variable production of renewable sources.

Large-scale growing of biofuels could also be affected by extreme weather, especially if the EU continues to import large quantities of palm oil (coupled with a shift in its share of primary use away from food products towards transport and power generation) from Asian countries susceptible to drought and typhoons. But, the wide geographical distribution of biofuel production (e.g. Indonesia, Malaysia and Brazil) means that individual extreme events (weather and otherwise) are likely to have limited impact.

Climate change policy may either increase or decrease reliance on the grid. If the policy encourages large-scale renewable energy installations or encourages electricity-powered transport, the grid will become more critical, increasing the potential for an extreme weather event to have strong knock-on effects throughout the energy system. If a policy encourages home micro-generation and distributed generation, then the grid becomes less vulnerable, reducing energy security risk.

As discussed in section 2.3 impacts from extreme weather fall into two main categories:

• Demand-side impacts due to extreme cold or hot periods.

• Supply-side impacts due to individual extreme events (e.g. storms)

It would be impossible to identify a single measure to cover all potential extreme weather events that might impact on the supply-side for energy security. However, the indicators proposed in section 5.4.2 (other extreme events) can be seen as a reasonable proxy for this – for example the impact of an extreme event (weather or otherwise) knocking out a key supply route.

The remaining key weather event risk for almost all of the European Union energy markets is for prolonged periods of unusually cold weather causing an unexpected spike in demand for heating – as opposed to the impact of extreme weather on energy supply. This spike in demand, if felt across multiple member states, could result in a physical shortage of energy. The key linkage with climate policy is therefore the impact on the overall peak demand for such fuels as driven by efficiency improvements and fuel switching.

Similarly, for a few southern European markets (e.g. Greece) a demand spike from extreme hot weather will be felt in the summer months as a result of increased cooling demand. Similarly, for a few southern European markets (e.g. Greece) a demand spike from extreme hot weather will be felt in the summer months as a result of increased cooling demand. This potential scenario becomes more likely as both air-conditioning use and average summer temperatures increase.

An additional issue in this case is that extended hot periods may reduce cooling water availability for thermal plants, and hence indirectly affect the supply side at the same time. As this is such a localised issue with negligible impacts on a country-wide

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system, the potential impact has not been quantified. However, the loss of a key plant is addressed in Section 5.4.2.

Demand spikes caused by extreme weather will put pressure on all energy sources required for heating and cooling. In most EU countries, space heating is gas- or oil-fired or electric. The indicators for extreme weather will therefore focus on these three energy sources. Principally due to environmental and efficiency concerns, coal use in space heating is now very rare in Western Europe and is quickly declining in Eastern Europe. As this is a forward-looking study, coal will not be considered in the indicator calculations.

5.4.1.2 Suitability/use of existing indicators

None of the existing indicators identified in section 4 are directly relevant in the targeted assessment of extreme weather events on energy supply. However, the key issue being examined here is the impact of extreme weather on peak energy demand and the potential disparity it causes relative to available supply. In this case the most relevant indicators are those which provide some estimate of the energy or capacity margin, see section Appendix B - B 2.1.

Furthermore, as the emphasis is on the short-run impact of extreme weather, indicators that highlight the flexibility of the system to adapt to meet this increased demand are particularly relevant – such as storage capacity and critical stocks of fuels in section Appendix B - B.1.1.1. These generally score well for suitability / transparently and moderately for data availability / ability to forecast.

5.4.1.3 Specification of indicator(s) at Stage I

From section 2.3.1 Stage I is, in this case, defined as a random extreme weather leading to a significant increase in energy demand.

It is assumed that climate policy will have no direct impact on the likelihood of extreme weather events. However, the estimates of 1 in 10 and 1 in 50 weather events provided by the meteorologists will take account of future changes in weather patterns including the impact of climate change.

The assessment of the CC policy impact on ES will therefore be scenario-based using a 1 in 10 and/or 1 in 50 year cold (hot) weather event.

5.4.1.4 Specification of indicator(s) at Stage II

This stage is defined as, in the case of extreme weather and demand, the impact on peak demand and any potential supply shortfall. Over 90% of heating needs in Europe are met by three energy sources: oil, gas and electricity. As such, this section will focus on these sources. To quantify the potential daily/hourly peak demand spikes, existing load curves will be applied to the annual demand forecasts from PRIMES. It is assumed that apart from space heating and electricity, all other end-use demands for energy are unaffected by the extreme weather.

Hourly load curves will be used for electricity, while daily loads will be used for gas and oil products. For the base index, an average load curve will be used, while a 1 in

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10 or 1 in 50 year peak load curve will be used for the extreme cold weather events. Where available, estimated load curves are used (e.g. from MS grid operating companies). When not readily available, the load curves are estimated from historic figures or load curves from neighbouring countries.

To capture the ES vulnerability from extreme weather events, two indicators need to be considered at Stage II. The first deals with oil and gas through the evaluation of the shortfall in available supply during peak demand periods, while the second concerns the effect on the de-rated peak capacity margin for electricity given the rise in peak demand.

Daily Peak Supply Shortfall (DPSS) for oil or gas: Average daily available supply for oil and gas from regular imports and domestic production. Ideally, this should be average daily supply during the peak demand season, as some countries would regularly schedule more fuel imports during their peak season, temporarily reduce exports or expand production.

Since peak season production data is unlikely to be available for all EU countries and particularly in terms of future projections, average daily production can be used as a proxy. This is considered a reasonable approximation given that an extreme event is likely to be random or unpredictable, giving Member States limited ability to respond beyond drawing on storage capacity.

This quantity would be expressed as Daily Peak Supply Shortfall:

• DPSS = Peak daily demand for fuel (accounting for extreme weather) - daily supply (Production + Net Imports) for fuel

Where:

• DPSS = Average daily peak supply shortfall excluding, excluding storage

• Net Imports = Average daily imports minus average daily exports

De-rated electricity peak capacity margin: Similarly for electricity the gap between available supply and peak demand will narrow as demand increases, hence de-rated power peak capacity margin (see section Appendix B - B.2.1.2) is the relevant indicator. The capacity estimate needs to be de-rated for both seasonal variations in renewables and historical forced outages. This will be consistent with the de-rated peak capacity margin indicator used to assess vulnerability to load balancing failure impacts in section 5.6.

The capacity margin is then calculated as the unused available capacity (after de-rating) at peak demand, expressed as a percentage of total peak demand:

• PeakDemandPeakDemandDCapacityfuel

/)(∑ −

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Where:

• DCapacity = De-rated capacity (MW)

• PeakDemand = Peak electricity demand (MW during the peak hour)

5.4.1.5 Specification of indicator(s) at Stage III

Stage III is defined as the severity of knock-on effects [which] depends on the flexibility of supply, storage and transport infrastructures. The extreme cold weather scenario considered here will have a direct impact on demand; but direct impacts elsewhere along the supply chain are assumed to be minimal or nonexistent. However, the increased demand will have knock-on effects on the supply chain, where capacity is insufficient or not flexible enough to respond to the increased demand.

An extreme cold weather event will result in a sharp short-run increase in demand for all fuels used in heating (including electricity). Indicators then need to assess the short-term available supply against short-term demand spikes. An extreme heat event will cause a direct spike in electricity demand. As a result, only the second indicators would be used (de-rated peak capacity margin).

To capture the ES vulnerability from extreme weather events at this stage, the Stage II indicators can be adjusted further. This adjustment will vary depending on the energy type:

• For gas the DPSS can be further evaluated in terms of the Short Run (SR) availability of storage (in terms of number of days) to cover any temporary supply shortfall.

• For oil, physical storage capacity is not a limiting factor in the same manner as it is for gas. It is therefore more meaningful to look at the required critical stocks of oil that would need to be kept to cover a benchmark period of time. For example IEA members are required to maintain stocks covering 90 days worth of average consumption – although in this case we are looking at the stock required to provide X days of coverage of the DPSS.

• For electricity, the de-rated peak capacity margin can also be adjusted further as a shortfall in primary supply will have knock-on implications for their availability for electricity generation.

SR availability (gas): this is the number of days availability that could be provided by existing storage given the scale of the shortfall. Aggregate SR availability will be calculated using each fuel’s respective percentage of total primary energy consumption.

SR availability is then calculated as follows:

• If DPSS ≤ 0 then SR Availability = 365

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• else, SR Availability27 (days) = Storage / DPSS

Where:

• Storage = Total storage capacity

SR availability (oil): this is the total stock of oil required to cover a benchmark number of days supply of the DPSS.

• DPSS * Benchmark days required

De-rated Electricity Peak Capacity Margin (further de-rating for primary fuel shortage): A further de-rating may be required for thermal power plants in countries where the same input fuels (primarily gas) are also used for space heating and supplies may not be sufficient for both power and heating markets during extreme cold weather events.

For safety reasons, space heating and cooking (tertiary, low pressure gas system) have priority in gas use. Gas pipelines have minimum pressure requirements. In tertiary systems, the network operator is responsible for maintaining gas pressure. Power plants and industrial systems have more flexibility and storage to maintain pressure and are therefore the first to lose gas.

To calculate accurately the additional de-rating, a detailed energy and electricity model for each market would be required. In the absence of such models for the 27 Member States, a ‘rule of thumb’ will be estimated to transpose short run availability of each fuel also used in space heating into an additional de-rating (loss of capacity) for power generation using that fuel.

In calculating the additional de-rating, it is assumed that the system operator has a target number of days’ coverage for each fuel, reflecting the expected duration of an extreme cold weather period. As a default this is assumed to be 10 days.

If the short run availability exceeds 10 days, then there will be no additional de-rating of the power capacity from that particular fuel. Where the SR availability is less than 10 days, the volume of fuel required to meet the target days coverage is then calculated as follows:

• If SRA ≥ MD then FD = 0

• Else FD = (DPSS x MD) - Storage

• Where:

- SRA = SR availability in days

27 For gas, the storage capacity needs to account for the maximum daily withdrawal rate when determining

available days of coverage. Where the DPSS > the maximum withdrawal rate the indicator is set to zero.

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- MD = Minimum number of days cover required, set to 10 as default

- FD = Additional fuel needed to meet minimum number of days cover

- DPSS = Daily Peak Supply Shortfall excluding storage

For each fuel used in space heating and power generation, the additional de-rating is calculated as follows:

• AD = [ ((FD / MD) / FinP) / 2 ] * CAP

• Where:

- AD = Additional de-rating

- FinP = Fuel use in power

- CAP = MW of electricity capacity relevant to the fuel with limited availability.

In the formulae FD is divided by MD to put the fuel requirement back in terms of volume per day. It is then divided by FinP, this is the average daily volume of that particular fuel used in power generation during an average peak demand period. This allows us to identify the approximate percentage drop in power production.

However, as the focus of the de-rating is on the ability to meet daily peak demand it is also important to differentiate between base-load plants, consuming fuel over the full 24-hour period, and mid- or peak-merit power production, which will only be running for a fraction of the day to meet peak demand. For example, where a peaking plant runs for 12 hours over the daily period to meet peak demand the loss of capacity due to the same unavailability of fuel (assuming equivalent efficiency) will be only half that of the base load plant.

As a first order approximation, we have used a factor of 2 to reflect peaking plant operating for half a day (for a base load plant the adjustment would be 1)28. This also reflects the fact that the fuels most likely to be in short demand are for peaking plants. Theoretically, this factor can be refined by looking at the daily-load curves for peaking plant operating over a cold weather period, if these were universally available for each member state.

The calculation for the de-rated peak capacity margin is then:

• PeakDemandPeakDemandADDCapacityfuel

/)(∑ −−

28 In the extreme case of a peaking plant operating for only a single half-hour period in one day the

maximum factor would then be 48.

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5.4.1.6 Specification of indicator(s) at Stage IV

Welfare impacts cannot be measured directly. However, some indicators can highlight the vulnerability of the demand-side. If a fuel does not play a significant role in a country’s energy mix, then the energy security impacts on welfare will likely be low. The following indicator points out the relative importance of primary fuels:

• Energy Dependency of fuel X = 1 - ∑fuelallTPESfuelXTPES /)(

Where:

• TPES = total primary energy supply in ktoe

• Using the complement of the share in primary energy, rather than the share itself, is necessary as an increase in the indicator at Stage III shows a decrease in vulnerability of the system and the complement of the share in primary energy would then move in the same direction.

For electricity, a simple metric showing the complement of the share of electricity within final energy consumption is proposed to illustrate the importance of electricity within the economy, as per the use of de-rated peak capacity margin in section 5.6.5.

5.4.1.7 Overall indicator

As the principle indicators are measured in different units it is not possible to aggregate them directly. As a result there will be three indicators for this section.

• Overall SR availability of primary fuel (gas) = SR availability in days * energy dependency

• Overall SR availability of primary fuel (oil) = SR availability in storage capacity required to maintain X days of coverage (in ktoe) * energy dependency

• (Further) De-rated Peak Electricity Capacity Margin (further de-rated for shortage): this is calculated as above (and multiplied by the complement of the share of electricity in final energy consumption). As the peak power capacity margin covers all electricity sources, it does not need to be aggregated.

5.4.1.8 Issues in application of the indicator(s) at EU and Member State/regional level

It is assumed that an extreme weather event would impact on more than one MS, limiting the ability of short-term shortages to be met by energy supplies from neighbouring countries.

For the purposes of simplification, it is assumed that a cold weather event will affect the entire EU region at the same time. In the case of cross-border trade, the there is an assumption that the MS/region closest to the source of supply has priority – i.e.

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each MS acts in its own self-interests. Similarly, any storage will be utilised first in its own MS.

However, as the weather event is unexpected, it is assumed that regular peak-period imports/exports will continue due to contractual obligations.

5.4.1.9 List of detailed data requirements

Data Possible sources

Short run availability (days supply) gas and oil

Average daily net imports PRIMES – future by MS

Eurostat – historic by MS

Average daily production PRIMES – future by MS


Storage capacity Gas Transmission Europe (by MS) - Historic

Average peak daily demand PRIMES – future by MS


Extreme cold weather parameters (load curves) MS System operators – historic by MS

Gross inland energy consumption (fuel and total) PRIMES – future by MS


Peak de-rated capacity margin

Extreme cold weather parameters (load curves) MS System operators – historic by MS

All other parameters as per section 5.6.9

5.4.2 Extreme events – Strikes, large-scale accidents and terrorist activities

These three types of extreme events have been grouped together as a result of the similarities among their effects on the energy system. All three events relate to a sudden, unexpected event, which will decrease physical supply of energy for an undetermined amount of time, but with the most significant impacts from the event assumed to occur over the short-term. They are defined as:

• Large-scale accidents - accidents which fall outside the scope of tolerance levels typically accounted for by industry

• Strikes – industrial action by workers or other forms of social unrest

• Terrorist activities – a direct attack affecting the physical supply of energy.

The first is similar to extreme weather in that it is a random event. The latter two are targeted events, but still are generally unexpected. These events can occur throughout the supply chain and welfare effects are largely dependent on the importance of the fuel within the system and to the country’s GDP.

The most significant impacts of these extreme events will be felt in the supply of gas, oil and electricity. Coal can be stockpiled relatively easily and due to the limited reliance on biofuels in energy production, relative ease of substitutability and multiple sources, there are no significant energy security impacts associated with biofuels and extreme events. Similarly, the impact of extreme events on uranium supply (given the

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higher energy density and extended refuelling period) are not considered significant), although an extreme event related to electricity generation from nuclear plants will be significant.

As mentioned in section 5.4.1.1, whilst the proposed indicators on extreme weather focused on its impacts on demand, the indicators below can be considered reasonable proxies for the impact of extreme weather on supply – i.e. where the extreme weather or other extreme event are likely to effect the same part of the energy supply chain (e.g. the electricity transmission network).

5.4.2.1 Key linkages with climate policy

As discussed in section 3.5 climate change policy will interact with energy security impacts from other extreme events in three main ways: demand reduction for energy will tend to reduce potential energy security impacts; fuel switching from coal to natural gas may expose it to increased risks particularly those related to imports into the EU; the shift to increased renewables may lead to lower impacts via greater use of distributed generation, but in some cases the expansion of transmission/distribution networks may lead to a slightly increased vulnerability.

5.4.2.2 Suitability/use of existing indicators

As per extreme weather the key issue here is the effect of an imbalance between supply and demand. For extreme weather this was represented by an increase in peak demand with supply unchanged, whereas in this case it is the potential decrease of supply (due to an extreme event) and its ability to meet peak demand.

The most relevant overall indicators are again those which provide some estimate of the energy or capacity margin, see section Appendix B - B 2.1. As per extreme weather, the emphasis is on the short-run impacts of the event so those indicators that highlight the flexibility of the system to adapt to meet this increased demand are again relevant – such as storage capacity and critical stocks of fuels.

The key gap in the existing indicators is some metric to assess the likelihood and magnitude of a supply shortfall due to different types and locations of extreme events.

5.4.2.3 Specification of indicator(s) at Stage I

There are no objective indicators available to predict the likelihood of any of these extreme events. From a subjective point of view, certain changes in the energy system (reduced industry revenues and resultant job losses) could increase the chances of strikes, accidents or terrorist activities, but there is no way to measure that potential objectively.

5.4.2.4 Specification of indicator(s) at Stage II

Stage II in this case is defined as the magnitude of the disruption caused to the sector(s) of the energy supply chain directly affected by the extreme event and the impact on the DPSS (Daily Peak Supply Shortfall) for oil and gas. The impact of the event will typically depend on the level of concentration of infrastructure.

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The impact of an extreme event will only be felt in a significant way if there is significant resource and infrastructure concentration. If terrorist activity disrupts a gas pipeline in a competitive, flexible market with multiple energy sources, the impact will be minimal. However, if a country relies on a single gas pipeline for 75% of its fuel for power generation, terrorist activity directed at that pipeline would have a significant impact on welfare. Relying on one pipeline, power plant or regasification terminal for a large portion of total energy demand increases the market’s vulnerability.

Climate change policy will have an effect on the level of concentration in the supply chain. For instance if a policy encourages switching to low-carbon fuels, there is likely to be a reduction in the amount of oil transported or the amount of power generated from large coal plants, resulting in increased flexibility and a reduced risk of large-scale disruption.

Any of the extreme events described – strikes, large-scale accidents and terrorist activities – could impact anywhere along the supply chain. As such, we need to measure the system’s vulnerability to disruption at different points along the supply chain. To do this, we have identified four possible scenarios:

• Reliance on a single supplier: The quantity supplied from the leading supplier/s (ktoe) expressed as a ratio of Gross Inland Consumption. Calculating this indicator would vary slightly with type of fuel/power. Similar to the risks associated with Resource Concentration, over-reliance on a single supplier could put the importing country in a vulnerable position to any significant social unrest, accidents or terrorist activity in the exporting country.

• Reliance on a single route: The quantity supplied over the most used route/s (measured in ktoe or GWh) expressed as a ratio of Gross Inland Consumption. Routes considered could include pipelines and high voltage transmission lines. Calculating this indicator would vary slightly with type of fuel/power. For electricity, this scenario will only be considered if there is an obvious concentration of high voltage transmission lines which represents a significant portion of the country’s electricity transmission.

• Reliance on a single plant: The quantity supplied by a lead plant/s (ktoe) expressed as a ratio of Gross Inland Consumption. This refers to primary production/refining for fuels and power generation for electricity. For gas, it could be an LNG regasification terminal. For oil, it could be a refinery or the principal import terminal if no refining is done in country.

• Reliance on largest nuclear facility: The quantity of power supplied by the largest nuclear power complex (GWh) expressed as a ratio of Gross Inland Consumption. If there are multiple reactors in the same location, they will be considered one nuclear facility, as an accident or terrorist attack on one is likely to also affect a neighbouring reactor. Reliance on nuclear power has been singled out as the potential effects of a large-scale accident or terrorist attack are so significant.

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The energy system is complex and highly integrated. If there is heavy reliance on an essential link in the chain, the system’s vulnerability to extreme event impacts increases.

Data availability

It should be noted that the majority of the data required to measure these four scenarios in future is not available from energy models such as PRIMES (due to its limited disaggregation and geographical representation of infrastructure), or any other single document/database/modelling platform. In addition, there are potential difficulties in forecasting some of these pieces of data. As a result, some of these future scenarios cannot currently be estimated. They have been included here to provide a framework for future development.

However, it is still possible to identify the current reliance on some of the most important elements. For example, the reliance on the largest gas import pipeline route into the EU or a region. Under the assumption that this is unlikely to change in future to 2020 or 203029, the link to climate policy is then made via the PRIMES model results (or other models with future energy balance data) by seeing how the significance of this reliance changes in future – e.g. under a shift to greater demand for gas. Similarly, it should be possible to identify the largest single electricity plants currently in operation30 and make an assumption about whether they would still be in operation in 2020 or 2030 (e.g. based on their construction date and standard operating lifetime). Under a climate policy promoting construction of new renewables, it is again reasonable to assume that the capacity of any single new generating plant31 will not exceed that of the largest existing fossil, large-hydro or nuclear facility.

As per extreme weather in section 5.4.1.4, the same indicators will be used at Stage II, adjusted for the loss of the largest supplier / route / plant.

Daily Peak Supply Shortfall (DPSS) for oil or gas:

• DPSS = Peak daily demand for fuel (accounting for extreme weather) – daily supply (Production + Net Imports - LSSPR) for fuel

Where:

• DPSS = Average daily peak supply shortfall excluding, excluding storage

• LSSPR = supply from largest supplier, plant or route (excluding storage)

• Net Imports = Average daily imports minus average daily exports

De-rated power peak capacity margin (adjusted for loss of plant):

29 Or alternatively, where there are planned/proposed new routes it would be possible to examine reliance

under both the assumption that these are / are not constructed. 30 For example, Drax (4 GW coal and co-fired biomass) in the UK 31 Or collection of plant in the case of large wind-farms

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• PeakDemandPeakDemandLoPDCapacityfuel

/)(∑ −−

Where:



• LoP = Loss of Plant capacity (MW) due to shut down of largest individual plant, loss of transmission line connecting plant(s), etc.

5.4.2.5 Specification of indicator(s) at Stage III

To determine whether there will be any significant knock-on effects in Stage III, we need to examine the system’s flexibility. The focus will be on its ability to cope with a shortfall in supply, in particular short-term substitutability of supply options.

As with extreme weather events, the same adjustments to the Stage II indicators (accounting for the loss of the largest X) are incorporated.

SR availability (gas): this is the number of days availability that could be provided by existing storage given the scale of the shortfall. Aggregate SR availability will be calculated using each fuel’s respective percentage of total primary energy consumption.

SR availability is then calculated as follows:

• If DPSS ≤ 0 then SR Availability = 365

• else, SR Availability32 = NetStorage / DPSS

Where:

• NetStorage = Total storage capacity net of largest supplier if this is storage

SR availability (oil): this is the total stock of oil required to cover a benchmark number of days supply of the DPSS.

• DPSS * Benchmark days required

(Further) De-rated Electricity peak capacity margin (adjusted for both loss of plant and further de-rating for primary fuel shortage): as per extreme weather the loss of primary fuel supply may further limit that available for power generation and so the calculation is adjusted for an Additional De-rating (AD) factor (see section 5.4.1.5 for calculation):

32 For gas, the storage capacity needs to account for the maximum daily withdrawal rate when determining

available days of coverage. Where the DPSS > the maximum withdrawal rate the indicator is set to zero.

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• PeakDemandPeakDemandADLoPDCapacityfuel

/)(∑ −−−

Where:



• LoP = Loss of Plant capacity (MW) due to shut down of largest individual plant, loss of transmission line connecting plant(s), etc.

• AD = additional de-rating.

5.4.2.6 Specification of indicator(s) at Stage IV

Welfare impacts cannot be measured directly. However, some indicators can highlight the vulnerability of the demand-side. If a fuel does not play a significant role in a country’s energy mix, then the energy security impacts on welfare will likely be low. The following indicator points out the relative importance of primary fuels:

• Energy Dependency of fuel X = 1 - ∑fuel

TPESfuelXTPES /)(

Where:

• TPES = total primary energy supply in ktoe

For electricity, a simple metric showing the complement of the share of electricity within final energy consumption is proposed to illustrate the importance of electricity within the economy, as per the use of de-rated peak capacity margin in section 5.6.5.


As the principle indicators are measured in different units (number of days’ coverage versus percentage capacity margin) it is not possible to aggregate them directly. The overall indicators (subject to the adjustments at Stage II / III) are the same as in section 5.4.1.7.

5.4.2.8 Issues in application of the indicator(s) at EU and Member State/regional level

In contrast to the extreme weather events, the impact of one of these extreme events would be more concentrated, likely confined to just one EU MS or regional market. As such, there will be more likelihood that any short-term shortages could be met by imports from neighbouring markets with regular surpluses. As these events will tend to be country / market-specific, aggregation for the EU is not likely to be less meaningful.

However, one exception may be major pipeline import routes as these may ultimately serve more than one MS or region and it may be necessary to examine how regions closer to the point of disruption draw down on gas potentially causing problems for subsequent regions as mentioned in section 5.3.2.

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5.4.2.9 Differences in application of indicator across energy types

There is some difference in applying the indicator across different energy types. For instance, the indicator “Reliance on a single route” applies only to energy types that have specific transport routes (e.g. pipelines, power lines, fuel terminals, etc.). Similarly, the “Reliance on a single plant” applies only to energy types for which there is some large-scale processing in country. This is not considered applicable to nuclear enrichment or biofuel production.

There is potential overlap between electricity production and the fuel sources for electricity production. However, the two indicators (SR availability for primary fuels) and peak de-rated capacity margin (for electricity) are kept separate.

5.4.2.10 List of detailed data requirements

In addition to the data requirements listed in the extreme weather events section 5.4.1.9 (excluding data specific to 1 in 10 / 1 in 50 extreme events and space heating consumption) information on the loss of daily supply from the largest single supplier route or plant at a Member State level would be needed. Historic data is generally available from MS system operators or overarching entities such as Gas Transmission Europe, however, projection data is not generally available from models such as PRIMES due to its level of aggregation.

5.5 Inadequate Market Structure – Insufficient investments in new capacity

Given the competitive nature of EU energy markets, investments on the supply side are in most cases dependent on decisions made on a commercial basis within the private sector33. There may be situations in which this could lead to underinvestment. For example, a high degree of regulatory or political uncertainty as to potential changes that could have a significant economic impact on investments could deter decisions. Alternatively, investors’ decisions may be influenced too strongly by a short term view of market developments, and as a result may not sufficiently reflect the impact of the potential for longer term price movements associated, for example, with resource depletion or tightening environmental policies.

5.5.1 Key linkages with climate policy

As discussed in section 3 there are a number of reasons why climate change policies may contribute to this investment uncertainty. This is particularly true for the electricity sector, where the biggest changes will be needed. This section therefore focuses primarily on power generation to provide a clear focus for the examples, before examining the application of the metrics to other areas.

Given the radical change in generation mix required to 2020, the overall scale of investment required by both private companies in competitive sectors (for example, in generation plant) and by regulated monopolies (for example, in expanding

33 This is not the case for investment in the transmission and distribution sectors, which are usually

governed by regulatory decisions.

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transmission networks) is substantial. There are a number of types of investment issue that may act to prevent sufficient investment being undertaken, which can broadly be summarised as:

• Uncertainty associated with policy interventions: Implementing climate change policies requires interventions in the market. This in itself can cause uncertainty that can impact the investment climate, where there is lack of clarity as to the specific implementation of policies, or where there is doubt or lack of credibility in the level of commitment to maintain those policies over the timeframe required to meet the stated objectives. The carbon price in the EU ETS is a good example of this. From an investor’s perspective, there is no guarantee that the market framework will remain through the economic lifetime of new plant, and the longer term price will in any event be heavily influenced by decisions made by Governments that will be subject to political pressures associated with rising energy prices (it should be noted that this uncertainty affects investments in both fossil and low-carbon supply).

• Uncertainty associated with the impact of a changing capacity mix on prices and revenues: Whilst some generation capacity, such as renewables, may be supported through subsidies, the future remuneration for conventional fossil fuel plant (which will still be required to provide flexibility and back-up) becomes more uncertain. This is due to the changing dynamics of price behaviour, with the potential for increasing volatility with higher penetration of intermittent low marginal cost plant, and lack of clarity with regard to the extent that other revenue streams (such as ancillary services payments) may be available for the provision of flexibility services.

• Increasing capital intensity of investment: The generation mix required to deliver the objectives associated with climate change policies – particularly increased levels of low carbon technologies such as renewables and nuclear - tends to be more capital intensive relative to the predominantly gas-fired investments since the liberalisation of the markets. By this, we mean that, of the total lifecycle costs, a greater proportion are associated with the capital costs involved in constructing the facility compared to annual running costs, and in particular fuel. This can make financing more difficult, as a greater proportion of funds are required upfront. It also can lead to a greater risk with regard to wholesale price uncertainty – as there is a direct exposure to variation in wholesale price levels. This contrasts with fossil fuel plant, where variable fuel costs are likely to correlate with electricity prices. On the other hand specific financial support mechanisms may offset this problem by providing more stable streams for particular forms of generation, and in some instances smaller units (for example, for renewable heat or electricity generation) may facilitate the investment process.

• Other barriers to investment: There can be other significant barriers to investment, such as national or local planning issues, delays with regard to grid connection and access rights, and public acceptance.

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5.5.2 Suitability/use of existing indicators

Four indicators were identified in Appendix B - B 1.4 relating to the investment climate, namely a “general business environment” metric, a measure of patents in the energy technology sector, market price signals and a ratio of investment to turnover. The first three of these were ranked low for relevance, and difficulties were identified with the fourth in data availability, and in linking historic measures to future needs. As a result, none represents a good metric in the context of climate change policy evaluation.

One key issue is that it is fundamentally hard to find a direct measure of the impact on future investments of new policies – as by definition the required investments are yet to happen. As a result, the metrics identified in this section are proxies that aim to indicate the potential scale and difficulty (i.e. the vulnerability) associated with the implied investment levels, rather than a direct measure of the potential lack of investment that may occur (i.e. an outcome based indicator).

5.5.3 Specification(s) of indicator at Stage I

For this root cause type, Stage I is, simply, too little investment in new capacity. This is something that only becomes apparent over time. Metrics used in policy assessments, therefore, must either be proxies that indicate some level of “difficulty” in the investment environment, or the modelled behaviour of investors within a given policy environment. Whilst the latter has been done, it is complicated and specific to the particular implementation of policies in each Member State, and is not a feature of models such as PRIMES that typically assume “rational” investment decisions taken with perfect foresight. Outputs from such models will therefore, by definition, not provide any direct indication of insufficient investment.

The complexity of this issue, and the lack of straightforward quantitative metrics, may mean that it is best addressed through qualitative analysis (or specific modelling), rather than attempting to derive messages through indirect proxies. There is a risk that this approach could be misleading if important aspects are missed.

We therefore focus here on simpler proxies. These could indicate vulnerability to the root cause based on the investment profile emerging from ‘rational’ models, and hence provide an indicator of the extent to which the required level of investments may in practice not materialise. We identify three potential ones:

• A metric of the overall investment in new capacity needed

• A metric of the capital intensity of investments needed

• A load factor measure (relevant for non-renewable investments)

We also consider a fourth metric that could be used to track investment following the implementation of a policy. This is more likely to be useful in a “monitoring” stage rather than an initial policy evaluation stage.

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5.5.3.1 Required new capacity

This would provide a simple metric of the impact of climate change policies on the overall level of investment in new capacity required in a particular sector. It is most likely to be useful as a cumulative metric over a given period (for example, through to 2020). It is derived from PRIMES (or other) modelling results, and would allow a Baseline scenario to be compared to the scenario incorporating the climate change policy (or policies) under analysis. We would propose measuring this both in GW terms and in €m of associated capital costs.

• ( ) ( )XYeartocapacitynewmodelledofSumcapacitynewRequired =

The RES scenarios will likely have higher levels of GW/total investment due to lower capacity factors, albeit offset to some extent by improved demand efficiency. The total investment volume measure does not account fore market imperfections, but assuming these exist, then we are more exposed if we require greater levels of investment (e.g. under a higher plant turnover assumption).

5.5.3.2 Capital intensity metric

The long run marginal cost of an investment is made up of a capital cost component (associated with the initial construction of the equipment) and an annual (fixed, variable and fuel) operational cost component that is incurred as the equipment is used. The higher the proportion of capital costs, the greater the demand on financing, with more money required upfront in the project. Other things being equal, then, the barrier to investment in these projects will be higher.

Key low carbon technologies, such as (non-biomass) renewables and nuclear, are examples of more capital intensive investments. Climate change policies overall are therefore likely to result in a more demanding investment regime.

A simple metric to indicate this would be to compare the proportion of cumulative capital costs to cumulative total costs under a Baseline scenario to a climate change policy scenario. It should be noted that this will be very dependent on the underlying assumptions of future capital costs of different technologies within the models used to produce these scenarios.

• ( ) ( ) ( )coststotalSumofcostscapitalSumofintensityCapital =

In practice, a significant proportion of the required new investments may be covered by financial support mechanisms that guarantee a proportion of revenues for a given investment (such as feed-in tariffs for renewable generation). Where these are available, this will facilitate financing. To reflect this, a subsidy-adjusted version of the Capital intensity ratio could in theory be used:

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• ( ) ( ) ( )costsTotalsubsidiescostsCapitalintensitycapitaladjustedSubsidy −=

The subsidy measure here would need to be gross subsidies (for example, a full feed-in tariff rather than the difference between a feed-in tariff and wholesale power prices) and the assumption is that these provide a firm revenue that can be used to secure financing. In practice, types of subsidies will vary and the degree of revenue certainty will correspondingly differ, a factor which would not be captured within the metric.

However, it is unlikely to be possible to apply this using data from the PRIMES modelling approach. For example, as outlined in E3MLab/NTUA (2008), the modelling work to examine the combined effect of the 20% GHG target, 20% RES target and EU-ETS cap used a combination of specified carbon values and a ‘virtual’ RES-value to effectively constrain the model into meeting the combined targets simultaneously. Hence the subsidies will exactly cover the additional costs of RES and subsidy adjusted capital intensity would drop to zero for new plant (comparing the climate policy case with the baseline).

5.5.3.3 Average Load factor

Where a plant is expected to run “baseload” (i.e. at all times except during periods of maintenance or forced outages), its revenues will be determined by time-weighted average prices, and hence it is not sensitive to the shape or volatility of prices. However, where a plant is expected to run at a lower load factor, then its “capture price” (the average of prices during the periods in which the plant is running) will be affected by shape and volatility. For example, the revenues for a peaking plant will be very sensitive to the frequency and size of price spikes.

As expected load factor of the plant drops, financing becomes more difficult (other things being equal) for two reasons. First, there is a higher proportion of capital costs to total costs, and second the uncertainty in revenues is higher because of a greater sensitivity to price shape and volatility. Ideally a metric would be available that considers directly the impact of changes to price shape and volatility on investment. It is unlikely, however, that a sufficiently transparent and simple metric could be defined given the low granularity of PRIMES and its focus on 5-yearly projection periods. As a much simpler proxy, the load factor of plant itself may be helpful.

In practice there will be different considerations in different markets. For example, some markets remunerate capacity directly (for example with capacity payment mechanisms), whereas others are “energy only”. These considerations will have a key impact on investment decisions, particularly for lower load factor or peaking plant.

It is likely that climate change policies will have an impact on load factors of conventional plant, as very low marginal cost intermittent renewables will tend to displace conventional plant at times of high output, but require conventional plant to be running at times of low output.

The impact on the capital intensity of investments associated with lower load factors will already have been incorporated in the capital intensity metric above, as there will be a correspondingly smaller proportion of variable costs included in the total

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generation cost calculation. A metric that directly focuses on load factor may be helpful first in isolating that component from other drivers, and second in representing a sensitivity to price shape and volatility uncertainty. This may be most easily done by comparing the load factor for each generation type between the Baseline and Climate Change policy model runs for a given year (say 2020).

5.5.3.4 Current:Required investment ratio

This would represent a measure of the current known level of investment in the production sector for a given fuel (such as electricity generation), compared to that required. As noted above, this is more likely to serve as a “monitoring” metric once a policy had been implemented, rather than as an evaluation metric. As investments for central generation are typically committed several years in advance, a sensible time period for the definition of the metric might be five years from current. It would be possible to derive this metric in both capacity terms and financial terms.

• ( ) ( )( )yearsnextovercapacitymodelledofSum

yearsnextovercapacitynewknownofSumratioinvestmentquiredCurrent:Re55

=

The “known” investment level would need to be derived from public domain projects. The “required” investment level would be derived from PRIMES (or other) modelling of the investment profile under the climate change policy.

5.5.4 Specification(s) of indicator at Stage II

Stage II is the reduction in capacity margin associated with the lack of new investment. We consider capacity margin metrics carefully in Section 5.6 on load balancing. To deploy such a metric here would require a view to be taken on the possible shortfall in investment. As indicated above, it is possible to model this, but this would be complex and Member State dependent, and so it is not proposed to provide a component for the indicator at this stage.

5.5.5 Specification(s) of indicator at Stage III

Stage III is associated with the level to which imports can offset the lower investment in providing sufficient supply. There are two aspects to this. First, whether on long-term timescales, investments in the transmission network can offset the impact of lower investment in generation in a particular country or region. It would be extremely difficult to capture the effects of the interactions between investments in generation and investments in transmission. Second, on short-term timeframes, whether access to existing interconnection capacity provides benefit in offsetting lower generation investment.

This would in principle be simpler to measure, with a metric focused on the “spare” interconnector capacity. This could be considered from an “energy” and a “peak” perspective. The “energy” measure would consider the unused import capacity across

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a year within a modelling scenario in which the appropriate level of investment is made:

• ( ) ( )( ) ( )periodTimecapacityctorInterconne

importsTotalmarginctorinterconneEnergy*

=

The peak measure would consider the “spare” capacity available through interconnectors at times of peak demand:

• ( ) ( )( )capacityctorInterconne

periodpeakinImportsmarginctorinterconnePeak =

Models such as PRIMES have a simplified representation of ‘single’ interconnectors between Member States, but the required data does not form part of standard results outputs.

5.5.6 Specification(s) of indicator at Stage IV

Stage IV is the impact at the demand side of the lower capacity margin associated with insufficient investment. A direct metric that has been deployed to measure this, expected energy unserved, is described in Appendix B - B 3.1. However, as identified there, complex probabilistic modelling is required to determine this, and hence it is not considered suitable in this context.

As a simple proxy, the share of electricity consumption in final energy34 use would provide a measure of the overall vulnerability of the energy system to its use of electricity (in the case of physical unavailability or price impacts caused by insufficient investment).

5.5.7 Issues in application of the indicator(s) at EU and Member State/regional level

It should be possible to deploy the Stage I measures at EU, regional and Member State levels. Care would need to be taken in interpreting the Stage III and IV measures at a regional or EU level, as they do not take into account transmission between MSs. Hence, a measure may show that available capacity can meet demand, where in practice the locational distribution of supply and demand may mean that this is not feasible. Similarly, the way in which interconnectors are treated in the Stage III measure would need to be considered carefully – e.g. as part of the simple geographical representation discussed in section 5.3.2.

34 Or the complement of this where a rising value of the earlier stages of the indicator signifies decreasing

vulnerability (e.g. load factor).

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5.5.8 Differences in application of indicator across energy types

For the analysis of climate change policies, the sector likely to be most sensitive to insufficient investment is the power sector. This is also the sector with the most detailed model output from PRIMES, which should provide suitable data for the more straightforward metrics discussed above.

In principle the metric could be applied to other fuels and infrastructure such as gas networks. Here, changes in efficiency generally and in fuel demand from the electricity sector in particular may lead to uncertainty and hence potential underinvestment in fossil fuel supply chain infrastructure. However, there is a less direct link between climate change policies and investment in these sectors. This is in part because of the dominance of needs arising from depletion of fossil fuel resources (such as the major changes associated with reducing EU domestic gas production) and also because overall demand is typically globally driven, and hence the impact of climate change policies in affecting EU demand are not necessarily sufficient to draw conclusions. Given the difficulty in any case of finding appropriate metrics, it does not appear appropriate to extend these beyond the electricity sector.

Investments in the supply sector for oil, gas, coal and uranium are considered to be too international to be captured in these relatively simple measures, which focus on investments within the EU. Whilst clearly such international investments will in turn impact on EU energy security, the factors influencing them and the data needed to assess them are beyond the scope of this study. Future investments in domestic (and to a lesser extent international) biomass and biofuel supply may also be important over the longer term. However, much investment is either at a smaller or more disaggregated scale compared to fossil fuel supply (e.g. looking at locally/regionally sourced supply chains for heat and power generation). In addition there are multiple possible primary biomass sources, processing/refining routes and final products, which adds to the complexity (e.g. see annex in PRIMES (2007) for an overview). The simple metrics proposed here are therefore not considered appropriate to address this issue, and it is of secondary importance compared to the investment required in overall electricity generating capacity.

5.5.9 Overall indicator

Given the difficulty of establishing straightforward metrics for Stages II and III for this root cause type, we propose that the overall indicator should be the most representative of those described for Stage I. Given the importance of the interaction between subsidies (representing stability in revenues) and additional demand on financing for new investments, we would suggest that the subsidy-adjusted capital intensity measure may be the most appropriate overall indicator.

However, given the approach to subsidies applied in the PRIMES model, this is not considered possible and so only capital intensity will used. This metric would also be coupled to the share of electricity in final consumption at Stage IV, given that most climate policies also impact significantly at the demand-side.

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The overall indicator is then:

• Capital intensity = Capital intensity (%) * share of electricity in final energy (%)

This still represents only a very partial measure as it does not take into account the issues around certainty or stability in the regulatory environment, or the impact on investment of anticipated changes in the shape or volatility of prices. Qualitative views can be taken on these, or sophisticated modelling can be undertaken to assess them. Neither of these is considered appropriate in this context, given the need to apply these widely, simply and transparently across the EU based on existing model result sets.

However, given that it is also likely to be possible calculate the Stage I’s for the average load factor and required new capacity it is also proposed to incorporate them within separate indicators to complement the capital intensity metric

• Average load factor = load factor (%) * share of electricity in final energy (%)

• Required new capacity = cumulative required new capacity (MW or €M) * share of electricity in final energy (%)

5.5.10 List of detailed data requirements

The following are the main data requirements for the metrics defined above for the electricity sector.


Capacity by generation type by year PRIMES – future by MS


Capital costs by generation type by year PRIMES (“Investment expenditure” – definition to be

checked – both future and historic by MS

Total costs by generation type by year PRIMES (Require breakdown available by generation

type) both future and historic by MS

Output by generation type by year PRIMES – future by MS


Final electricity use (annual) PRIMES – future by MS


Final energy use (annual) PRIMES – future by MS


Availability factors for generation plant PRIMES – future by MS

Calculated from Eurostat data – historic by MS

For Stage III component only

Net imports by year PRIMES – future by MS


Interconnector capacity (average) Some historic data reported by MS system operators

Interconnector capacity (peak) Some historic data reported by MS system operators

Imports in peak period Some historic data reported by MS system operators

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5.6 Inadequate market structure: Load balancing failure

Load balancing in the short term is especially challenging for electricity due to the network infrastructure and the lack of storage capability. The gas system has similar issues, but to a lesser extent given storage capability in specific facilities and the inherent “linepack” storage of the network itself. A changing mix of technologies, interconnection and demand-side behaviour will change the dynamics of load balancing, and hence potentially affect the risk associated with this root cause.


As discussed in previous sections climate change policies will lead to a significant increase in the penetration of intermittent renewables. Hence, for a given total nameplate capacity on the system, the proportion available at a given time will depend heavily on wind (and potentially wave, tidal and solar) output at that point. It thus becomes significantly harder to determine whether there will be sufficient available capacity to meet peaks in demand, compared to a fossil fuel-based system.

In addition, wind and wave output is not just intermittent but unpredictable (tidal is fully predictable). Forecasts of wind, and to a lesser extent wave output, will be subject to significant error. This is likely to increase the need for reserve capacity to cover fluctuations in output. In addition, the rate at which output can change can be high, leading to a need for sufficient flexibility (of ramping) for conventional plant to meet these fluctuations.

Given that the dominant impact of climate change policies on supply/demand balancing will be in the electricity sector, we focus on that in this section. In summary, the impacts we are trying to measure in this section are:

• the expected available capacity to meet peak demand levels

• the flexibility of the system to respond to rapid rates of change in output.


An existing indicator, identified in section 4, is directly relevant here. The de-rated peak capacity margin (see Appendix B - B.2.1.2) is a direct measure of the difference between peak supply and demand, and hence a measure of the ability to meet it. This measure ranked well on relevance, and moderately on data availability and transparency, and hence we intend to draw on these here.

The measure of expected energy unserved translates this into the impact on demand in volumetric terms. Whilst this was identified as highly relevant, it ranked low on data availability and transparency, given the complexity of the modelling required to quantify it. We therefore do not pursue this measure in this section.

No measures were identified that directly addressed the issue of system flexibility.

5.6.3 Specification(s) of indicator at Stage I

Stage I is defined in section 2.4.2 as a supply/demand imbalance occurring that the system operator fails to compensate for. As discussed above, we break this out in this

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section based on two potential causes, unavailability of supply and lack of flexibility in available supply.

5.6.3.1 De-rated peak capacity margin

The first of these is directly addressed through the de-rated peak capacity margin. As described previously, this is measured by taking the nameplate capacity of each generation type, and then scaling by a “capacity credit” that represents the expected percentage of capacity of that generation type that will be available at peak:

• ( )( ) ( ) ( )

( )demandPeak

demandPeakcreditcapacitycapacitynameplatemargincapacitypeakDerated estechnologi

−⎟⎟⎠

⎞⎜⎜⎝

⎛

=∑ *

Capacity and peak demand are available from PRIMES. The capacity credit, however, will not be a direct PRIMES output. For conventional thermal plant, the capacity credit represents the probability of forced outage.

For intermittent renewables, the situation is more complicated. Due to the correlation in output between capacity of the same type, and between types that depend on a connected underlying resource (such as wind and wave), depending on geographical distribution, the effective capacity credit is a function of the levels of penetration of each type, the locations, and the wind (or other resource) patterns for those locations.

One way to quantify this is to model output from all plant stochastically, and determine, for a given mix of plant, the capacity of conventional plant that provides the same expected availability as the marginal capacity of renewables. The capacity credit can be derived as the ratio between the two. Multiple studies of this sort have been conducted for different markets, and results are often published in the form of a graph of capacity credit against total capacity on the system for a given technology.

Over time it is likely that there will be increasing information available on capacity credits in different Member States and for different mixes of capacity in different locations. However, in the first instance it is proposed to assess existing studies and derive some representative data to be used initially, recognizing that this should be reviewed and updated on a regular basis.

Other complexities in establishing the de-rated peak capacity margin include:

• determining how to treat interconnectors – and whether these can be considered to contribute to peak demand provision, or in fact add to load at times of peak

• accounting for demand-side response.

However, data availability – particularly in terms of projection data from models such as PRIMES is not sufficient to implement these additional elements.

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5.6.3.2 Flexibility margin

As noted above, no indicators were identified in section 4 that measured the flexibility of the system to respond to rapid changes in output. The “worst case” situation for this would be the coincidence of a maximum change in demand with a maximum fall in wind output, leading to the largest requirement for response from the controllable plant on the system. Our proposed measure is therefore defined as:

• ( ) ( ) ( )increasedemandofrateMaxfalloutputntintermitteofrateMaxneedyflexibilitMax +=

• ( ) ( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛∗= ∑

iestechnoleControllabCapacitytyAvailabiliraterampMaxavailablemarginyFlexibilit

log)(*)(

• ( ))(

)()(needyflexibilitMax

needyflexibilitMaxavailableyflexibilitMaxmarginyFlexibilit −=

Capacity data for this metric will be direct from PRIMES. Other inputs may need to be assessed in other ways. Approximate assumptions with regard to the maximum ramp rates for controllable technologies could be made, although a complexity here will be finding an appropriate way to recognise the differences between ramping from a cold, warm or hot start (i.e. from a situation where the plant has been off for a sustained period, has been off but was recently running, or is already running on a part-loaded basis).

Calculating the actual maximum rate of fall in intermittent output for a given geography is likely to be complicated. We would propose using a simplified representation drawn from published reports on wind output variation in the first instance. Over time this could be expanded to take into account differences across geographies and correlations between different renewable types.

The current maximum rate of demand increase is easy to determine from the half-hourly demand profiles available via the UCTE35. We would assume this stays constant over time unless a change is assumed (or modelled) in PRIMES.

As above, whether and how to incorporate the effect of interconnectors and demand-side response would require further assessment during subsequent tasks.

The availability of a plant type to respond to the needs of load shifting depends on how much of that plant is already being used in the system. This will obviously depend on the time of day and will be different for different plant types. If the increase in demand occurs early in the morning (6am onwards) then overall demand is low so there is lots of flexible plant in reserve. If it occurs during the late evening peak (~5-6pm) then demand is already high so there is less flexible plant.

35 Union for the coordination of the transmission of electricity http://www.ucte.org/

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5.6.4 Specification(s) of indicator at Stages II/III

Stage II is defined in section 2.4.2 as tripping of system components, such as transmission lines or other generation units. Stage III is the knock-on impact of these first failures across a broader span of the system. Attempting to quantify the potential impact of this would be a complicated process involving a detailed model of the transmission system (which is not built into long-term energy projection models such as PRIMES or POLES). We do not therefore propose any specific metric for these Stages (the simple geographical representation of networks discussed in section 5.3.2 would not be sufficient to examine this).

5.6.5 Specification(s) of indicator at Stage IV

Stage IV is defined in section 2.4.2 as the impact on the demand side. This will depend notably on the extent of knock-on effects across the system in Stages II and III, which we are not attempting to measure. The expected energy unserved metric provides a measure assuming that all available capacity is used to meet demand (and hence that Stage II/III effects are not material), but, as noted above, we consider this to be too complex to be suitable here.

Peak demand is already incorporated into the indicator at Stage I, but to reflect the overall importance of electricity use within the economy (and hence the magnitude of a disruption in supply due to load balancing), it is proposed to incorporate a simple metric of the complement of the share of electricity within final energy use at Stage IV. Data required would be direct from PRIMES.


In theory the indicators described here could be applied at EU, regional or Member State level. However, the issue with the treatment of interconnection in applying the Stage I indicators, noted above, clearly becomes more pronounced where there is a high level of interconnection between markets. It may therefore be most appropriate to deploy these metrics at a regional level, for example using the simple geographical representation discussed in section 5.3.2.

5.6.7 Differences in application of indicator across energy types

As discussed above, the primary sector for the application of these metrics is electricity. However, the networked infrastructure for gas means that the de-rated peak capacity margin can be applied. A more specific example of this, in terms of short-run availability of gas supply during an extreme weather cold spell, is proposed in the indicators in section 5.4.1. These consider both the short-run availability of gas during peak demand and the potential knock-on implications for the electricity capacity margin, given limitations in primary gas supply.

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5.6.8 Overall indicator

As discussed above, the metrics in this section are designed to be applied to the electricity sector only. We consider that the de-rated peak capacity margin is the main metric for this section, and hence should be treated as the overall indicator. However, the flexibility margin can also be considered an additional flag alongside this as a separate indicator. We do not propose to aggregate these indicators.

• De-rated electricity peak capacity margin (%) * (1- share of electricity in final energy) (%)

• Flexibility margin (%) * (1-share of electricity in final energy) (%)


The following are the primary data requirements for the metrics defined above for the electricity sector.


Capacity by electricity generation type by year PRIMES – future by MS


Peak electricity demand by year PRIMES – future by MS

Historic – system operators

Interconnector capacity (peak) ENTSOE – historic by MS

(Note: interconnector capacity varies during year

depending on pattern of flows across system).

Capacity credit Approximate figures derived from published studies.

Demand-side response Approximate figures would need to be derived from

published studies – e.g. by system operators

Ramp rates for controllable technologies Relatively standard assumptions available from

published sources that can be used as approximate

averages.

Max intermittent output change Approximate figures derived from published studies.

Max demand change Use UCTE half-hourly historic demand profiles to

determine maximum rate of change

To implement comparable energy margin for gas.

Capacity by gas supply type (storage, LNG, pipeline

etc) by year

Historic national operator data may be available in

some cases, but would require assumptions about

change to infrastructure going forward.

Peak gas demand by year Not available in PRIMES, only annual. Would need to

link this to historic peak demand.

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5.7 Supply shortfall associated with resource concentration

The energy security implications of resource concentration are important and have been the object of much analysis. As discussed in section 4, several indicators in the literature attempt to capture this in a variety of ways.

As discussed in section 2.5, in the case of supply shortfalls associated with resource concentration, it is important to distinguish between international markets where the price mechanism effectively balances supply and demand, and thereby minimizes physical unavailability risks, and markets where there is no or only limited price signals. The causal mechanisms in each category are different and our quantitative approach needs to capture these distinctions. The following sections consider each case separately.

Differences in application of indicator across energy types

With the spread of liberalization to most energy markets around the world, the importance of the second category has diminished. The international oil and coal markets, as well as segments of the international gas market can notably be considered as markets with effective price mechanisms, thereby falling in the first category. This does not mean that there are no energy security concerns associated to resource concentration in these markets, but rather that concerns are linked to the price of energy rather than to its physical availability, as detailed in section 5.7.3.

Some markets still lack effective price signals, thereby shifting much of the energy security problem on physical availability. This is notably the case of natural gas trade to Europe, which, while officially liberalized, is still relatively immature and in transition. Many gas transactions are based on oil price indexation, which effectively removes the price mechanism. The assessment of markets with no or only limited price mechanisms in section 5.7.4 focuses on such gas market organization.

As mentioned in section 2, resource concentration has primarily been a concern for fossil fuel markets. With respect to other fuels, the case for the applicability of a resource concentration-based indicator is less clear-cut. For uranium, the fuel used in nuclear power plants, resources are also geographically concentrated but uranium has a much higher energy density than fossil fuels and as a result, uranium can easily and economically be stored in large quantities. This contributes to significantly reduce energy security concerns associated with the import of Uranium. Known reserves by location are shown in the table below and the of use uranium in 2007 was approximately 65kt36. In addition, when compared to fossil fuel based electricity generation, fuel costs represent a significantly smaller fraction of total electricity costs. Nuclear power is therefore less affected by fuel price movements than fossil fuels. For these reasons, nuclear power can be considered significantly less prone to energy

36 This is of course a static picture of supply (which e.g. does not include secondary supplies and

reprocessing or future discovery) and demand (an expansion of nuclear power will increase demand for

uranium but new reactor designs use significantly less uranium per unit of electricity generation).

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security concerns associated with resource concentration and is therefore not included in this analysis.

Table 5 - 15 Known recoverable reserves of Uranium in 2007

Country Reserves % of World

Australia 1,243,000 23%

Kazakhstan 817,000 15%

Russia 546,000 10%

South Africa 435,000 8%

Canada 423,000 8%

USA 342,000 6%

Brazil 278,000 5%

Namibia 275,000 5%

Niger 274,000 5%

Ukraine 200,000 4%

Jordan 112,000 2%

Uzbekistan 111,000 2%

India 73,000 1%

China 68,000 1%

Mongolia 62,000 1%

Other 210,000 4%

World total 5,469,000 100%

Source: http://www.world-nuclear.org/info/inf75.html

Note: Reasonably Assured Resources plus Inferred Resources, to US$ 130/kg U, 1/1/07, from OECD NEA &

IAEA, Uranium 2007: Resources, Production and Demand ("Red Book").

The potential inclusion of biomass (including biofuels) within a measure of resource concentration is complicated. In particular, there are large uncertainties related to the evolution of the biomass industry and markets over the coming decades. This makes it very hard to undertake any objective analysis of potential energy security implications.

Unlike fossil fuels, biomass production is renewable and the potential for further expanded production is significant within the EU as well as globally. But, this is subject to constraints related to cost, fossil fuel prices, developments in trade and tariffs, land-availability and competition with food and non-food crops and environmental sustainability concerns. Whilst there are a number of clear key producers at present (e.g. Brazil and the US for bioethanol, Malaysia for Palm Oil and the EU for biodiesel), how this situation will change in future is uncertain and currently more difficult to model than estimates of declining reserves for fossil fuels.

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There are also a wide-range of different primary biomass sources37, processing routes and possible biofuels (see annex to PRIMES (2007) for a high-level overview). Imports and international trade may occur in relation to the primary/secondary commodities produced (e.g. pelletised wood, processed biomass sugars or oils) or final products such bioethanol or biodiesel (or even fossil / biofuel blends). Depending on the evolution of biomass trade, different product(s) should be targeted by an indicator of resource concentration. The direction of future technological developments adds further uncertainty; in particular, the development of second-generation biofuels may expand the range of biomass feedstock to woody-biomass, residual non-food parts of current crops as well as other non-food crops.

The main issue for the EU and energy security is therefore to what extent the rising demand for biomass and biofuels exceeds the limits of what can produced domestically (subject to various constraints) and the concentration of key non-EU producers for these products.

Recent modelling work (EEA, 2008) shows that the possible expansion of bioenergy production from “environmentally-compatible” biomass/biofuel potential within the EU is likely to be significant out to 2030. If all of the potential were used in a ‘least-cost’ manner (i.e. favouring heat and electricity generation), by 2030 bioenergy would meet around 16% of EU-25 primary energy consumption, 18% of heat demand, 13% of electricity demand and 7% of diesel and gasoline demand in road transport – albeit under a low-carbon scenario with sizeable end-use efficiency improvements. Giving priority to the 2020 target of a 10% share for transport biofuels38 leads to substantially higher production costs (depending on the price for fossil transport fuels which are offset). But, this relies on key assumptions about the development of intra-EU trade and the rapid development of second generation technologies to successfully employ the large potential from woody biomass in the EU.

The most significant issue is therefore likely to be the import of biofuels (or biomass commodities for refining in the EU) for use in road transport to meet higher targets (TradeAG, 2007).

However, given these uncertainties and the fact that the EU target for 2020 is only 10% of road transport fuels, the maximum potential energy security impacts are likely to be small when set in context of those for oil and its continued dominant use in transport. This is not to say that biofuel resource concentration may not become an important issue over the long term, as sustainable production limits are reached, international markets for the products mature, and fossil alternatives decline. But a simple indicator of resource concentration is not considered practical or meaningful at this stage.

37 Such as energy crops (including hay, sugar, oil and wood crops), agricultural residues, forestry, aquatic

biomass and wastes. 38 As there are a wider variety of other non-biomass technologies to generate renewable electricity and heat

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As highlighted in section 3, the main effects of climate change mitigation on the energy security implications of resource concentration will come through changes in the fuel mix and energy demand of countries. These alter the exposure of countries to the various resource concentration risks that characterize international fuel markets.


The most directly relevant indicators in the literature are those derived in IEA (2004, 2007) in Appendix B - B 2.5. These will form the basis for the approach adopted in this study and will be refined where possible. Much can be learned, however, from other indicators and information will be drawn from them when necessary.

5.7.3 Markets characterized by an effective price mechanism

5.7.3.1 Specification(s) of indicator at Stage I

In international markets where the price mechanism effectively balances supply and demand, the primary concern associated with resource concentration is that market participants do not behave competitively. This is stage I of the causal mechanism defined in section 2.5. To capture this quantitatively, we adopt the same approach as IEA (2007), i.e. a measure of market concentration, referred to as Energy Security Market Concentration (ESMC), which aims to represents the ‘price risk’ resulting from resource concentration.

ESMC is based on the Herfindhal-Hirschman Index (HHI), equal to the sum of the square of the individual market shares of all the participants. As noted in Appendix B - B.1.5.1 HHI is a well established measure of market concentration commonly used by governments as a tool to assist them in determining market power in the scope of competition law.

As discussed in IEA (2007), defining the boundaries of each market, both geographically and in terms of product, lies at the heart of an approach based on market concentration. The IEA study considers each fossil fuel market independently, i.e. it measures ESMC for oil, gas and coal. We propose to adopt the same approach here. However, while not an issue for oil or gas, further research could be undertaken to establish whether the international coal market should be subdivided into two separate products: coking coal and steam coal. These can broadly be considered as two independently-priced markets with separate uses in steel production and electricity and heat generation. The question is whether such distinctions are sufficiently important to mandate considering two markets instead of one. With respect to climate policy and the interaction with energy security, the implications of resource concentration in the international coking coal market are likely to be less important. In contrast to electricity production, steel can be produced globally and then imported in significant quantities to the EU so direct energy-related security issues are potentially of secondary concern to wider international trade issues.

The market participants in the scope of this study are assumed to be countries as governments ultimately have control over the exploitation of their natural resources.

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In addition, the majority of energy resources are exploited by government owned companies. In the case of oil and gas, for example, state-owned companies operate approximately 80% of reserves. From a security perspective, therefore, a country level approach seems best suited.

One important exception to this is the treatment of the Organization of the Petroleum Exporting Countries. OPEC has set production quotas for its members since the early 1960s. However, the effectiveness of this process is unclear. Decisions on quota levels are the result of often tedious negotiations, in part because oil revenues typically represent such a large share of the economy (Adelman, 2001). Also, quotas have not always been respected, particularly in times of low oil prices. Nevertheless, while clumsy, OPEC remains a cartel-like organization. We therefore propose to assess the role of OPEC in the analysis through a variety of cases and sensitivity analysis based on the inclusion of OPEC as one supplier and progressively relaxing this assumption to considering each country individually.

While the uneven distribution of energy resources is the cause of the energy security concern we are interested in, it is only through the physical development of the international market that this concern materializes. Measuring market shares based on resources is therefore inappropriate. Other options are using production levels or exports. Using production levels is also unsuitable as not all production from a given country is made available on the international market. Many producing countries notably price differently domestic consumption and exports. In addition, there are physical constraints to how much may be exported. Using a measure of net export potential as the basis for the definition of market shares therefore seems best suited as it encompasses physical limitations and whether countries price domestic consumption differently from exports or not. For each fossil fuel f, therefore, ESMC is defined by:

• ESMCf = Σi (Sif2) (1)

• Where Sif is the percentage share of each supplier i in the international market for fuel f defined by its net export potential (Sif varies from 0 to 100).

Values of ESMCf range from zero, which suggests a highly competitive market, and 10,000 for a pure monopoly. A higher ESMCf value therefore implies higher risk of insecurity should the suppliers exploit their market power.

In the case of oil and coal, net exports from all countries are included in the measure of ESMC. In the case of natural gas, a distinction needs to be made between gas sold through spot or spot-derived pricing and gas sold in the scope of oil price indexation. Only gas sold on spot or spot-derived markets is considered in ESMC.

An important question from an energy security perspective is whether and how a country’s own share of the market affects risk. In other words, whether ESMC should vary from country to country depending on the country’s own market share or whether all countries face the same ‘price risk’. The answer to this question is not straightforward. If a country is a large net exporter then prices set above the competitive level would lead to an enhanced revenue stream. From the consumer’s

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perspective, however, much depends on the magnitude of this revenue stream and on redistribution policies. In a case where there are no, or only limited wealth redistribution policies associated with export revenues, a price increase would not benefit consumers and therefore the price risk should be considered the same as any other country. If, on the contrary, a country has effective wealth redistribution policies then the price risk on the market should be considered less than that of a country with a smaller share of the market. For simplicity and as a precautionary stance a single price risk is assumed irrespective of a country’s own position on the market. In other words, all countries are considered to face the same price risk associated with resource concentration.

As discussed in section Appendix B - B.1.6.2 accounting for political stability seems particularly important (albeit subjective) for any measure of energy insecurity associated with resource concentration. As mentioned earlier, in addition to being geographically concentrated, energy resources are also often located in politically sensitive areas of the world. This may affect the reliability of countries as trade partners. To account for political stability, the measure of ESMC defined in equation 1 can be modified as follows:

• ESMCpol-f = Σi (ri * Sif2) (2)

• Where ri is a political risk rating for country i.

The inclusion of this parameter should scale up market concentration risks when participants are politically unstable. The approach proposed in equation (2) assumes linearity in the scaling of market share by political risk but other approaches may be considered. The extent of the scale-up should reflect the importance given to political stability but the lowest value possible is 1, representing zero political risk.

5.7.3.2 Specification(s) of indicator at Stage II

The indicator at Stage II is classified as a shortfall in the international production sector, compensated to some extent by increases in production from other countries. The impact of uncompetitive behaviour of certain countries on the international production sector is difficult to estimate. The problem is that while resource concentration attributes a form of market power to countries with large export potentials, it does not indicate whether and how such power may be used. The impact on overall levels of production on the international market is therefore difficult to determine and, as such, a measure of this stage of the causal mechanism is not practicable.

5.7.3.3 Specification(s) of indicator at Stage III

The extent to which supply shortfalls in the international production sector linked to resource concentration impact other domestic sectors of the supply chain depends on both supply flexibility and liquidity of the domestic market.

A number of market characteristics affect both of these parameters and it is not a straightforward task to account for these quantitatively. IEA (2004) introduces a measure of market liquidity by linking the size of the country’s demand for a given

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fuel to total available supply of the fuel on the market (see section Appendix B - B.1.6.1). For simplicity, IEA (2007) assumes that flexibility and fluidity do not contribute to exacerbate energy security impacts and therefore ignores this stage of the causal mechanism. Implicitly, all fuel markets are therefore considered as suitably (and equally) fluid and flexible.

None of the indicators in the literature seem to adequately account for flexibility or liquidity. Not addressing these quantitatively as in IEA (2007) can be considered a fallback approach, yet it is useful to try to at least identify potential alternatives in the scope of this study.

Fuel quality flexibility parameter

With respect to supply flexibility, one important characteristic to consider is the ability of plants to accept different qualities of input fuels (refineries in the case of oil, industrial facilities or power plants in the case of coal or gas, regasification terminals in the case of LNG). One possible indicator is to measure the average quality range of a given fuel that a country’s plants can process. In the case of coal, for example, quality can be defined as a function of ash content and humidity. The indicator will therefore measure the average range of coals that a country’s industrial plants or electricity generation units can accept. In the case of crude oil, the key criteria are density and sulphur content and the indicator would measure the average range of crude qualities a country’s refineries can process. This would then be used as the basis for a fuel input flexibility parameter Kf for each fuel f. Flexibility is maximized when Kf equals 100%, indicating that plants can accept the full range of fuel qualities. Lower values indicate lower flexibility. The inclusion of this parameter could scale up market concentration risks when flexibility is limited. Much like the political risk factor introduced earlier, the extent of the scale-up should reflect the importance given to political stability but the lowest value possible is 1, representing total flexibility to accept any fuel qualities. One possibility is to use 1/ Kf as the multiplying factor. For values of Kf between 100% and 50%, 1/ Kf ranges from 1 to 2. 1/ Kf increases exponentially for values of Kf lower than 50%.

A meaningful application of this proxy is only possible if related directly to the different shares of fuel quality in the market (i.e. it would then be necessary to sub-divide crude oil into e.g. different sweet/sour ‘qualities’).

Liquidity parameter

With respect to market liquidity, an indicator should ideally be able to capture the suitability of the market’s transport network as well of its institutions for the well functioning of market operations. This is particularly difficult to evaluate and necessarily involves some subjectivity. One option would be to consider using data on trade volumes as a proxy for market liquidity. While this only captures liquidity of trade in contracts, it does give a useful indication of the maturity and liquidity of the underlying fuel market. One difficulty, however, is that it is impossible to predict the evolution of trade volumes. Nevertheless, this could still be a useful way to underscore the important differences in each fuel market in the future.

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5.7.3.4 Specification(s) of indicator at Stage IV

To capture the exposure of a given country to the price risks associated with resource concentration IEA (2007) measures the share of the country’s total energy demand exposed to each of the fuel markets ESMCpol values as follows:

• E-f /TPES (6)

Where E-f is the country’s energy demand exposed to the ‘price risk’ of fuel f, and TPES is the country’s Total Primary Energy Supply39.

In the case of coal, E-coal is simply total coal supply. In the case of oil and gas the value of E-f depends on the country’s gas market organization. As mentioned, a distinction needs to be made between a market organization where prices reflect market fundamentals and a case where prices are fixed or indexed to oil. In the first case, the approach is as simple as in the case of coal. E-oil and E-gas are respectively total oil and total natural gas supply. If the price of gas is simply fixed then there is no price concern for gas and E-gas is equal to zero. In most cases, however, the gas price is indexed to oil in one way or another. As such, gas is also exposed to the resource concentration price risk of the oil market. In this case therefore E-oil is equal to total oil supply plus the gas supplied through oil-indexed contracts and E-gas equals zero. In some cases, natural gas supply is partly based on spot or spot-derived prices and partly based on such oil-indexed contracts. In such cases E-oil is equal to total oil supply plus gas supplied through the oil-indexed contracts and E-gas is equals to gas supplied through purchases in the competitive segment of the gas market.

While appealing for its simplicity, the approach fails to account for important parameters that also affect the exposure of countries to price risks. In particular, this approach fails to capture the role of fuel substitutability and storage/stockpiling. Fuel substitutability allows for a plant to switch fuel use in case of an energy security concern while stockpiling offers a temporary alternative fuel source.

Instead of considering total domestic fuel demand as the basis of the measure of exposure to the price risk (E-f in equation 6), one simple way to account for such parameters would be to consider ‘minimum’ fuel demand. In other words total domestic fuel demand would be reduced by the amount which can effectively be covered by means of substitutability or stockpiling. We propose to adopt the same basic approach as in IEA (2007), though we modify equation 6 as follows:

• E-f-min / TPES (7)

Where E-f-min is the country’s energy demand exposed to the ‘price risk’ of fuel f, corrected to account for fuel substitutability potential and the commercial stockpiling capacity40 for the year the analysis is being undertaken. So E-coal-min, for example, would be total domestic coal demand minus the sum of multi-fuel capacity at

39 E-f and TPES should be both measured in energy units so E-f / TPES is in percentage points. 40 Strategic stocks are only maintained for oil and reserved for extreme cases under the governance of the

International Energy Agency. We therefore do not consider these for the price concerns discussed here.

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industrial and power plants and the coal stocks available that year. For the rest, the approach is the same as that described above.

Accounting for ‘minimum shares’ as described above raises some important questions. In particular, in the event that two energy security alerts affecting different fuels occurred simultaneously, fuel substitutability capacity between the two fuels affected would be of limited use. Similarly, if an energy security alert lasts for an extended period then accounting for storage as suggested above would be misleading as during that time the stocks would not be replenished. Nevertheless, such cases are extreme and highly unlikely. While imperfect, we therefore think the modifications to IEA (2007) suggested above are potentially useful additions.


We can combine the measures defined for the various stages into a single Resource Concentration Price Indicator (RCPI) as follows:

RCPI = Σf [ESMCpol-f * 1/Kf * Ef-min / TPES] (8)

5.7.4 Markets with no or limited price mechanism

5.7.4.1 Specification(s) of indicator at Stage I

The first stage of the energy security causal mechanism in the context of markets where there is no or only limited price mechanism, and in particular for our interests the segments of the gas market dominated by oil indexed contracts, is a supply shortfall not appropriately compensated for. The possible reasons for such a supply shortfall are various, including both unexpected rise in demand in the importing country or drop in supply in the exporting country. These may be accidental as well as premeditated. Due to the undefined nature of such events, there are no useful proxies for the potential risk of an event occurring at this stage of the causal mechanism.

5.7.4.2 Specification(s) of indicator at Stage II

The severity of the impact on domestic supply due to a shortfall in imports in a context of no or limited price mechanism will depend on the extent to which this can be compensated by domestic production or increased imports from other countries.

IEA (2007) highlights the importance of gas import infrastructures flexibility in determining the country’s physical unavailability risk. In particular, due the difference in the inherent flexibility of LNG- and pipe-based trade it argues a distinction should be made between these modes of gas transport.

In the case of pipe-based contracts between two countries a supply shortfall cannot be easily compensated for by other supplies. Generally, the importing country cannot use the same infrastructure to import from other sources, as a pipeline tends to tie a customer to a given supplier. If the country has access to other import pipes then it may be able to compensate for some of the lost supply though this is uncertain. Spare capacity availability generally depends on the time of year with no or very limited

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spare capacity during periods of strong demand (e.g. winter peak). Also, supplier countries may not be able to increase production to compensate for a supply shortfall in recipient countries as their export infrastructure may also be operating at capacity. If the importing country also has access to spot cargoes thanks to LNG infrastructures, much depends again on physical availability constraints. It will only be able to increase LNG imports to compensate for a supply shortfall from pipe-based imports if there is available capacity at the regasification terminals.

In the case of an LNG-based contract, in the event of a supply shortfall, IEA (2007) argues that the country to which the LNG cargo was destined has the opportunity to use the freed capacity to import LNG from elsewhere. The country would most likely look at LNG spot cargoes to replace lost volumes. Unlike the case of pipe-based contracts, there should be no capacity constraint and physical unavailability risks are limited.

Due to the inflexibility of pipelines, IEA (2007) proposes to simply measure the ratio of pipe-based gas imports that fall under regulated bilateral contracts to total gas consumption. The term ‘regulated’ is used here to indicate a context where prices do not reflect gas supply and demand dynamics (e.g. where prices are indexed to oil).

• Gasimp-pipe-regulated / GAS (4)

• Where Gasimp-pipe-regulated is the supply of gas imported by pipeline based on regulated contracts, and GAS is total gas consumption.

This value ranges from 0 when either no gas supply is purchased on regulated terms, there are no pipe-based imports (i.e. imports are entirely based on LNG), or the country is self sufficient in gas (i.e. no imports), to 100 (percent) in the hypothetical case where the country’s gas supply is entirely imported through pipelines and based on regulated contracts.

While appealing for its simplicity, the approach has some important limitations. First, the assumption that a supply shortfall in the scope of an LNG-based oil-indexed contract can be fully compensated by turning to the LNG spot market seems overly optimistic in the time frames of interest to us, at least in the period up to 2020, although it may be more reasonable moving beyond 2030. At present there is not a comparable spot market for LNG in the same manner as crude oil. In LNG terms there are very few cargos available for third party title transfer and there are almost no LNG brokers or traders outside of the integrated players. This is likely to remain the case until there is a substantial excess supply of LNG. A further constraint is that there is not uniform specification for LNG and hence regasification can only take some cargoes. IEA (2007) does, however, note that as LNG trade volumes increase this should become less of a problem. This therefore implies that the LNG spot market will eventually become sufficiently mature to be able to cope with supply shortfalls on the regulated segments of the market. However, we propose to adopt a more precautionary stance and include all types of contracts in our analysis, whether LNG-based or pipe-based.

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Another important limitation to IEA (2007) is that it fails to account for diversity in supply sources - the approach proposed in equation 4 does not distinguish whether the importing country is supplied by one or several countries. A greater diversity in import sources will tend to mitigate the energy security impacts and should ideally be accounted for.

Another limitation is that it does not account for flexibility in domestic production. As noted above, the country suffering the supply shortfall may be able to increase domestic capacity to compensate some of the loss of imports. This issue of domestic production flexibility is discussed further in section 5.7.4.3.

With respect to the issue of LNG-based contracts and import source diversity, an alternative to equation 4 is to adopt a measure of import concentration targeted at all imports that fall under regulated contracts. The benefit of this approach is that it accounts for diversity of import sources irrespective of whether they are LNG- or pipe-based. This concentration measure is based on the shares of total gas imports that fall under regulated contracts. Using the same HHI approach as for market concentration an Energy Security Import Concentration (ESIC) is defined as follows:

• ESICgas = Σi (Gasimp-regulated-i / GASimp-regulated)2 (5)

• Where Gasimp-regulated-i is the supply of gas imports based on regulated contracts from country i and GASimp-regulated is total regulated gas imports.

Gasimp-regulated-i / GASimp-regulated is a percentage and ranges from 0 to 100. The resulting value of ESICgas is zero when the country is self sufficient in gas (i.e. no imports), as well as if there are an infinite number of countries supplying gas on regulated terms. It is 10,000 in the hypothetical case where the country’s regulated gas imports all come from one country.

In contrast to IEA (2007), the drawback of this approach is that it does not distinguish between pipe and LNG based trade. While a parameter could be introduced to progressively shift the emphasis away from LNG-based contracts as the LNG spot market matures for the reasons noted above, developments in the LNG market are still very uncertain and for the time-frame of this study it seems more appropriate to consider LNG equal to pipe.

In a similar manner to the ESMC metric in section 5.7.3.1 it is also possible to weight the political stability of imports from country i based on the same political risk ratings (1 = most stable to 3 = least stable). This changes the ESIC metric to:

• ESICgas-pol = Σi ((Gasimp-regulated-i * ri )/ GASimp-regulated)2 (5b)

• Where ri is a political risk rating for country i.

5.7.4.3 Specification(s) of indicator at Stage III

As highlighted in section 2.5, in a context where the price mechanism is inexistent or ineffective, the extent to which an import supply shortfall affects other sectors in the supply chain depends on capacity constraints in the domestic gas transport network. Energy security impacts would be mitigated to some extent if, for example, the

150


pipeline networks within a country allowed for a supply shortfall at one import pipeline to be compensated by the eventual increase in imports at another import location or if storage capacity were available to compensate (at least temporarily) for the shortfall. However, an important dimension is whether the regulatory environment within the country hampers the eventual shift in domestic gas transit.

Ideally, an indicator of this stage would capture the flexibility of domestic gas transport network. This is a difficult task and no indicator in the literature addresses this issue. IEA (2007) notably does not factor this into its measure of physical unavailability risks in regulated gas markets.

However, as a simplification, the availability of gas storage to cover any supply shortfall is considered; this is factored into the Stage IV component below, to adjust the annual share of gas purchased under regulated contracts to a minimum share that it is not possible to cover via storage.

5.7.4.4 Specification(s) of indicator at Stage IV

In its assessment of regulated market settings IEA (2007) adopts again a very simple measure of demand side exposure by simply considering the share of natural gas in total energy demand (i.e. GAS / TPES). The resulting overall indicator is therefore:

• (Gasimp-regulated / GAS) * (GAS / TPES) = Gasimp-regulated / TPES (6)

Here we adopt a similar approach, though as in the case of markets with effective price signals, we propose a number of modifications. In particular, we propose to account for domestic production flexibility as well as fuel substitutability capacity and stockpiling/storage.

There is also the potential to adopt the same approach at Stage IV as in the case of markets with effective price signals described above and introduce the following factor:

• GASreg-imp-min / TPES (7)

Where GASreg-imp-min is the country’s minimum gas imports met by purchases on regulated terms. This is equal to total gas imports met by purchases on regulated terms corrected for unused domestic production capacity, gas substitutability capacity at industrial and electricity generation plants, and stockpiling. This is the same approach as that described for markets with effective price mechanisms (section 5.7.3.4) except that here the emphasis is on total gas imports and that we also account for unused domestic gas production. The same caveats also apply in this case.


We can combine the measures defined for the various stages into a single Resource Concentration Physical Availability Indicator (RCPAI) as follows:

• RCPAI = ESICgas-pol * GASreg-imp-min / TPES (8)

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As per the indicator on other extreme events in section 5.4.2, it would be necessary to examine the geographical distribution of existing pipeline import routes. These may ultimately serve more than one MS or region and it would be necessary to examine how regions closer to the point of disruption draw down on gas and that remaining for subsequent regions as mentioned in section 5.3.2. In addition, where there are multiple pipeline routes into a MS/region it will be necessary to aggregate those of the same non-EU country origin.

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Fuel export potential by country for oil, coal and gas.

If estimates for all countries are not available, top

10 largest exporters.

Estimates of export potential is difficult to

determine, particularly in the scope of projections,

therefore net export can be used instead. If no

estimates of net exports are available, subtracting

estimates of domestic consumption from production

can be used. If country level estimates are not

available, regional estimates will have to be

disaggregated.

Future global data on net exports may be available

from the POLES model. As a fall back the IEA World

Energy Outlook data in IEA (2007) for 2030 can be

used.

Historic data on exports from Eurostat / IEA / EIA

Details of natural gas import arrangements for each

EU country

– i.e. share of total gas imports purchased in

the scope of bilateral contracts, and share

purchased on spot or spot-derived markets;

contracts specifications, i.e. oil indexation

arrangements, exporting countries, and

estimates of how world gas market will

evolve. In particular, the share of

production sold under oil-indexed contracts

– supply routes and capacity.

If no data is available on future gas market

evolution illustrative estimates can be considered

under various scenarios. Current data based on

national operators and other available estimates.

Existing data on supply routes and capacity as per

indicator on extreme events.

Total energy consumption by EU country and by

fuel.

PRIMES – Future

Eurostat – Historic

Assessment of flexibility of plants to use various fuel

qualities for refineries, power plants, and industrial

sector (steel in particular).

For refineries some data on flexibility by fuel quality

is broadly available (e.g. Oil and Gas Journal) but

cannot be readily aggregated as there are no

defined categories. An option would be to identify

flexibility by process. This is the same approach that

is used for power plant flexibility.

Duel fuel/multi fuel capacity of plants Existing – some data from MS Governments,

PLATTS, etc

Political risk rating Several options available see section Appendix B -

B.1.6.2

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5.8 Summary

The indicators proposed in the quantitative approach are summarized below. It should be noted that:

• For extreme events the previous sections separated the indicators into the impact at the demand side (i.e. extreme weather) versus the impact at the demand side (from other extreme events including weather).

- However, as the underlying construction of the indicators is the same it is possible to combine the elements of the indicators into one - to (potentially) explore the impact of an extreme cold spell on demand couple with the loss of the largest supplier (i.e. a “worst case”-type scenario). This functionality is implemented in the spreadsheet tool described in the following section.

• The basic formulation of the DEPCM (De-rated Electricity Peak Capacity Margin) indicator is the same for both extreme events and load balancing failure. The differences being the:

- Adjustment to peak demand (in an extreme weather situation as opposed to a typical winter peak) and the;

- Sum of de-rated capacity, which is further de-rated due to the loss of a key plant (in the case of an other extreme event) and/or the shortfall in primary gas supply (due to an extreme cold weather event) limiting the availability of gas-fired generation.

Hence, without these differences the calculated value of the DEPCM will be identical.

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Table 5 - 16 Summary of energy security indicators under quantitative approach

Root cause Indicator name Description Overall Short-Run availability of primary fuel (gas)

• If DPSS ≤ 0 then SR Availability (days) = ⎟⎟⎠

⎞⎜⎜⎝

⎛−

fuelsall

fuelX

TPESTPES

1*365

• else, SR Availability (days) = ⎟⎟⎠

⎞⎜⎜⎝

⎛−⎟

⎠⎞

⎜⎝⎛

− fuelsall

fuelX

TPESTPES

LSSPRDPSSNetStorage 1*

Where


Peak demand will increase under an extreme weather event. - TPES (ktoe) = Total Primary Energy Supply - NetStorage = maximum available storage capacity (ktoe) - net of the largest supplier if considering the

impact of other extreme events on supply and if storage is the largest supplier. - LSSPR (ktoe) = supply from largest supplier, plant or route (excluding storage) if considering the impact

of other extreme events on supply (otherwise this is set = 0)

Extreme events (covers both extreme weather and other extreme events within same formulae)

Overall short-Run availability of primary fuel (oil)

• SR Availability (ktoe) =

( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛−−

fuelsall

fuelX

TPESTPES

requirederageofdaysBenchmarkLSSPRDPSS 1*cov*

Where


Peak demand will increase under an extreme weather event. - TPES (ktoe) = Total Primary Energy Supply - LSSPR (ktoe) = supply from largest supplier, plant or route if considering the impact of other extreme

events on supply (otherwise this is set = 0)

155


Root cause Indicator name Description Further De-rated electricity peak capacity margin (adjusted for loss of plant and/or further de-rating for primary fuel shortage)

• DEPCM (%) = ⎟⎟⎠

⎞⎜⎜⎝

⎛−

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛ −−−∑

all

yelectricitiestechnoAll

FECFEC

DemandPeak

DemandPeakADLoPDcapacity1*log

Where:

- DCapacity (MW) = de-rated capacity of electricity plant - AD (MW) = Additional De-rating (due to primary fuel shortage) - PeakDemand (MW) = peak electricity demand (this will increase if considering the situation under an

extreme weather event) - FEC (ktoe) = final energy consumption - LoP (MW) = Loss of Plant capacity (MW) due to shut down of largest individual plant, loss of

transmission line connecting plant(s), etc – if considering the impact of other extreme events on supply (otherwise this is set = 0)

Capital Intensity

• Capital intensity (%)= ⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟

⎠

⎞

⎜⎜

⎝

⎛

∑∑

=

=

all

yelectricitt

i

t

i

FECFEC

tstotal

tscapital*

cos

cos

1

1

Where

- ∑=

t

i 1= cumulative sum of capital costs or total costs of electricity generation from Year 1 to t in €M.

- FEC (ktoe) = Final energy consumption Average Load Factor • Average annual load factor (%) = ⎟⎟

⎠

⎞⎜⎜⎝

⎛−⎟⎟

⎠

⎞⎜⎜⎝

⎛

all

yelectricit

FECFEC

generationpossibleMaximumgenerationyElectricit 1*

Where - FEC (ktoe) = Final energy consumption


Cumulative required new capacity • Required new capacity (MW or €M)= ( ) ⎟⎟

⎠

⎞⎜⎜⎝

⎛∑ =

all

yelectricitt

i FECFEC

capacitynew *1

Where

- ∑=

t

i 1= sum of new generation capacity (total cost €M or nameplate capacity in MW) from Year 1 to t.

- FEC (ktoe) = final electricity consumption

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Root cause Indicator name Description De-rated electricity peak capacity margin

• DEPCM (%)( ) ( ) ( )

( ) ⎟⎟⎠

⎞⎜⎜⎝

⎛−

−⎟⎟⎠

⎞⎜⎜⎝

⎛

=∑

all

yelectricitsechnologietAll

FECFEC

demandPeak

demandPeakcreditcapacitycapacitynameplate1*

*

Where - Name plate capacity (MW) = total nameplate capacity of each technology type - Capacity credit (%) = annual output as a % of nameplate capacity for each technology type given

planned outages, intermittent generation, etc. - PeakDemand (MW) = peak electricity demand - FEC (ktoe) = Final energy consumption

Inadequate market structure: Load balancing failure

Flexibility Margin • FM (%)

=

( )

)(

)()(*)(log

needyflexibilitMax

needyflexibilitMaxCapacitytyAvailabiliraterampMaxiestechnoleControllab

−⎟⎟⎠

⎞⎜⎜⎝

⎛∗∑

⎟⎟⎠

⎞⎜⎜⎝

⎛−

all

yelectricit

FECFEC

1*

Where - Max flexibility need (MW) = Max rate of intermittent output fall in 1 hour period + Max rate of possible

demand increase in 1 hour period. - Capacity (MW) = total nameplate capacity of each dispatchable plant type - Max ramp rate (%) = % of nameplate capacity from each dispatchable plant type available in 1 hour. - Availability is the % of the capacity that is available for use at the time that availability is needed - FEC (ktoe) = Final energy consumption


Resource Concentration Price Indicator (markets characterized by an effective price mechanism)

• RCPI = ∑⎥⎥⎦

⎤

⎢⎢⎣

⎡ −− TPES

EK

ESMC f

ffpolf

min*1*

Where - ESMCpol-f = Σi (Sif

2)= Energy Security Market Concentration measure for fuel f. Sif = the % share of each supplier i in the international market for fuel f defined by its net export potential (Sif varies from 0 to 100).

- Ef-min (ktoe) = minimum primary energy demand for fuel f. - TPES (ktoe) = Total primary energy supply - Kf (%) = fuel input flexibility parameter

157


Root cause Indicator name Description Resource Concentration Physical Availability Indicator (markets with no or limited price mechanism)

• RCPAI = ⎟⎟⎠

⎞⎜⎜⎝

⎛ −−− TPES

GasESIC impreg

polgasmin*

Where - ESICgas-pol = Energy Security Import Concentration = Σi ((Gasimp-regulated-I * ri )/ GASimp-regulated)2 . Where

Gasimp-regulated-i is the supply of gas imports based on regulated contracts from country i and GASimp-

regulated is total regulated gas imports. - Where GASreg-imp-min is the country’s minimum gas imports met by purchases on regulated terms (in this

case minimum demand is estimated as annual net imports purchased under regulated contracts NOT covered by available storage capacity.

- TPES = Total primary energy supply - ri = political risk rating for country i.

N/A Alternative Stage IV

indicator - Adjusted Share of Primary Fuel in FEC (Applies to any non-electricity related Stage IV)

• Adjusted share of primary fuel in FEC =FECTotal

EffEffEHCDFEC

all

fuelfuelfuel ⎟

⎠⎞

⎜⎝⎛ + *

Where: - DFECfuel = Direct Final Energy Consumption of fuel - EHCfuel = Electricity and heat generated by fuel* - Total FEC = Total Final Energy Consumption - Effall = efficiency of all heat and power generation (including CHP and district heating) = total inputs to

thermal plant (including nuclear) / sum of total heat and electricity generation* - Efffuel = efficiency of heat and power generation from fuel (including CHP and district heating) = total fuel

input to thermal plant / sum of electricity and heat generation from fuel* * = net of energy branch consumption and transmission and distribution losses (these are apportioned across each fuel depending on its share in net generation).

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6 Quantitative analysis of the energy security impact of key new climate policies

6.1 Introduction

The primary focus of this project has been to develop a base methodology to assess the impact of climate change policies on energy security and a selection of targeted vulnerability indicators has been developed in the previous sections to achieve this.

As shown in section 5.1, quantitative data on the evolution of the energy system both with and without a policy in place is needed to construct the indicator in each case, with the difference between the two (i.e. change in the EU or MS’s vulnerability) showing the policy’s impact on energy security. For many of the new climate change policies, modelling work has been undertaken as part of their supporting Impact Assessments – an overview (including any discussion of energy security impacts) is given in Appendix C.

This section focuses on the following policies for which the Commission has provided the PRIMES modelling results to 2030:

• Baseline scenario (BL): the business as-usual scenario of DG-TREN of end November 2007 (NTUA, 2008).

• Climate Package scenario - EC Proposal with CDM and with RES trading (NSAT-CDM): scenario corresponding to the effort sharing scheme proposed by the EC which meets the EU’s 2020 targets separately in the EU - for the EU ETS, Non-ETS and RES targets. Exchange of GOs (Guarantees of Origin) among the Member-States is allowed, resulting in RES developing differently from RES obligations by Member-State, but overall RES developing on a cost effective basis. There is also the possibility to take emission credits from CDM lowering the carbon value to a uniform price of 30€/tCO2 (NTUA, 2008).

• CCS scenario - No mandatory CCS scenario (RVCVtar-A) with 2020 RES/GHG targets: the scenario assumes that the 20% GHG and 20% RES targets are met in 2020. There is no mandatory CCS requirement or capture-ready obligation (NTUA, 2008b).

It should be noted that both the climate package and CCS scenario effectively implement a complete set of the generic climate policy mitigation options outlined in Table 3 - 4. This is because both contain the impact of the GHG and RES targets to 2020, but both also contain a small amount of CCS, albeit in later years. Under the climate package around 4% of CO2 emissions from heat and power (all autoproducers and public thermal plant) are captured in 2030 (with nothing before this point). Under the CCS scenario, capture starts earlier (1% and 2% in 2020 and 2025, respectively), but rises more rapidly to 15% in 2030. Therefore, the evolution of the energy system

159


under the climate package and CCS scenario is similar in a number of respects in the early scenario period from 2010-2020.

Other policies

As discussed in section 5.2.3, given the prominence of PRIMES in this modelling work particularly for the Climate Package and CCS policies, data availability from its outputs has been a key consideration in the development of the indicators. For many of the other IA’s of new climate policies the modelling work is more limited, or targeted towards specific sectors (e.g. TREMOVE to estimate the impact of the new CO2 policy for cars), and so does not provide sufficient data to calculate all (or in some cases any) of the indicators41. There would also be a need to translate the more targeted modelling results (e.g. TREMOVE’s energy demand by fuel type in the transport sector) to overall energy system results by adjusting the relevant portion of a typical baseline scenario’s results, such as those from PRIMES.

For other policy options, such as the 10% biofuels target, a separate PRIMES scenario is not available as this element is integrated within the wider 20% RES 2020 target. A more manual comparison between a baseline and 20% RES target policy scenario, looking at the difference in impact on fuel demand in transport only, could be undertaken (the overall effect on the indicator trends would be similar to those for the CO2 policy in cars41). But, an individual, fully consistent scenario, focusing on one policy impact only would be preferable to be able to more accurately explore its effects.

6.2 Other input data

In addition to the PRIMES data a range of other input data or underlying assumptions are also required as part of the implementation of the indicators. These are currently fixed across all scenarios and in some cases estimates or illustrative values are used, which are described below.

It is recommended that these are updated as part of further work (see section 8.4), and in some cases means that the indicator results should also be viewed as illustrative (for some their impact on the results is described within the subsequent results sections as part of sensitivity analysis).

41 In this case the most relevant policy impact is the reduction in oil demand for road vehicles. It would

therefore only impact on two indicators: a) The SRA indicator for oil in the case of extreme events - i.e. it

would reduce the potential daily peak supply shortfall for oil and the level of storage capacity necessary to

provide the required benchmark days of coverage. (b) It would also lower the vulnerability to the RCPI

(Resource Concentration Price Indicator) as the share of oil consumption in total primary energy would

decline – reducing the EU’s expose to oil price risks.

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6.2.1 Extreme events

Extreme weather – impact on energy demand

PRIMES provides data to calculate the average annual consumption for oil and gas, but these need to be converted to peak daily demand figures for a typical or extreme winter for use in the SRA (Short Run Availability) of primary fuels indicator. Two ratios (for ‘typical’ winter and ‘extreme’ winter) have been calculated for each fuel, which are then held constant over time.

These are estimated for each MS from historic monthly oil and gas data from Eurostat for consumption and Heating Degree Days (HDDs), during winter months from 1985 to 2008. Peak demands in a typical winter period are estimated by looking at average monthly peaks climate corrected against the long-run average HDDs over the period available. Extreme winter peaks are estimated by further scaling the typical peak by the difference between the long-run average HDDs and the maximum HDDs seen in the period.

A similar process is used for estimating the peak electricity demand within the SRA – DEPCM (De-rated electricity peak capacity margin) indicator, however, in this case PRIMES provides a direct figure for this in the outputs, so an estimate is only needed for the additional ratio comparing a typical to extreme winter.

One difficulty with the calculation of the peak winter ratios is the link to the demand for space heating, which is the primary area that will be affected by the extreme winter weather event. Whilst the fluctuations in the monthly figures do account for the change in space heating demand as the temperature changes the link to the HDDs is more indirect. Scaling consumption for space heating only by the change in HDDs would be more accurate, however, estimates of the share of space heating by fuel type in total consumption across all MSs are fairly limited42.

This is more important moving forwards where space heating requirements are likely to change significantly (particularly due to improved energy efficiency under a with climate policy scenario). PRIMES does provide an output parameter for heating and cooling (including cooking) demand in the domestic and services sectors. To provide a proxy for the change in space heating requirements, the change in the share of this parameter within total domestic and services energy consumption (which does vary in the different scenarios) is used to further scale the peak demand ratios calculated above.

Other extreme events – impacts on energy supply

For the SRA primary fuels indicator the following underlying data is used:

• Benchmark days of oil storage: this is simply the number of days for which sufficient storage of oil should be available to cover the daily peak supply shortfall. As a default this has been set to 90 days in line with the period that IEA member countries are supposed to provide (note that the IEA

42 Some is e.g. available from source such as the Odyssee Database http://www.odyssee-indicators.org/

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requirement refers to average daily consumption where the indicator value relates to the daily peak shortfall only). This value is fixed in all years.

• Reliance on largest single supplier / route:

- For each MS the largest single (non-EU) supplier of oil and gas has been calculated from historic data43 to provide an estimate of the typical daily supply that would be lost. Data for the most recent year (2007) is held constant for future years. This may change over time as dependence on particular suppliers changes (PRIMES only provides data for total net imports by fuel, but not the country of origin), and would need to be examined further in conjunction with projection data for the ESIC calculation described in section 6.2.3.

- For the largest single supply route to each MS 2009 data for existing infrastructure has been used. For gas this is based on data from Gas Transmission Europe44, which provides maximum daily pipeline capacities. For oil the figures are based on the largest daily pipeline or refinery capacity45. As per the largest supplier the most recent historic data is held constant over time. Future work could potentially update this to reflected planned new capacity, where this is larger

For the SRA – DEPCM (De-rated electricity peak capacity margin) indicator the following underlying data is used:

• Reliance on the largest single plant: this is based on the current largest single electricity plant in each MS (e.g. Drax in the UK). This value is assumed to remain constant over time as PRIMES only provide data of aggregate installed capacity by type as opposed to details of individual new plants. This could be updated if future plants with larger capacities are developed or given the retirement of the largest existing plant.

• Additional de-rating assumptions: in developing additional de-rating (in the face of gas supply shortages) the following assumptions are held fixed over all years:

- Minimum days coverage – based on the assumption that the system operator has a target number of days’ coverage for gas, reflecting the expected duration of an extreme cold weather period. As a default this is assumed to be 10 days.

- Proportion of daily operation of gas peaking plant. As a default this has been set to 50% (i.e. average gas plant operates for 12 hours per day). Further work could examine how gas plant is used currently within each MS

43 Based on data from Eurostat and the UN Comtrade database 44 http://www.gie.eu.com/maps_data/capacity.html 45 The largest refinery capacities are used for all countries except UK (Norpipe from Norway), and Germany,

Poland and Slovakia (Druzhba). Pipeline capacity for Druzhba to Czech republic and Hungary similar to

refinery capacity

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(e.g. based on system operator data) and estimates of how this may change in future46.

Both extreme weather and other extreme events

The SRA (Short Run Availability) of primary fuels indicator also looks at daily net imports and domestic production as part of the calculation of a daily peak supply shortfall. PRIMES only provides annual data for these values, which are currently set to average daily figures by dividing by 365. Peak ratio factors have been incorporated into the spreadsheet tool to be able to adjust peak daily supply, however, they are currently set to 100% (i.e. the average daily figure is used). In the case of ‘other extreme events’ this is a reasonable assumption as the event is likely to be random/unpredictable in nature so there is limited time to increase such supply options. However, for extreme cold weather, the periods over which they are likely to occur are more predictable. The indicator implicitly assumes that storage facilities are maintained at their maximum levels, however, with appropriate planning there may be some flexibility to increase daily net imports and domestic production further and hence the peak ratios would be >100%. This should be investigated on a country by country basis as part of further work.

6.2.2 Load balancing failure

For the flexibility margin indicator, the following assumptions have been used:

• Cold start and spinning/warm ramp rates (% of capacity available in 1 hour) for oil, gas, coal and biomass-waste electricity plant types have been estimated from available literature for a ‘typical’ plant47. For lake/reservoir hydro the ramp-rate is assumed to be 100% (no contribution to flexibility is assumed for run of river hydro). The availability of these plant types to contribute to the flexibility margin depends in part on how much of the plant type is already being used i.e. on the time of the day flexibility is needed. For gas and oil it has been assumed to be the same as their capacity credits (see section 6.2.4), but for coal and biomass it is assumed that at certain times of the day there availability would be close to zero so an average value of 20% was used as an illustration. It is assumed that nuclear plant does not operate in peak power generation mode and so its availability is effectively zero48. These ramp rates and availability factors are assumed to be constant across all MSs and all years.

• Hourly wind swings (as a % of available capacity) have been estimated based on UK historical data for a range of scenarios (maximum, 1 in 365, 1%

46 Whilst it does not form part of the standard outputs, PRIMES models the intra-day demand peaks for

different time segments in typical winter and summer days, hence underlying calculations are undertaken

for how different types of electricity plant are utilised to meet peak demand. 47 E.g. Combined Cycle Gas Turbine of class “F” with a capacity of 800 MWe 48 Even if some of the newer reactor designs such as the EPR (European Pressurised water Reactor) will

have some ramping capability.

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probability). Again, these factors are currently assumed constant across all MSs and all years.

• The maximum hourly electricity demand increase has been estimated using historic hourly load profile data from the various system operators49. These values are held constant across all years. Data was not available for Ireland and Malta, and the same values for GB and Cyprus, respectively, were used in lieu of other data.

6.2.3 Supply shortfall associated with resource concentration

Markets characterized by an effective price mechanism

For the RCPI (Resource Concentration Price Indicator) it is necessary to calculate the ESMC (Energy Security Market Concentration) measure for each primary fuel market (oil, coal and natural gas) in each year. Historic calculations are based on IEA and Eurostat data (using a measure of net export potential as outlined in section 5.7.3.1), and projections are currently based on data in IEA (2007) and are held constant across scenarios (i.e. under the assumption that EU climate policy does not effect the future development of international fossil fuel suppliers). Ideally, this should be updated using supply and consumption data at the global level that is more closely correlated with the rest of the energy modelling work of PRIMES. This may require defining methods to disaggregate regional data at the country level. Alternatively, the latest IEA World Energy Model data and alternative scenarios could in theory be implemented to reflect the impact of EU climate policy under a situation where a future global reduction agreement is also reached (e.g. a 450ppm stabilization target scenario) as this would likely have a considerable impact on international supply of fossil fuels.

Markets with no or limited price mechanism

For the RCPAI (Resource Concentration Physical Availability Indicator) it is necessary to calculate the ESIC (Energy Security Import Concentration) of net imports of natural gas to each country, based on the origin of supply.

The ESIC is currently only calculated for 2008 based on data on pipeline and LNG movements from the BP Statistical Review of World Energy 200950. The ESIC is then held constant over time. PRIMES only provides data on how net imports of gas will change in future, but does not provide information on the origin of supply. Additional assumptions (e.g. from global energy system models such as POLES or GEM-E3 would be needed to understand how the dependence on particular countries will change out to 2030).

49 Primarily from the new ENTSO-E (European Network of Transmission System Operators for Electricity

http://www.entsoe.eu/ - which incorporates previous sets of operators/groups UCTE, NORDEL, BALTSO,

UKTSOA, ATSOI) and the Cyprus Transmission System Operator.

50 http://www.bp.com/productlanding.do?categoryId=6929&contentId=7044622

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Both RCPI and RCPAI indicators

There are two other sets of assumptions, which are fixed across scenarios for both of the resource concentration indicators:

• Political stability: as per IEA (2007) we have utilized the World Bank Governance Indicators51. However, given the inherent subjectivity in such measures and difficulty in relating specific metrics to energy security risks, we have chosen to use the aggregate country measure (composed of all 6 sub-indicators as opposed to just the 2 used in the previous IEA work). Historic data is available for most years from 1996 to 2007, but the values are assumed to remain constant from 2007 onwards as no projections are available.

• Share of gas purchased on spot or spot derived markets. This is used in the RCPI to split the share of gas exposed to the gas price risk (as estimated by the gas ESMC) and the share exposed to the oil-price risk, due to gas being purchased under oil-indexed contracts (as estimated by the oil ESMC). It is also used within the RCPAI indicator, as only the share of gas not purchased on spot or spot derived markets is exposed to the physical unavailability risk as estimated by the ESIC metric. It is assumed that the gas markets for EU countries develop in future towards greater purchase of gas on spot or spot derived markets. These are linked to a number of illustrative assumptions for the market in 2010 and beyond:

- UK National Balancing Point (trading hub) (75% share)

- Currently developing trading hub (25%) – e.g. AT, BE, FR, DE, IT, NL.

- Neighbour to developing hub (5%)

- Other (0%)

- Assume a step up in maturity (~25%) every 10 years such that by 2030 most MSs are purchasing around half / three-quarters of gas on spot or spot derived markets.

6.2.4 Cross-cutting

There are two other sets of key assumptions, which underpin more than one indicator.

Capacity credit

Capacity Credit is a vital determinant of security of supply metrics. It measures the amount of output from a considered power source that can statistically be shown to contribute to the peak demand. In addition to the load factor of the considered technology, factors such as penetration rate and intermittency of source also effect values of capacity credit. These values are used to convert available nameplate capacity from PRIMES to de-rated capacity, which is utilized in the DECPM (de-rated electricity peak capacity margin) for both the extreme events and load balancing failure root causes.

51 http://info.worldbank.org/governance/wgi/index.asp

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Estimation of capacity credits was carried out for technologies on a MS basis, with the values held constant across all years. Underlying calculations and assumptions made in the process are summarised below. We have drawn on published studies where possible, but it should be noted that there are very disparate methodologies and approaches for calculating capacity credits and as such there is inevitably a degree of inconsistency between the resulting data set:

• Wind Power: capacity credits for European countries for various penetration levels were sourced52. Reported values were calculated by interpolating against realised 2007 penetration levels in respective countries. For the countries where no capacity credit data was available, proxies were used; for example Germany was used as a proxy for Austria and average of Germany and Greece in case of Romania. In case of Portugal, the data available was not against a comparable benchmark and hence Spain was used as a proxy. Given the presently low levels of offshore penetration at 1.4% of the total EU wind capacity and 3.4% of the total output, it was assumed that the overall capacity credit approximately represented the onshore capacity credit. The offshore capacity credit was calculated by scaling up the onshore credit by a factor of three-fifths as per the conclusions of an IEA study on wind power integration (IEA, 2008). The offshore capacity credits were, however, capped at 38%, which was the highest figure reported in any concerted study on the subject. Other higher figures were encountered, but these corresponded to a particular season of high winds only (such as winters in Northern Europe).

• Solar: Germany and Spain are forerunners in solar power generation in Europe. An average capacity factor for major installations in the two countries was calculated. Taking account of the diurnal and seasonal variations in solar insolation, difference in performance of technology of installations (Solar photo-voltaic or solar thermal) and the presently low levels of market penetration, capacity credit was taken as half the average capacity factor. Germany was used as proxy for countries in northern and central Europe and Spain for southern Europe.

• Geo-thermal: Once running, geothermal facilities can be operated continuously. Hence the capacity credit of the geo-thermal plants is likely to be very close to their availability. The values sourced fell in wide range of 75% to over 90%. A mid-point value of 80% was chosen across the board.

• Nuclear: Load factors for countries with active nuclear power generation facilities were sourced. Taking account of the fact that most countries use nuclear power in a base-load supply mode (or load-matching mode in case of France) and assuming planned outages were scheduled in periods with manageable load, adjustment was made to scale up the load factor to reflect only unplanned outages. For this a planned outage rate of 7 % a year was used. In case of countries with no active nuclear facility, a capacity credit of

52 As the share of wind in electricity generation capacity from the PRIMES outputs increases, the

corresponding capacity credit declines (also see section Appendix B - B.2.1.2)

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90% assuming new capacity would come from new (more reliable) developments.

Capacity credits for other generating technologies (hydro, fossil plant and biomass-fired) were also estimated from available historic data and high-level assumptions about the likelihood of unplanned outages. For the ‘other renewables’ capacity type in PRIMES (primarily tidal or wave power) a default value of 30% has been incorporated.

Gas storage

Available gas storage capacity and maximum daily withdrawal rates are used in both the:

• Extreme events: SRA – primary fuels indicator, to calculate days of available coverage of a peak supply shortfall, and the;

• Supply shortfall associated with resource concentration: RCPAI indicator. To calculate the minimum share of the net imports of gas in total primary energy (purchased under regulated contracts) not covered by available storage capacity.

GIE (Gas Infrastructure Europe) data for each MS53 has been used. Values for 1990 to 2009 are assumed to be fixed at 2009 capacities. Future estimates of new capacity are based on currently planned projects – it is assumed that all projects are completed by 2030, with capacity increasing in a linear manner (in the absence of other information).

6.3 Development of spreadsheet tool

An excel-based spreadsheet tool has been developed as part of this project to implement the indicators. It has been structured to allow easy updating of the abovementioned PRIMES projections as well as other input data.

The indicators have been constructed based on annual historic energy balance data54 from 1990 to 2006/7 (where available), with the first projection results starting in 2010, and the other input data/assumptions as described in section 6.255. Projections are then provided in 5-yearly intervals to 2030. Intermediate years have been linearly interpolated.

A flexible pivot chart and graph sheet allows the user to:

• Select the combination of indicators (also by stages and fuel type), scenarios and MSs to be displayed.

- The selection of MSs includes two user-selectable groupings – i.e. to examine the impact on regional groups of MSs, where this is more appropriate due to infrastructure integration (see section 5.3.2).

53 http://www.gie.eu.com/maps_data/index.html 54 Primarily based on Eurostat and IEA data 55 Contained within the yellow sheets within the main spreadsheet tool. These assumptions can be changed

by the user and will carry through into the final calculations.

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• Quickly alter a small number of variable factors and see the resulting impacts. These include:

- For the indicators related to the ‘supply shortfall associated with resource concentration’ root cause: the effect of including political risk ratings of supplier countries and treating OPEC as a single supplier or individual countries.

- For the extreme events root cause indicators, the ability to alter the peak demand for oil, gas and electricity to reflect a ‘typical’ or ‘extreme’ winter weather event. In response to an extreme event affecting energy supply, the ability to alter the available peak supply of oil and gas to reflect the loss of the largest supplier or supplier route, and for electricity the loss of the largest generating plant. It should be noted that it is possible to combine both these factors in the same tool to examine a “worst-case”-type scenario.

- For the load balancing root cause - factors which affect the flexibility margin indicator; including the severity of the drop in intermittent wind generation and whether the ramp rates of dispatchable plant reflect cold-start or warm-start/spinning reserve.

- Where relevant the ability to change the Stage IV indicator to reflect the alternative Stage IV - adjusted share of primary fuel in final energy consumption.

6.3.1 Calculation of Stage IV under the baseline and with policy scenario

The stage IV proxies within the indicators are currently based on the share of consumption of an energy type within total primary or final energy consumption. However, it is important to note that in all cases the denominator is the total consumption under the baseline scenario – i.e. in its generic form:

• Earlier Stages ⎟⎟⎠

⎞⎜⎜⎝

⎛

scenarioBaseline

baselineincludingscenarioAny

nconsumptioenergyfinalprimaryTotalXtypeenergyofnConsumptio

/* )(

Stage IV provides a proxy for the vulnerability at the demand side, hence an increasing share of consumption increases the overall vulnerability of the country to a price or physical unavailability impact at the supply side. However, if the total consumption within a policy scenario is used as the denominator a situation could arise whereby both the consumption of energy type X and total consumption increase in the same scenario – i.e. the share remains unchanged even though the vulnerability has actually increased.

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6.3.2 Limitations in the current implementation of the proposed indicators

Due to confidentiality issues, it was not possible to incorporate some of the PRIMES output data under this project. This means that it has not been possible to calculate the following indicators:

• Capital intensity indicator.

• Required new capacity indicator in €M terms.

In addition, due to limitations on resources and data availability, it was not possible to calculate a number of the proposed components in the proposed indicators outlined in section 5:

• For the insufficient investment metrics at Stage III (see section 5.5.5) the measure of (spare) Peak Interconnector Margin.

• For the (RCPI) Resource Concentration Price Indicator (markets characterized by an effective price mechanism) at Stage III (see section 5.7.3.3) the potential fuel quality flexibility parameter and liquidity parameter.

• For both the RCPI and RCPAI indicators at Stage IV the minimum primary fuel demand accounting for multi-firing capability in heat and power generation (although the RCPI Stage IV is still adjusted to reflect available gas storage capacity).

These are discussed within the context of areas for further work in section 8.4.

6.3.3 Introduction to results

The following sections provide the initial results focused primarily on the EU-27 (subject to the assumptions and limitations described above) for the indicators under each of the available PRIMES scenarios. Examples of individual MSs and key country groupings are also provided where relevant.

Individual key stages of the indicators are presented, as well as the overall indicator as a more complete measure of vulnerability (e.g. including the effect at the demand side at Stage IV).

A number of sensitivities are also examined by exploring the different variable factors mentioned above.

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6.4 Results – Extreme events

6.4.1 Overall Short-Run Availability (primary fuel) oil

6.4.1.1 Stage II

The figure below shows the DPSS (i.e. where peak demand exceeds available peak supply) for oil for an extreme winter situation (increasing peak daily demand). Primary production of oil for the EU-27 follows a similar (declining) trend across all three scenarios, hence it is the change in absolute demand for oil that is the key driver.

In this case there is a reduction in the total absolute oil demand under each of the PRIMES scenarios, with the most significant reduction seen under the CCS scenario followed by the climate package and baseline case. As absolute demand decreases the peak winter demand also decreases leading to a smaller DPSS over time, and therefore a decrease in the EU’s vulnerability.

Illustration 6 - 7 SRA (primary fuels) indicator – Oil – Stage II - Extreme winter

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6.4.1.2 Stage III

The figure below shows the Short Run Availability (primary fuels) indicator for oil at Stage III for the EU-27. It shows the total storage capacity of oil required to cover a benchmark number of days (in this case 90) worth of coverage of any DPSS (daily peak supply shortfall). The situation shown is that for an extreme winter, and indicates a decreasing requirement for storage from 2010 to 2030 (i.e. a decrease in vulnerability) and follows the same trend as Stage II.

Illustration 6 - 8 SRA (primary fuels) indicator – Oil – Stage III - Extreme winter

Total storage capacity needed to provide benchmark days coverage of shortfall

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Under a typical winter scenario, the lower level of peak demand, coupled with the general reduction in oil demand over time means that by 2015 there is no visible DPSS and hence storage capacity required also becomes zero.

The impact of an extreme event on the supply side can be seen in the figure below. Here a typical winter peak demand is assumed, but coupled with the loss of the largest single oil supply route56 into the EU (Norpipe). In this situation the loss of import capacity leads to a DPSS, which gradually decreases over time to 2030 (zero under the CCS and Climate Package scenarios) as the absolute level of oil demand declines. Under the situation of an extreme winter coupled with the loss of the largest supply route (not shown) this would add around 10,000 ktoe of required oil storage under each of the scenarios to the 2030 value shown in the previous figure.

56 Note that the maximum possible daily supply from this route is currently held fixed in all years.

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Illustration 6 - 9 SRA (primary fuels) indicator – Oil – Stage III – Typical winter and loss of largest supply route

Total storage capacity needed to provide benchmark days coverage of shortfall

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6.4.1.3 Stage IV

The figure below shows how the share of total primary oil consumption in each scenario varies as a proportion of total primary energy consumption under the baseline – as a proxy of the EU-27’s vulnerability at the demand side. In a similar manner to the previous section oil consumption (and hence its share) declines most under the CCS scenario to 2030, followed by the Climate Package, with only a gradual decline under the baseline.

The alternative Stage IV indicator (not shown), for the adjusted share of primary fuel in FEC (final energy consumption) accounting for heat and power transformation efficiency, has a very similar trend to that shown below. However, the shares in 2030 are around 3-5% higher, reflecting the lower transformation efficiency of oil use for heat and power relative to the average across all fuels in this year.

Illustration 6 - 10 SRA (primary fuels) indicator – Oil – Stage IV

Share in primary energy

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The figure shows the overall SRA indicator for oil (under the situation with an extreme winter impacting peak demand but no loss of the largest supply route), which is effectively the required oil storage capacity at Stage III, scaled by the vulnerability at the demand-side at Stage III. As both the long-term Stage III and Stage IV trends are declining these reinforce each other within the overall indicator, leading to a stronger decrease in the EU’s vulnerability to 2030 under all of the scenarios.

Illustration 6 - 11 SRA (primary fuels) indicator – Oil – overall indicator – Extreme winter

Overall indicator

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6.4.2 Overall Short-Run Availability indicator - Gas

6.4.2.1 Stage II

The figure below shows the DPSS (i.e. where peak demand exceeds available peak supply) for gas for an extreme winter situation (increasing peak daily demand). Primary production of gas for the EU-27 follows a similar (declining) trend across all three scenarios, hence it is the change in absolute demand for gas that is the key driver. This declines most in absolute terms under the CCS scenario, less so under the Climate Package scenario and continues to increase gradually to 2030 under the baseline case. As absolute demand decreases the peak winter demand also decreases leading to a smaller DPSS over time, and therefore a decrease in the EU’s vulnerability.

Illustration 6 - 12 SRA (primary fuels) indicator – Gas – Stage II – Extreme winter


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6.4.2.2 Stage III

The next figure below takes the DPSS shown in the previous section and compares it with the available gas storage capacity to estimate the number of days of short-run availability of supply provide by this storage. Available gas storage capacity for the EU-27 is shown in section 6.7.2.2, but effectively doubles by 2030, assuming all planned capacity is built by this point57. This positive increase in storage capacity, coupled with a decrease in the DPSS at Stage II leads to a strong positive increase in days of coverage. Even for the baseline case where the DPSS only plateaus over time, the increase in storage capacity helps to reduce the EU-27’s vulnerability.

The indicator at stage III also compares the DPSS with the maximum daily withdrawal rate, where the DPSS exceeds this the short-run availability is set to zero – i.e. even though sufficient total capacity may exist the maximum amount that can be withdrawn may not be able to cover the supply shortfall. In this case the figure shows that the daily withdrawal rates are sufficient.

Illustration 6 - 13 SRA (primary fuels) indicator – Gas – Stage III – Extreme winter

Complete daily coverage (given gas storage withdrawal rates) - short-run availability of supply

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To check the sensitivity, it is also possible to look at the impact, given the loss of the largest single supply (EUStream into Slovakia from Russia via Ukraine58), route coupled with a typical winter, in the figure below. Here the SRA in days is higher than in the previous case, indicating that the loss of the largest supply route has a smaller

57 This is unlikely, and therefore the days of coverage will be fewer than those modelled in the graph. 58 It is currently assumed the shortfall in capacity from this route remains constant across all years (i.e. a

larger capacity pipeline is not built).

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effect on the DPSS than the difference between a typical and extreme winter peak demand.

Illustration 6 - 14 SRA (primary fuels) indicator – Gas – Stage III – Typical winter – loss of largest supply route


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However, looking at the indicator for the EU-27 as whole effectively aggregates the DPSSs and available storage capacities across all MSs. This assumes that the available gas storage can be utilised by the appropriate country where the DPSS occurring, and that physical connectivity or other technical factors such as withdrawal rates are not a limiting factor.

Member States / Country groupings

It is therefore also important in this case to look at the impact on individual MSs or groups that have integrated infrastructure such as those described in section 5.3.2. For example, under the extreme winter case (without the loss of the largest supplier/route) the number of days SRA is zero (under all scenarios) for the country grouping of BG/GR/RO. For UK/IE it is zero until 2010, when it rises steadily (driven by a decline in primary gas consumption under all scenarios).

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Illustration 6 - 15 SRA (primary fuels) indicator – Gas – Stage III – Extreme winter – various country groupings


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This means that there is sizeable total storage capacity in both cases, however, it appears that maximum daily withdrawal rate is the limiting factor, with the DPSS exceeding this in BG/GR/RO. In the UK after 2010 the DPSS declines slightly and gas storage also increases, this pushes the maximum daily withdrawal rate above the DPSS and SRA becomes non-zero.

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6.4.2.3 Stage IV

The figure below shows how the complement of the share of gas consumption under the baseline changes relative to the total primary energy consumption under the baseline scenario. In the baseline scenario the share of gas consumption (as well as absolute gas consumption) in the EU-27 continues to increase (i.e. the complement decreases) whilst in the climate package and CCS scenario both the share and absolute level of gas consumption decrease from 2010 to 2030.

Illustration 6 - 16 SRA (primary fuels) indicator – Gas – Stage IV

Complement of Share in primary energy

70%

72%

74%

76%

78%

80%

82%

84%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year


Indicator Short Run Availability (primary fuels) Energy type Gas Stage IV Y-axis Value

Data

Scenario

Member State

◄▬▬▬

In

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The alternative Stage IV indicator, shown below, for the adjusted share of primary fuel in FEC (final energy consumption) accounting for heat and power transformation efficiency, has a similar trend to the standard Stage IV. However, the complement of the shares in 2030 is lower overall, indicating higher vulnerability – i.e. due primarily to the importance of gas in electricity generation. This increased vulnerability is more pronounced under the baseline scenario (e.g. ~3% in 2030), but less so under the non-baseline scenarios (~2% in 2030), due to more rapid improvements in gas electricity generating efficiency.

179


Illustration 6 - 17 SRA (primary fuels) indicator – Gas – Alternative Stage IV

Complement of (Heat and power transformation efficiency) adjusted share in FEC

67%

68%

69%

70%

71%

72%

73%

74%

75%

76%

77%

78%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year


Indicator Short Run Availability (primary fuels) Energy type Gas Stage IV Y-axis Value

Data

Scenario

Member State

◄▬▬▬

In

cre

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▬▬◄

180



The SRA of gas in days at Stage III (under the extreme winter case – no loss of key supplier/route) scaled by the complement of the share of gas in primary energy at Stage IV is shown below. From 2010 onwards, under the climate package and CCS scenario both the Stage III and Stage IV show positive trends (i.e. decreasing vulnerability) which reinforce each other. In the baseline case the improving trend at Stage III is offset to some degree by the deterioration seen in Stage IV, but the overall vulnerability is still decreasing, albeit more slowly than the other scenarios.

Illustration 6 - 18 SRA (primary fuels) indicator – Gas – overall indicator – Extreme winter

Overall indicator

0

20

40

60

80

100

120

140

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

(Sta

ge I

V s

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d) d

ays


Indicator Short Run Availability (primary fuels) Energy type Gas Stage All Y-axis Value

Data

Scenario

Member State

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6.4.3 (Further) De-rated Electricity Peak Capacity Margin

6.4.3.1 Stage II

The figure below shows the DECPM (De-rated Electricity Peak Capacity Margin) for the EU-27 under the extreme winter case, leading to increase in peak demand (an example under a typical winter situation is shown in section 6.6.1)59. Note that a complete set of historic data is only available from 2000 onwards.

The DECPM is a measure of the surplus (de-rated) generating capacity above peak electricity demand, with lower values indicating increasing vulnerability to a load balancing failure and values approaching or below zero indicate that there may not be sufficient available capacity to meet peak demand – shown occurring in ~2017 in the

59 note that the DECPM would look much worse in case of an extreme winter compared to a typical winter

181


baseline case. Care must be taken in the interpretation of the DECPM and this is discussed further in section 6.8.

Illustration 6 - 19 De-rated Electricity Peak Capacity Margin (DEPCM) indicator – Stage II – Extreme winter

De-rated peak capacity margin

-4%

-2%

0%

2%

4%

6%

8%

10%

12%

14%

16%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year


Indicator (Further) De-rated Electricity Peak Capacity Margin (DEPCM) Energy type Electricity Stage II Y-axis Value

Data

Scenario

Member State

◄▬▬▬

In

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▬▬◄

There are therefore two opposing drivers affecting the DECPM as you move towards 2030:

• Peak electricity demand increases under all 3 scenarios but this increase is most significant under the baseline, followed by the climate package and then the CCS scenario. However, the evolution of peak demand is quite different. Under the baseline, a continuous steady increase is projected from ~2005 through to 2030. In the CCS and climate package scenarios the increase is more limited to 2015-2020 (due to the effect of policy), before rising more rapidly again to 2030. In 2010, where the DEPCM is highest for the CCS scenario, peak electricity demand is ~1% lower than under the package.

• The total level of de-rated capacity on the system, subject to the fixed assumptions made on capacity credit for each generating type outlined in section 6.2.4. The composition of different generating technologies in terms of their nameplate capacity and the total de-rated capacity is outlined in Table 6 - 17.

Whilst the total de-rated capacity rises in all scenarios, this is insufficient under the baseline to maintain the DEPCM, given the rapidly rising peak demand. Under the other scenarios, the increase in de-rated capacity coupled with a slower increase in peak demand (more so under the CCS scenario) leads to an increase in the DEPCM around 2010-2015 before starting to decline again.

182


Table 6 - 17 Projections of installed nameplate capacity and total de-rated capacity under each

scenario – EU-27

Baseline CCS Climate Package GW (Nameplate)

2010 2020 2030 2010 2020 2030 2010 2020 2030

Nuclear 125 113 102 126 114 107 125 112 105

Hydro 111 114 116 111 114 115 112 114 116

Wind on-shore 68 112 129 76 132 183 77 132 190

Wind off-shore 4 9 17 7 28 70 7 30 72

Solar 4 9 15 4 14 41 4 13 44

Other RES 0 1 2 0 4 6 0 4 6

Solids 188 186 188 188 148 128 184 153 139

Oil 68 38 31 65 35 17 65 35 17

Gas 244 282 313 234 235 298 233 235 293

Biomass-waste 23 36 51 24 70 112 25 85 138

Fuel cells 0 0 0 0 0 0 0 0 0

Geothermal heat 1 1 1 1 1 1 1 1 1

Total Nameplate 835 901 966 837 894 1077 833 916 1120

Total (de-rated) capacity 662 680 715 656 641 720 652 659 752

It is also possible to look at the decrease in the DEPCM with the loss of the largest single generating plant, the figure below provides the example (still under the extreme winter case) with the loss of Gravelines nuclear plant in France (5.5 GW).

183


Illustration 6 - 20 De-rated Electricity Peak Capacity Margin (DEPCM) indicator – Stage II – Extreme winter and loss of largest single plant


-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

12%

14%

16%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year


Indicator (Further) De-rated Electricity Peak Capacity Margin (DEPCM) Energy type Electricity Stage II Y-axis Value

Data

Scenario

Member State

◄▬▬▬

In

cre

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▬▬◄


As per the SRA indicator for gas in section 1.1.1.1, the overall DECPM may be misleading when viewed at the EU-27 level. What is more important is the ability of individual MSs or groups of MSs with integrated electricity networks to maintain a sufficient margin to meet peak demand and avoid load balancing issues – i.e. where they share a synchronous grid. Examples of the DEPCM under a more ‘typical’ winter situation are provided in section 6.6.1.

6.4.3.2 Stage III

Stage III of the indicator provides an estimate of the link between the SRA of gas in an extreme event and how the impact of any potential shortfall in primary fuel supply limits gas electricity generating capacity. Where there is insufficient gas this can potentially translate into a further de-rating of the DEPCM.

At the EU-27 level under an extreme winter and with the loss of the largest supply route, there is no additional rating under any of the scenarios. However, it is again more important to look at individual MSs or groupings.

The further de-rating is a function of the gas DPSS, available storage capacity, the coverage period required (i.e. the likely period of a cold-weather spell, currently set to 10 days) and the typical daily operation of gas plant (currently set to 50% - i.e. 12 hours operation per day).

184


Under an extreme winter case (assuming no loss from the largest supply route), some illustrative losses of capacity under the baseline scenario include:

• UK/IE - 6GW in 2010, dropping to <1 GW from 2015 onwards.

• BENELUX – 3-5 GW in 2010 dropping to 2-3 GW moving forwards.

• GR/BG/RO – 4-5 GW dropping to < 4 GW moving forwards.

Under the CCS and climate package scenarios these figures are typically reduced by one third.

These figures are of course a simplification and are also dependent on the key assumptions such as gas plant operation. It also does not consider the availability of on-site distillate backup – for example, as shown in Appendix B - B.1.1.2 the UK has sufficient back to maintain 5 to 2 GW of CCGT capacity (on a declining trend) over a period of up to 10 days.

It is recommended that this is explored in more detail as part of further work – i.e. identifying what share of gas plants are used for peak or base load in each MS. However, the indicator still provides a useful high-level proxy of a potentially important indirect vulnerability.

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6.4.3.3 Stage IV

Stage IV provides a measure of the vulnerability at the demand-side based on the (complement of) the share of electricity consumption in each scenario as a proportion of the FEC (final energy consumption) under the baseline.

As shown in the figure below - in the baseline scenario both the absolute level of electricity consumption and the share of electricity continue to increase – leading to an increase in vulnerability. Under the CCS and climate package scenarios electricity consumption still increases in absolute terms, but the rate of growth is slower between 2010 and 2020, as a result of policy impacts, leading to a smaller share as a proportion of total FEC under the baseline. From 2020 onwards the consumption of electricity starts to increase again and at a faster rate than the baseline – leading to a gradual convergence in shares by around 2030.

Illustration 6 - 21 De-rated Electricity Peak Capacity Margin (DEPCM) indicator – Stage IV

Complement of share of electricity in final consumption

73%

74%

75%

76%

77%

78%

79%

80%

81%

82%

83%

84%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year


Indicator (Further) De-rated Electricity Peak Capacity Margin (DEPCM) Energy type Electricity Stage IV Y-axis Value

Data

Scenario

Member State

◄▬▬▬

In

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▬▬◄

186



The combined impact of the Stage III indicator scaled by that at Stage IV is shown in the figure below and the vulnerability broadly follows the same trend as the DECPM at Stage III – increasing steadily under the baseline, decreasing in the near term under the CCS and climate package scenario before decreasing again.

Illustration 6 - 22 (Further) De-rated Electricity Peak Capacity Margin (DEPCM) indicator – Overall indicator

Overall indicator

-4%

-2%

0%

2%

4%

6%

8%

10%

12%

14%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

(Sta

ge I

V s

cale

d) %


Indicator (Further) De-rated Electricity Peak Capacity Margin (DEPCM) Energy type Electricity Stage All Y-axis Value

Data

Scenario

Member State

◄▬▬▬

In

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▬▬◄

6.5 Results - Insufficient investments in new capacity

As discussed in section 6.3.2, the PRIMES scenario data to calculate the capital intensity or required new capacity in €M terms indicators has not been made available. Only the results for the average load factor and required new capacity in MW terms are presented below

6.5.1 Average load factor

6.5.1.1 Stage I

The figure below shows the average load factor for the EU-27 across all electricity generating plant. Under the baseline this continues to rise from 2010 to 2030, driven by rapidly rising electricity demand and continued use of baseload fossil generation – as shown in section 6.4.3.1. By comparison, the load factor under the CCS and climate package scenario drops from 2007 to 2010 due to a much smaller increase in electricity demand coupled with the gradual increase in installed capacity (seen in all 3 scenarios). Whilst electricity demand under the non-baseline scenarios starts to

187


increase more rapidly from around 2015-2020 onwards the load factor does not start to converge due to the increasing difference in the mix of generating options – particularly more intermittent renewables – shown in Table 6 - 17.

As discussed in section 5.5.3.3, load factor is only a relatively indirect metric for the insufficient investment root cause, and its relevance will be subject to a number of other factors such as the type of policy support (e.g. payments for capacity versus energy only). However, all else being equal it provides a high-level indication of the risk in developing new plant, as revenue streams become more unpredictable as the load factor decreases. Very low marginal cost intermittent renewables will tend to displace conventional plant at times of high output, but require conventional plant to be running at times of low output.

Illustration 6 - 23 Insufficient investment in new capacity - Average load factor – Stage I

Load factor

38%

40%

42%

44%

46%

48%

50%

52%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year


Indicator Average load factor Energy type Electricity Stage I Y-axis Value

Data

Scenario

Member State

◄▬▬▬

In

cre

asi

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vu

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rab

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▬▬◄

The Stage IV component of the indicator is the complement of the share of electricity in total final energy consumption. The results are the same as those shown in section 6.4.3.3.

Country groupings

Given the integration of many national electricity systems it is important to look at groupings of individual Member States that operate as ‘broadly’ synchronous grids. Some illustrative examples for those with lower load factors, particularly under the climate package and CCS scenario, are shown in the figure below.

188


Illustration 6 - 24 Average load factor – Stage I – Country groupings

Load factor

20%

25%

30%

35%

40%

45%

50%

55%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

%


Indicator Average load factor Energy type Electricity Stage I Y-axis Value

Data

Scenario

Member State

◄▬▬▬

In

cre

asi

ng

vu

lne

rab

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▬▬◄

Note: Group (1) = EE/LV/LT, Group (2) = ES/PT

189



The combined impact of the Stage I indicator scaled by that at Stage IV for the EU-27 is shown in the figure below and the vulnerability broadly follows the same trend as the load factor at Stage I. However, as the growth in demand for electricity under the non-baseline scenarios starts to exceed that under the baseline from around 2020 onwards, the relative gap in vulnerability shown in the overall indicator between these scenarios increases towards 2030. i.e. the lower load factor coupled with increasing demand indicates an increased vulnerability to insufficient investment in new capacity.

Illustration 6 - 25 Insufficient investment in new capacity - Average load factor – Overall indicator

Overall indicator

30%

31%

32%

33%

34%

35%

36%

37%

38%

39%

40%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

(Sta

ge I

V s

cale

d) %


Indicator Average load factor Energy type Electricity Stage All Y-axis Value

Data

Scenario

Member State

◄▬▬▬

In

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▬▬◄

6.5.2 Required new capacity (MW)

6.5.2.1 Stage I

The figure below shows the cumulative new (nameplate) capacity required for power generation under each of the scenarios for the EU-27 (including end of life replacement, retrofit and expanded capacity). The results are based solely on the PRIMES projections hence the period from 2005-2010 is based on modelled expectations of new capacity.

The required new capacity is substantial given that net installed capacity in the EU-27 in 2007 was around 800 GW. Interestingly there is little difference in required new capacity between the baseline and other scenarios up to around 2020, indicating that a significant level of end of life replacement will be required in any event, and the

190


issue is primarily about what type of capacity will replace it. Hence there is little difference in vulnerability between the scenarios.

Beyond this point the required new capacity tends to be higher under the climate package scenario, followed by the CCS scenario, due to increasing levels of new intermittent generation with lower capacity credits (hence more nameplate capacity is required to provide the same level of electricity output of the course of the year).

Illustration 6 - 26 Insufficient investment in new capacity – required new capacity (MW) – Stage I

Cumulative new capacity

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

MW Baseline - EU-27


Indicator Required new capacity (MW) Energy type Electricity Stage I Y-axis Value

Data

Scenario

Member State

►▬▬▬

In

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▬▬►

191


6.5.2.2 Stage IV

The component of the indicator at stage IV is simply the share of electricity consumption under each scenario relative to total FEC under the baseline scenario. It is the reverse of the complement of the share of electricity consumption shown in section 6.4.3.3 and so the same underlying drivers of the trends still apply.

Illustration 6 - 27 Insufficient investment in new capacity – required new capacity (MW) – Stage IV

Share of electricity in final consumption

15%

16%

17%

18%

19%

20%

21%

22%

23%

24%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year


Indicator Required new capacity (MW) Energy type Electricity Stage IV Y-axis Value

Data

Scenario

Member State

►▬▬▬

In

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▬▬►

192



The figure below shows the Stage I indicator scaled by the share of electricity in FEC at Stage IV. Whilst the trends are broadly similar, the more limited growth in electricity consumption from 2010 to 2020 under the climate package and CCS scenarios is shown in a slightly lower vulnerability relative to the baseline over this period.

Illustration 6 - 28 Insufficient investment in new capacity – required new capacity (MW) – Overall indicator

Overall indicator

0

50,000

100,000

150,000

200,000

250,000

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

(Sta

ge I

V s

cale

d) M

W


Indicator Required new capacity (MW) Energy type Electricity Stage All Y-axis Value

Data

Scenario

Member State

►▬▬▬

In

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▬▬►

6.6 Results - Load balancing failure

6.6.1 De-rated Electricity Peak Capacity Margin (DEPCM) indicator

6.6.1.1 Stage I

The results of a DECPM-based indicator have already been presented in section 6.4.3, but within the context of an extreme event; an extreme winter cold spell effecting demand, potential loss of the largest plant and a potential shortfall in primary gas availability leading to a further de-rating.

The results shown in the figure below for the EU-27 are under a more ‘typical’ peak situation (i.e. normal cold winter period). The same underlying assumptions on capacity credit described in section 6.2.4 are still used.

The evolution of total electricity generating capacity by type for each scenario is the same as in section 6.4.3. The trend in electricity growth is also the same, but the ‘typical’ peak demand is lower, hence the overall DEPCM follows the same evolution under each scenario but is higher (i.e. lower vulnerability) in all cases. For example, it

193


is still 10% in the baseline scenario by 2030, as opposed to negative in the case of an ‘extreme’ winter.

Illustration 6 - 29 De-rated Electricity Peak Capacity Margin (DEPCM) – Stage I


0%

5%

10%

15%

20%

25%

30%

35%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year


Indicator De-rated Electricity Peak Capacity Margin (DEPCM) Energy type Electricity Stage I Y-axis Value

Data

Scenario

Member State

◄▬▬▬

In

cre

asi

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▬▬◄


As already mentioned the overall DEPCM may be misleading when viewed at the EU-27 level. Looking at groupings of countries with more integrated electricity networks provides a better proxy of the potential for load balancing issues. It also helps to (at least partially) avoid one of the limitations of the current specification of the indicator (due to data availability) – described in section 5.6.3.1 – when looking at individual countries. I.e. how to account for the impact of interconnectors, which may contribute to peak demand provision or even add to the load requirements.

Some examples of the DEPCM for different country groupings are shown in the figures below.

194


Illustration 6 - 30 De-rated Electricity Peak Capacity Margin (DEPCM) – Stage I – various country groupings


-40%

-20%

0%

20%

40%

60%

80%

100%

120%

140%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

%



Data

Scenario

Member State

◄▬▬▬

In

cre

asi

ng

vu

lne

rab

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▬▬◄

Note: Group (1) = UK/IE, Group (2) = ex-BALTSO (EE/LV/LT)

Illustration 6 - 31 De-rated Electricity Peak Capacity Margin (DEPCM) – Stage I – ex-UCTE EU Members


-20%

-10%

0%

10%

20%

30%

40%

50%

60%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

% Baseline - Group(1)CCS - Group(1)Package - Group(1)


Data

Scenario

Member State

◄▬▬▬

In

cre

asi

ng

vu

lne

rab

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▬▬◄

Note: Group (1) = AT, BE, BG, CZ, DK, FR, DE, GR, HU, IT, LU, NL, PL, PT, RO, SL, SK, ES

195


However, the second figure above highlights a potential complication for the current geographical scope of the indicators (and correspondingly projection data) and their interpretation. The ex-Union for the Co-ordination of Transmission of Electricity (UCTE)60 members operates within a synchronous zone. However, in addition to current EU Member States the zone also includes:

• Croatia, Bosnia and Herzegovina, Serbia, Montenegro, Macedonia, Ukraine (small west electricity island) and Albania

• It also only includes the Western Part of Denmark (the whole country is included above) and is synchronised with Morocco, Algeria, Tunisia through the Gibraltar AC link.

6.6.1.2 Stage IV

The Stage IV component of this indicator is the complement of the share of electricity in total FEC – and is the same as that shown in section 6.4.3.3 for the EU-27.


The combined impact of the Stage I DEPCM component scaled by that at Stage IV for the EU-27 is shown in the figure below and the vulnerability under each scenario broadly follows the same trend as the load factor at Stage I.

Illustration 6 - 32 De-rated Electricity Peak Capacity Margin (DEPCM) – Overall indicator

Overall indicator

0%

5%

10%

15%

20%

25%

30%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

(Sta

ge I

V s

cale

d) %


Indicator De-rated Electricity Peak Capacity Margin (DEPCM) Energy type Electricity Stage All Y-axis Value

Data

Scenario

Member State

◄▬▬▬

In

cre

asi

ng

vu

lne

rab

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▬▬◄

60 Now ENTSO-E (European Network of Transmission System Operators for Electricity)

196


6.6.2 Flexibility Margin

6.6.2.1 Stage I

The figure below shows the results for EU-27 for the flexibility margin (i.e. ability of dispatchable plant to respond sufficiently quickly to increases in peak demand and reduction in intermittent supply) under each scenario – for the case with a typical (1% probability) loss of wind generation and warm/spinning reserve ramp rates. Figures above zero represent sufficient available flexibility.

Under all three scenarios the flexibility margin declines steadily (i.e. increasing the vulnerability to a load balancing failure) until ~2015, when the rate of decline decreases under the baseline but is maintained under the CCS and climate package scenarios. This decline results in a lack of flexibility from around 2025.

Illustration 6 - 33 Flexibility margin – Stage I – typical loss of wind generation and spinning reserve ramp rates

Flexibility margin

-20%

0%

20%

40%

60%

80%

100%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year


Indicator Flexibility margin Energy type Electricity Stage I Y-axis Value

Data


◄▬▬▬

In

crea

sin

g v

uln

erab

ilit

y ▬▬◄

There are three underlying drivers affecting this trend:

• The change in peak electricity demand (which impacts on the maximum possible hourly demand swing), which is the same as that described in relation to extreme events in section 6.4.3.1. I.e. strong steady increase under the baseline to 2030, slower growth under the non-baseline scenarios to 2020, but much faster growth (exceeding that in the baseline) to 2030 – albeit marginally lower overall for the CCS scenario compared to the climate package.

197


• The level of wind capacity on this system, which as shown in Table 6 - 17, starts to increase most rapidly in the non-baseline scenarios compared to the baseline scenario from around 2015-2020 onwards – leading to a greater potential loss in hourly output from this capacity. The level of wind is slightly higher under the climate package scenario compared to the CCS.

• The level and type of dispatchable plant on the system. The installed capacity of coal, oil and gas from around 2015-2020 onwards is higher under the baseline scenario compared to the others, but in both the CCS and climate package scenario, the higher capacity of biomass-waste fired generation acts to offset this to a large extent61.

To test the sensitivity it also possible to explore a potential “worst case” scenario – the figure below shows the flexibility margin with a maximum likely loss of wind62 and cold start ramp rates. The single biggest factor affecting the flexibility margin is the contribution of coal generation, as under a cold start it is currently, assumed that only 1% of plant capacity is available within the hour. Under a warm/spinning reserve ramp rate this raises to 60% and a reserve management by system operators will aim to ensure that sufficient plant is held in this state to meet the net swings required. In the worst case scenario, there is a loss of flexibility from 2010 in all scenarios.

Illustration 6 - 34 Flexibility margin – Stage I – maximum likely loss of wind generation and cold start ramp rates

Flexibility margin

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year



Data


◄▬▬▬

In

crea

sin

g v

uln

era

bilit

y ▬▬◄

61 This assumption needs to be considered with care as biomass fired plants could be running baseload so

may not be able to “ramp up” to manage swing. 62 As outlined in the fixed assumptions in section 6.2.2, this is currently based on historic UK data and

corresponding to a maximum hourly loss of 27% of wind capacity.

198



As per the DEPCM indicator discussed in section 6.6.1.1, it is important to look at the flexibility margin with respect to groups of countries with an integrated electricity network. The same example groups are shown below (assuming typical loss of wind generation and spinning reserve ramp rates) and with the same caveats surrounding the figure for ex-UCTE EU MS grouping.

Illustration 6 - 35 Flexibility margin – Stage I – typical loss of wind generation and spinning reserve ramp rates – various country groups

Flexibility margin

0%

50%

100%

150%

200%

250%

300%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

%



Data


◄▬▬▬

In

creas

ing

vu

lner

ab

ilit

y ▬▬◄

Note: Group (1) = UK/IE, Group (2) = ex-BALTSO (EE/LV/LT)

199


Illustration 6 - 36 Flexibility margin – Stage I – typical loss of wind generation and spinning reserve ramp rates – various groups - ex-UCTE EU Members

Flexibility margin

0%

50%

100%

150%

200%

250%

300%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

% Baseline - Group(1)CCS - Group(1)Package - Group(1)


Data


◄▬▬▬

In

creas

ing

vu

lner

ab

ilit

y ▬▬◄

Note: Group (1) = AT, BE, BG, CZ, DK, FR, DE, GR, HU, IT, LU, NL, PL, PT, RO, SL, SK, ES

6.6.2.2 Stage IV

The Stage IV component of this indicator for the EU-27 is the complement of the share of electricity in total FEC – and is the same as that shown in section 6.4.3.3.

200



The combined impact of the Stage I flexibility margin component (assuming typical loss of wind generation and spinning reserve ramp rates) scaled by that at Stage IV for the EU-27 is shown in the figure below and the vulnerability under scenario broadly follows the same trend as the load factor at Stage I.

Illustration 6 - 37 Flexibility margin – Overall indicator – typical loss of wind generation and spinning reserve ramp rates

Overall indicator

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

(Sta

ge I

V s

cale

d) %


Indicator Flexibility margin Energy type Electricity Stage All Y-axis Value

Data


◄▬▬▬

In

crea

sin

g v

uln

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bilit

y ▬▬◄

6.7 Results - Supply shortfall associated with resource concentration

6.7.1 Resource Concentration Price Indicator (RCPI) (markets characterized by an effective price mechanism)

6.7.1.1 Stage I

The figure below shows the estimated ESMC (energy security market concentration) value for each primary fuel over time. Under the RCPI these values are applied equally to all MSs given that they are exposed to the same price risk for fuels purchased on international markets. The projected values from 2010 onwards are currently based on data from IEA (2007), and show the situation where OPEC is treated a single supplier63, with no political stability ratings. The values are currently held constant across all PRIMES scenarios.

63 Under the case where OPEC members are treated as individual suppliers the Oil ESMC drops considerably,

to around 900 in 2010, rising to

201


Both oil and coal show a gradually rising ESMC from 2010 onwards, indicating that the available supply of fuel on the international market is increasingly concentrated amongst fewer suppliers – hence it indicates an increased vulnerability to a price impact should suppliers exploit their market power.

Gas ESMC values reflect the market concentration of gas that is not sold under oil-indexed contracts (i.e. on gas-based terms). Over the period 1990 to 2005 this is relatively limited and we have used the measure of each country’s share of the market based on net export potential (primary production – TPES) as an estimate combined with some very simple rules of thumb:

• Assume all pipe trade to Europe and Asia to be long-term oil indexed.

• Assume the remaining gas import demand in Europe and Asia comes from LNG and is also oil indexed. This is taken proportionally from all LNG exporters based on their total net export potential.

• The remaining net exports worldwide (both pipe and LNG) are included in the gas ESMC measure (e.g. North American gas) – hence the peak in the late 1990s is driven primarily by the net export potential in Canada within a relatively limited overall international market of non-oil indexed exports (according to these assumptions).

For policy analysis, the projections are the more important element. The values in 2010 (and beyond) are based on the original IEA (2007) values (with the underlying approach still based on the share of each country’s net export potential). From 2005 to 2010 this first assumes a transition from regional to global market dynamics (i.e. existing suppliers can access a wider pool of customers). Moving beyond 2010 there are then opposing dynamics, which broadly balance out in terms of the overall ESMC value. An increase in the amount of net export potential that is available on gas-based terms (i.e. shifting away from oil-indexation - which would generally lower the ESMC if this increased the number of supplier countries), and the overall trend towards resource concentration within oil suppliers (which will increase the ESMC).

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Illustration 6 - 38 Resource Concentration Price Indicator – ESMC – Stage I – OPEC as a single supplier, no political risk ratings

ESMC

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

ESM

C

Coal - Baseline - EU-27Gas - Baseline - EU-27Oil - Baseline - EU-27

Indicator Resource Concentration Price Indicator (RCPI) - Markets with effective price mechanism Stage I Y-axis Value

Data

Energy type

Scenario

Member State

►▬▬▬

In

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▬▬►

As a sensitivity, the figure below shows the ESMC values scaled by political risk ratings (from 1 = risk free to 3 = highest degree of risk) based on the World Bank’s Governance indicators (note that the estimates for 2007 are held constant over time as no projections are available). ESMC increases significantly, doubling in many cases, highlighting that many of the suppliers of primary fossil fuels are perceived to have a sizeable risk attached to them.

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Illustration 6 - 39 Resource Concentration Price Indicator – ESMC – Stage I – OPEC as a single supplier, political risk ratings included

ESMC

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

ESM

C

Coal - Baseline - EU-27Gas - Baseline - EU-27Oil - Baseline - EU-27

Indicator Resource Concentration Price Indicator (RCPI) - Markets with effective price mechanism Stage I Y-axis Value

Data

Energy type

Scenario

Member State

►▬▬▬

In

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▬▬►

6.7.1.2 Stage IV

Stage IV for the RCPI is the share of primary energy in each scenario as a proportion of total primary consumption under the baseline scenario. The following graphs show the evolution under each scenario for coal, oil and gas for the EU-27. Both the standard Stage IV and Alternate Stage IV (heat and power transformation efficiency adjusted share of primary fuel in FEC) are shown for each fuel.

For gas an additional graph shows the share of gas consumption in total primary energy, for gas purchased on spot or spot-derived markets only. This is required to separate the gas purchased under oil-indexed contracts (and hence exposed to Oil ESMC) and that purchased on spot markets exposed to the Gas ESMC. Note that the stepped nature of this figure primarily reflects the illustrative, high-level assumptions on the future evolution of the gas market in Europe described in section 6.2.3, but broadly implies that over time less EU gas consumption will be exposed to oil price risks.

For all fuels the figures show a declining share (i.e. decreasing vulnerability to that fuel – all else being equal) of gas, coal and oil under the climate package and CCS scenarios out to 2030 – based on strong improvements in end-use efficiency and fuel switching towards renewables. For oil and coal, the decline is stronger under the CCS scenario, however for gas there is almost no difference between the scenarios.

204


Illustration 6 - 40 Resource Concentration Price Indicator – Stage IV – Coal


0%

5%

10%

15%

20%

25%

30%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year


Indicator Resource Concentration Price Indicator (RCPI) - Markets with effective price mechaEnergy type Coal Stage IV Y-axis Value

Data

Scenario

Member State

►▬▬▬

In

cre

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Illustration 6 - 41 Resource Concentration Price Indicator – Alternative Stage IV - Coal

(Heat and power transformation efficiency) adjusted share in FEC

0%

5%

10%

15%

20%

25%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year


Indicator Resource Concentration Price Indicator (RCPI) - Markets with effective price mechaEnergy type Coal Stage IV Y-axis Value

Data

Scenario

Member State

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In

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Illustration 6 - 42 Resource Concentration Price Indicator – Stage IV - Oil


0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year


Indicator Resource Concentration Price Indicator (RCPI) - Markets with effective price mechani Energy type Oil Stage IV Y-axis Value

Data

Scenario

Member State

►▬▬▬

In

cre

asi

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▬▬►

Illustration 6 - 43 Resource Concentration Price Indicator – Alternative Stage IV - Oil


0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year


Indicator Resource Concentration Price Indicator (RCPI) - Markets with effective price mechani Energy type Oil Stage IV Y-axis Value

Data

Scenario

Member State

►▬▬▬

In

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▬▬►

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Illustration 6 - 44 Resource Concentration Price Indicator – Stage IV – gas, all consumption

Share in primary energy of gas

0%

5%

10%

15%

20%

25%

30%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year


Root cause Supply shortfall associated with resoIndicator Resource Concentration Price IndicaEnergy type Gas Stage IV Y-axis Value

Data

Scenario

Member State

►▬▬▬

In

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▬▬►

Illustration 6 - 45 Resource Concentration Price Indicator – Stage IV – gas, purchased on spot or spot derived markets

Share in primary energy of gas (purchased on spot or spot derived markets)

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year


Indicator Resource Concentration Price Indicator (RCPI) - Markets with effective price mechanEnergy type Gas Stage IV Y-axis Value

Data

Scenario

Member State

►▬▬▬

In

cre

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Illustration 6 - 46 Resource Concentration Price Indicator – Alternative Stage IV – gas, all consumption


0%

5%

10%

15%

20%

25%

30%

35%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year


Root cause Supply shortfall associated with resoIndicator Resource Concentration Price IndicaEnergy type Gas Stage IV Y-axis Value

Data

Scenario

Member State

►▬▬▬

In

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▬▬►


The figure below shows the overall RCPI indicator for primary oil, coal and gas – i.e. the sum of the ESMCs for each fuel at Stage I multiplied by their share in primary consumption (standard Stage IV), with the split for gas reflecting consumption exposed to the oil price risk due to oil-indexing or gas price risk. In this case OPEC is treated as a single supplier and no political risk ratings have been applied.

Under the baseline scenario the indicator fluctuates to 2030, but is broadly stable, reflecting a stabilising/gradually declining level of consumption offsetting the gradually increasing ESMC values. By contrast the overall RCPI declines (indicating decreasing vulnerability) under the CCS and climate package scenarios as the stronger declines in primary consumption of fuels more than offset any increase in the concentration of suppliers over time.

208


Illustration 6 - 47 Resource Concentration Price Indicator – Overall indicator – OPEC as a single supplier, no political risk ratings – all primary fuels

Overall indicator

0

500

1000

1500

2000

2500

3000

3500

4000

4500

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

ESM

C


Indicator Resource Concentration Price Indicator (RCPI) - Markets witEnergy type Primary fuel (solids, oil, gas) Stage All Y-axis Value

Data

Scenario

Member State

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6.7.2 Resource Concentration Physical Availability Indicator (RCPAI) (markets with no or limited price mechanism) - Gas

Stage

6.7.2.1 Stage II

As described in section 6.2.3, the Stage II component of the indicator the ESIC (energy security import concentration) value is currently based on the concentration of net imports of gas (by country or origin) to each Member State in 200864. This value is held constant in future years as PRIMES only provides data on the change in net imports, but not changes in their country of origin.

The figure below shows these values, both with and without political risk ratings. Values range from 0 (no net imports or an infinite number of supplier countries) to 10,000 or 30,000 respectively (indicating a single monopoly supplier of net imports).

64 Excluding transit gas

209


Illustration 6 - 48 Resource Concentration Physical Availability Indicator – Stage II – ESIC – 2008 values

Note: In the case of Cyprus, net imports are only from “Other Europe and Eurasia”. Further disaggregation

is not available, therefore even though the calculation indicates a single monopoly supplier country, in

practice net imports may be provided by >1 supplier.

6.7.2.2 Stage III

Stage III is the availability of gas storage to cover a physical interruption to supply. The figure below shows the total storage capacity for the EU-27 and selected MSs (these are currently assumed to be fixed under all scenarios), based on current capacity and planned projects (all assumed to be complete by 2030).

210


Illustration 6 - 49 Resource Concentration Physical Availability Indicator – Stage III – Gas Storage Capacity

Gas storage capacity

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

ktoe

Baseline - EU-27Baseline - FranceBaseline - GermanyBaseline - ItalyBaseline - NetherlandsBaseline - United Kingdom

Root cause Supply shortfall associated with resoIndicator Resource Concentration Physical Av Energy type Gas Stage III Y-axis Value

Data

Scenario

Member State

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6.7.2.3 Stage IV

This stage looks at the share of EU-27 annual gas consumption not purchased on spot or spot-derived markets (e.g. under oil-indexed contracts) which is not covered by available storage capacity – i.e. the remaining consumption that would be subject to a potential physical unavailability risk.

The overall trend, under all scenarios, from 2010 onwards is declining vulnerability, albeit declining more quickly under the CCS and climate package scenarios. A number of underlying factors drive this trend:

• The share of primary gas consumption, which starts to stabilize under the baseline scenario from around 2020 onwards, but decreases (both its share and in terms of absolute consumption) under the CCS and climate package scenarios.

• The share of gas purchased on spot or spot-derived markets – which is assumed to increase over time as the EU markets develop under all scenarios.

• The level of available gas storage increases to 2030 in all scenarios.

Subject to the assumptions for some of these values described 6.2.3, by 2030 available gas storage is sufficient to cover all annual oil-indexed gas consumption.

211


Illustration 6 - 50 Resource Concentration Physical Availability Indicator – Stage IV

(Heat and power transformation efficiency) adjusted share of imported gas in FEC

0%

2%

4%

6%

8%

10%

12%

14%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year


Indicator Resource Concentration Physical Availability Indicator (RCPAI) (markets with no or limEnergy type Gas Stage IV Y-axis Value

Data

Scenario

Member State

►▬▬▬

In

cre

asi

ng

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▬▬►

Member State / country groupings

The figure for the EU-27 implicitly assumes that available gas storage can be utilized in any area where a physical interruption may occur. The figures below provide examples of the Stage IV component for other country groupings that have integrated gas infrastructure.

212


Illustration 6 - 51 Resource Concentration Physical Availability Indicator – Stage IV – various country groupings

Share in primary energy of imported gas (not purchased on spot or spot derived markets) not covered by storage

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

%



Data

Scenario

Member State

►▬▬▬

In

cre

asi

ng

vu

lne

rab

ility

▬▬►

Note: Group (1) = UK/IE, Group (2) = ES/PT

Share in primary energy of imported gas (not purchased on spot or spot derived markets) not covered by storage

0%

5%

10%

15%

20%

25%

30%

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

%



Data

Scenario

Member State

►▬▬▬

In

cre

asi

ng

vu

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▬▬►

Note: Group (1) = IT/SL, Group (2) = DE/CZ/SL

213



The overall indicator, Stage I ESIC (with no political risk ratings) multiplied by Stage IV, for the EU-27 is shown below. Because the Stage I values are currently held constant over time, the trend is the same as that seen in Stage IV.

Illustration 6 - 52 Resource Concentration Physical Availability Indicator – Overall indicator – no political risk ratings

Overall indicator

0

20

40

60

80

100

120

140

160

180

200

1990. 1995. 2000. 2005. 2010. 2015. 2020. 2025. 2030.

Year

(Sta

ge I

V s

cale

d) E

SIC


Indicator Resource Concentration Physical Availability Indicator (RCPAI) (markets with no or li Energy type Gas Stage All Y-axis Value

Data

Scenario

Member State

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In

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▬▬►

6.8 Summary

The results from each of the indicators (the overall indicator combining all stages), in terms of both the trend in vulnerability at each point in time and the relative ranking of each scenario, are shown in the table below.

It should be noted that these reflect the ‘variable factors’ selected in each case for the overall indicator (i.e. typical loss of wind generation and spinning reserve ramp rates for the flexibility margin).

Different sensitivities for these may alter the summary results for each indicator, but given the relatively small difference between the CCS and Climate Package and number of fixed underlying assumptions these are not significant for the scenarios examined – in terms of the comparison with the baseline scenario.

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Table 6 - 18 Summary of EU-27 vulnerability (direction of trend and ranking of each overall indicator) for each policy scenario

Baseline Climate Package CCS Root cause Indicator

2010 2020 2030 2010 2020 2030 2010 2020 2030 Overall short-Run Availability of primary fuel (Oil) (3) (3) (3) (2) (2) (2) (1) (1) (1)

Overall short-Run Availability of primary fuel (Gas) (3) (3) (3) (2) (2) (2) (1) (1) (1)

Extreme events

Further DEPCM (de-rated electricity peak capacity margin) (3) (3) (3) (2) (2) (2) (1) (1) (1)

Average Load Factor (1) (1) (1) (2) (2) (3) (3) (2) (2)

Cumulative required new capacity (MW) (3) (3) (1) (2) (2) (3) (1) (1) (2)

Cumulative required new capacity (€M) n/a


Capital Intensity n/a

DEPCM (De-rated electricity peak capacity margin) (3) (3) (3) (2) (2) (1) (1) (1) (2) Inadequate market

structure: Load balancing failure Flexibility Margin

(3) (1) (1) (2) (2) (2) (1) (3) (3)

RCPI (Resource Concentration Price Indicator) (3) (3) (3) (2) (2) (2) (1) (1) (1) Supply shortfall

associated with resource concentration RCPAI (Resource Concentration Physical Unavailability

Indicator) (3) (3) (3) (2) (2) (1) (1) (2) (2)

Key:

= Trend towards increasing vulnerability, Trend towards decreasing vulnerability, No significant change in vulnerability.

(3) = Highest/worst vulnerability of all scenarios

(1) = Lowest/best vulnerability of all scenarios

(2) = Vulnerability in between other two scenarios

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Broadly speaking, both the climate package and CCS policy lead to an overall improvement (i.e. decrease in vulnerability) in energy security at the EU-27 level across the indicators relative to the baseline, with a small number of exceptions.

This is driven primarily by the impact of the GHG and RES targets (which are incorporated under both scenarios) leading to significant energy efficiency improvements (both end-use and supply) and fuel switching away from fossil fuels. In many cases the PRIMES results show absolute decreases in EU-27 primary energy consumption for specific fuels (including natural gas) under the non-baseline scenarios out to 2030 (patterns of consumption vary significantly at the MS / country grouping level – with corresponding differences in the indicator results).

A relative improvement is shown even in the DEPCM-related indicators, which would be expected to show an increase in vulnerability due to the rapid expansion of intermittent generation. However, the improvement is driven by a much slower increase in electricity demand relative to the baseline in the near term, which more than offsets the growth in intermittent generation up to 2010.

But, more rapid growth in electricity demand is seen after around 2015, which when also combined with a more rapid expansion in intermittent renewables leads to the relative difference between the baseline and non-baseline scenarios declining.

When looking at the impact of one policy on energy security compared to another, it also highlights that it is important to not only look at the relative difference between scenarios at a single point in time but also the overall trend in vulnerability. I.e. the policy impact on electricity demand buys a short-term, temporary decrease in vulnerability, but as this effect is not maintained other factors combine to increase the vulnerability on a similar trajectory to the baseline scenario.

It also reiterates the (obvious) point; that the accuracy of the indicators depends almost wholly on the efficacy of the underlying projections. If the observed improvements (particularly on end-use energy efficiency) do not materialise, the implications for energy security will be considerably worse.

The three main indicators where the baseline scenario scores more favourably relative to the non-baseline scenarios are the:

• Average load factor. This is the result of the much more rapid expansion of intermittent renewables under the non-baseline scenarios.

• Cumulative required new capacity in MW: this is very similar in all scenarios up to around 2020. However, after this point the increase in electricity demand under the non-baseline scenarios, coupled with the requirement for lower carbon, lower capacity credit renewables leads to an increase in the required nameplate capacity.

• Flexibility margin: under the baseline, the main difference is the lower level of intermittent wind generation (would could potentially be lost), as the overall

216


level of dispatchable plant is more balanced across the scenarios (greater fossil plant under the baseline, but more biomass/waste-fired generation under the non-baseline scenarios).

The EU’s vulnerability, under the non-baseline scenarios therefore seems to be shifting over time away from primary-fuel related risks towards electricity-system related risks (this can also be seen in the radar diagrams below). Although, as shown the table, the overall trend under the baseline in these three cases is still towards increasing vulnerability.

Radar presentation of results

An alternative presentation of the overall indicators for each scenario (without weighting the indicators to produce an aggregate indicator) is via the use of a radar-diagram. This has the advantage of showing the scale of the relative difference between each scenario, rather than just the ranking as per the table above (although it becomes more difficult to assess the trends). The figures below show the results for the EU-27 in 2010 and 2020 and 2030. Each of the calculated indicators is normalised individually on a scale of 0 to 1, such that they all move in the same direction, with values closer to zero indicating lower vulnerability.

• For indicators where an increase in value indicates increasing vulnerability

- )()(

)(

ysysysys

ysysjp

jp XMinXMax

XMinXI

−

−=

• For indicators where an decrease in value indicates increasing vulnerability

- )()(

)(

ysysysys

ysysjp

jp XMinXMax

XMaxXI

−

−=

• Where

- Ijp = the normalized indicator value in year j for policy scenario p

- Xj = the actual indicator value in year j for policy scenario p

- Xy = the actual values in years y = 1, 2, …, n for policy scenarios s = 1, 2, …. k).

The normalisation has been calculated for each indicator based on each country's values (or EU-27 or country grouping) in the 5-yearly periods from 2000 to 2030 and across all 3 scenarios. I.e. a value of 1 indicates the highest vulnerability seen over this period in all scenarios, and a value of 0.5 indicates half the maximum vulnerability.

The current normalisation intentionally does not specify the relative importance of each indicator. It should be noted that values are a reflection of the extremes seen within the current three scenarios. Incorporating a wider range of (plausible) worst / best case scenarios alongside the policy(ies) of interest would help in the analysis, as the vulnerability values would be better set within the upper and lower limits. For

217


example, comparing policies with only minimal differences within the normalisation would tend exaggerate the difference in values.

Alternative forms of normalisation are also possible, for example, normalisation between country values or between different indicator values in a given year, but these would require an alternative calculation and so are not presented here.

As data is not available to calculate the capital intensity and required new capacity (in €M terms) they are not incorporated into the radar charts.

Note for graphs: results for the overall indicators are as per those in previous sections (i.e reflect the

same variable factors). EE = Extreme Events; II = (inadequate market structure) Insufficient Investment;

LBF = (inadequate market structure) Load Balancing Failure.

218


Illustration 6 - 53 Normalised indicator results for EU-27 in 2010 (lower value indicates decreasing vulnerability)

0.0

0.2

0.4

0.6

0.8

1.0







(EE) Short-Run Availability of primary fuel (Gas)



Member State EU-27

2010.

Indicator name

Scenario

219



0.00.1

0.2

0.30.4

0.5

0.60.7

0.80.9










220



0.00.10.20.30.40.50.60.70.80.91.0










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7 Case study analysis of other policies

In addition to the analysis of the three overarching policy scenarios examined in the previous section, a number of more targeted case-studies (in terms of policies and Member States) have also been undertaken, to further test the use of the quantitative indicator approach and explore the impact of other policies on energy security.

The results for these case-studies are provided in Appendix D and include:

i ) Impact of renewable energy support policies in both the UK and Spain from 1990 to 2007 (section D 1).

ii ) Impact of policies to promote CHP in the Netherlands from 1990 to 2000 (section D 2).

iii ) Impact of the Large Combustion Plant Directive in the UK and Poland from 2010 to 2020 (section D 3).

The choice of case studies reflects the desire to:

• Undertake ex-post analysis in i) and ii) in addition to the ex-ante analysis seen in the previous section. As in this situation only one scenario (the counterfactual in the absence of the policy) has to be estimated, with the actual outturn (i.e. historic data) reflecting the impact with the policy in place.

• Examine a non-climate change policy in iii), which still leads to impacts on the energy system. Whilst there is no specific ‘without LCPD’ scenario available, the specific nature of the policy (i.e. in terms of fossil plant which would be not be closed in the absence of the policy) allow this to be assessed with a reasonable set of assumptions.

It should be noted that the case-studies directly apply the quantitative framework from the previous section, this means that in many cases a number of underlying assumptions outlined in section 6.2 (e.g. ramp rates, capacity credits) are still applied.

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8 Conclusions and recommendations

8.1 Introduction

The main objectives of this project were to explore the impact of climate change policies on energy security and to establish a methodology that would serve as a base ground for further analysis, and which is able to quantify which policy measures are effective under shifting conditions.

We have taken a step-wise approach to this:

• A review of the concept of energy security and an overarching ES framework were first developed, which have been used throughout this study. The framework focuses on the concept of root causes of energy insecurity and outlines a staged approach to how these root causes translate into welfare impacts.

• A qualitative assessment of the impacts of different climate change mitigation options on energy security was then undertaken, to explore the key linkages and areas of interaction. This was used to understand how newly proposed climate policy options may impact on energy security and highlight the most important areas for quantification.

• We then undertook an extensive review of existing approaches to quantifying energy security, focused around the use of indicators. These were evaluated within the context of our ES framework.

• We then developed a series of bespoke indicators targeted at each of the root causes of energy insecurity, drawing on the outputs from each of the preceding steps. Given the complexity and situation specific modelling required for outcome-based indicators, the overall quantitative approach was based around the use of vulnerability-based indicators, as a proxy for the likely welfare impacts.

• These indicators have been incorporated within an excel-based spreadsheet tool and have been used to analyse the impact of the new climate package on energy security - via a comparison of the impact under a baseline scenario with a small number of alternative policy scenarios (based on energy systems modelling data from PRIMES as a key input). We have also undertaken a number of more targeted case studies to help test the quantitative approach.

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8.2 Implications for current EU climate policy on future energy security

The results were analysed under a baseline scenario and two alternative scenarios (a climate package containing the impact of the 2020 GHG and RES targets and CCS scenario65) out to 2030.

The results need to be considered as illustrative to some extent, given the nature of some of the other input data and assumptions (including those made by the PRIMES team), which are currently fixed across the scenarios (described in section 6.2).

The main summary table and radar diagrams are presented in section 6.8 and so are not repeated here, however, the following broad conclusions can be drawn:

• In general, both the climate package and CCS policy lead to an overall improvement (i.e. decrease in vulnerability) in energy security at the EU-27 level across the indicators relative to the baseline, with a small number of exceptions. These are focused around the electricity sector (as shown by the average load factor, flexibility margin and cumulative required new capacity indicators) – hence there is an overall shift away from primary-fuel related vulnerabilities towards more electricity-system related vulnerabilities.

• A key underlying driver in the improvements seen under the non-baseline scenario is related to energy efficiency (both end-use and transformation). This leads to a reduction in energy consumption (absolute in some cases) out to 2030 compared to the baseline scenario (even for gas). If this is not realized then the resulting energy security vulnerability is considerably worse.

• Even though the climate policies improve the situation relative to the baseline the long-term trend in vulnerability is still worsening in a number of cases – primarily for the electricity-related vulnerability indicators.

- Although a more limited growth in electricity demand in ~2015-2020 compared to the baseline helps to improve indicators such as the DEPCM, this then rises rapidly after this point and is combined with greater levels of intermittent renewables. Demand-side policy impetus therefore needs to be maintained and even strengthened beyond 2020.

- It was not possible to calculate the capital intensity or required new capacity (in €M terms) indicators due to data confidentiality issues. However, the increasing reliance on electricity in all scenarios indicates that it would be important to try to calculate them – to provide a more complete set of information on all vulnerabilities and root causes of energy insecurity.

• Trends and relative levels of vulnerabilities under each scenario at the individual Member State level, or country group level (where their

65 As discussed previously, the impact of the CCS scenario is very similar to the climate package given that

it also contains the 2020 GHG and RES targets, it is only by ~2030 that a sizeable amount of CCS is actually

implemented.

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infrastructure is more integrated) can look very different to those at the EU-27 level for the different indicators.

8.3 Recommendations for policy makers in using the quantitative approach

The analysis undertaken in this report is for the most part novel: we have developed new ways to gauge the various dimensions and vulnerability to energy insecurity. As such, the interpretation and handling of the indicators and their quantitative results must be considered with care.

The indicators and corresponding spreadsheet tool developed in this project are intended to help policy makers better grasp energy security dynamics. They are not intended to replace expert judgment but rather to complement and enhance it, by providing more objective information.

The tool is sufficiently transparent and modular for the analyst to explore the causes of changes in trends, as well as to assess the impact of varying some of the underlying assumptions.

8.3.1 Interpretation of individual indicators

8.3.1.1 General issues

A number of general issues with respect to the interpretation of individual indicators are outlined below:

• For each root cause, we devised the indicators following a logical progression. At each Stage, when an indicator is available, its value is most often directly understandable and usable.

- In some cases this may not be so, and some of the metrics at a Stage are less familiar or have less intuitive meaning (e.g. the Stage I flexibility margin). Similarly, when considering stages for a root cause indicator in combination, the absolute values of the indicators start to lose clear meaning.

- Combining a Stage IV to produce the overall indicator reduces the physical or tangible meaning of earlier stages (e.g. short-run availability in days versus this value scaled by the share of gas in primary energy), but this is undertaken to provide a better overall proxy of energy system vulnerability.

- It is therefore important to consider not only the evolution of the overall indicator, but also how the Stages evolve independently (the spreadsheet tool structure allows for this).

• However, the thrust of the main analysis is comparative: we want to know how situations evolve compared to a baseline.

- As already shown by the results, when comparing one policy option against another it is important to look at both the trends and the relative

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positions of each scenario (as shown by the indicator) over the whole time horizon.

- For example, as illustrated in the case study on renewable support mechanisms in section Appendix D - D 1. Under the counterfactual (without renewable policy) scenario, it is assumed that generators would increase utilization of their existing gas plant before building new capacity. In the short term this has the effect of lowering the DEPCM as there is less total capacity on the system than under the ‘with policy’ case. The opposite is generally expected due to the lower capacity credit of intermittent renewables. However, at some point generators would not be able to continue to meet peak demand with existing plant, and so as new gas capacity will be built the DEPCM will increase again over time (and likely to be above the with policy case – i.e. lower vulnerability).

• Sensitivity analysis is central to any evaluation of the relative performance of one policy option over another. At a high-level it is possible to understand the impact of changing the input values by examining the structure of the indicator formulae (e.g. where multiplicative or exponential relationships exist between input parameters). It is of course still necessary to undertake more detailed sensitivity analysis and this falls into two main areas:

- The variation in the impact of the climate policy on the energy system itself (as e.g. modelled within different PRIMES scenarios). Whilst it is an obvious point, the robustness of the results from the energy security indicators will therefore depending on the robustness of the underlying modelling data.

- Variations in the other input data and underlying fixed assumptions that are used to help construct each indicator. Some examples, which can be quickly altered are given by the ‘variable factors’ in the spreadsheet tool66 and have already been presented in the results. For example, looking at the impact on short-run availability with and without the loss of the largest supply route.

8.3.1.2 Indicator specific issues

In addition to those issues above, there are a number of more specific issues related to the interpretation of certain indicators. These are considered separate to the areas for further work (e.g. refining some of the underlying input data, implementation of additional stages of the indicators) described in section 8.4

Integrated infrastructure

The first issue is that comparisons of vulnerability at the individual Member State level (or even EU-27 level, although aggregating across many countries reduces this

66 All ‘other input data’ and assumptions are presented transparently in the tool, in the separate yellow

sheets, and can be easily updated, with changes carrying through into the indicator results.

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problem to some extent) may potentially be misleading when considering integrated infrastructure.

It is also important to consider groups of countries that share a gas network or are part of the same electricity system (e.g. a synchronous grid), as the vulnerability in one part cannot often be physically separated from the vulnerability in another. A number of provisional pre-set country groupings (as well as the ability to custom select groupings) is already built into the spreadsheet tool. This issue applies most directly to the following indicators:

• Overall Short-Run Availability of primary fuel (Gas)

• Further DEPCM (de-rated electricity peak capacity margin)

• Average Load Factor

• DEPCM (De-rated electricity peak capacity margin)

• Flexibility Margin

• RCPAI (Resource Concentration Physical Unavailability Indicator)

For example, the Overall SRA of primary fuel (gas) will show high vulnerability for a Member State with limited or no gas storage even if it is strongly integrated to another that does have sizeable capacity. By contrast, only focusing on the Member State with the storage capacity may indicate an artificially low vulnerability as it does not account for the peak gas demand from the other Member State which is linked to it.

It should be noted that:

• Grouping countries in this manner reflects physical infrastructure issues. It does not, however, reflect contractual or political issues – e.g. country A actively limiting the access of country B to its storage facilities in the event of a disruption to supply.

• At the EU-27 level, indicators related to gas effectively assume that available storage can be supplied to the area of need without physical restrictions.

• For the electricity-related indicators it implies that the EU-27 should be treated as a single system. To some extent this is a semi-reasonable assumption as a much of mainland Europe forms part of the ex-UCTE wide area synchronous grid, and activity is ongoing to strengthen the interconnectivity of the electricity system across Europe. However, as discussed in section 6.6.1.1, this does raise the further complication of interconnection of the electricity system beyond the EU-27 Members and hence geographic boundaries of the current indicators.

Absolute and benchmark values

The second issue is the interpretation of absolute values. Whilst the methodology is most concerned with relative comparison of the results across policy options, there is inevitably a question about the physical meaning of earlier Stages of an indicator, particularly when values drop to zero or even become negative.

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In some cases the explanation is more straightforward, for example, in the case of the Short-Run Availability of primary fuel (gas) a value of zero indicates insufficient storage capacity (no capacity or that the maximum daily withdrawal rate is less than the Daily Peak Supply Shortfall).

A key indicator where such interpretation is more difficult is the DEPCM (de-rated electricity peak capacity margin), both in its standard form (i.e. under the load balancing root cause) and its use under the extreme events root cause (where it is further de-rated):

• In theory a value of ≤ 0% indicates that during a period of peak demand there is unlikely67 to be sufficient available generating capacity. It does not mean that a load balancing failure will actually occur.

• It is important to note that all indicators, including the DEPCM, are a simplification of reality. In its current formulation, data limitations mean it is does not account for:

- the ability of interconnectors to contribute to meeting or adding to peak demand (although analyzing groups of interconnected countries as discussed above helps overcome this to a large extent), or;

- the ability of a planned, coordinated demand-side response to reducing peak demand. It may be expected that the materiality of this will increase over the next 10 years as new policies are introduced, technology enables smaller customers to participate, and price signals become more targeted. Where demand-side response volumes can be estimated or modelled, this could be included in the metric as an adjustment to peak load.

• However, even if it were possible to incorporate these factors the values would still not necessarily be an accurate reflection of the likelihood of a load balancing failure, given the complexity of the electricity system.

• There is a limit to how far you would want to go to add to the sophistication of the indicators, given the resources required to implement them and limitations on data availability related to the impacts of climate policy on the energy system (particularly for projections).

• In this case, the indicator should be viewed as a high-level proxy, and if under one policy option, a particularly rapid increase in vulnerability or low absolute value is shown, then separate situation specific analysis could then be undertaken (i.e. using dedicated electricity network models).

One option to improve this for policy makers would be to create benchmark values (ideally at the country grouping level), beyond which the increase in vulnerability is deemed too high. I.e. if policy option X moved the DEPCM indicator below this then the option could be discounted. It should be noted that benchmark values may vary considerably across country groupings.

67 The capacity credits of the different generating technologies are based on a probabilistic assessment of

their likely availability during the period of peak demand.

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For example, as part of more detailed analysis in the UK (IHS Global Insight 2009), estimates of the level of expected energy unserved68 are estimated to be negligible when the DEPCM is >10%, low and manageable at between 10%-6% (<4 GWh/year), but start to spike significantly once it drops to <5%.

The difficulty in interpreting absolute values and the potential for benchmark values applies in the exactly the same way to the flexibility margin indicator.

8.3.2 Interpretation of the ‘package’ of indicators

All the indicators that we have developed aim to measure vulnerability to energy insecurity. These are targeted towards specific root causes and amount to 11 in total (although within each root cause some are only marginally different – i.e. required new capacity in both MW and €M terms or Short-Run Availability separately for oil and gas).

However, a key question is the comparability of indicators across root causes. The challenge comes when a policy maker is faced with a summary table such as that in section 6.8 and must interpret the package of indicators to select the ‘best’ option - this may be far from straightforward if the trends and values of the indicators are moving in different directions69.

The Staged approach to the indicators has been developed as a proxy for welfare impacts. But, without a direct measure of the welfare impact itself a 1% change in vulnerability under each indicator may in reality lead to a considerably different impact in actual welfare. As outlined in section 6.8 the normalized radar diagram results do not provide information on the relative difference between indicators, only within the indicator itself.

Aggregation of the indicators into a single index is of course possible, and may aid high-level communication – although this is almost certainly followed by the question of what are the underlying factors driving this trend. However, as outlined in section 5.2.2, the process of aggregation is inherently subjective, and requires weighting the relative importance of each indicator.

When making a choice on the ‘best’ policy option without a common metric for comparison (such as welfare), some subjectivity is of course inevitable – whether it is undertaken explicitly or implicitly by the policy maker.

We decline to do this here as it is beyond the scope of this study – which is focused on the creation of an objective, quantitative approach to support policy makers – and in certain cases touches on the realm of political considerations. However, we would reiterate the care that should be taken when making any value judgments about the relative importance of different energy security issues, as an overall trend may provide a misleading view of changes in vulnerability.

68 A probabilistic estimate of the likely physical unavailability of energy supply. 69 Although in the case of the scenarios that we have examined, baseline versus non-baseline (climate

package and CCS), the overarching distinctions are relatively clear.

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As a starting point, we do propose a number of ‘factors’ that a policy or decision-maker may wish to consider when interpreting the set indicators – particularly with regard to the EU’s or Member States’ own ability to mitigate against either the root cause of energy insecurity or the energy security impact itself.

For example, whether:

i ) The indicator reflects a root cause of energy insecurity that is internal or external to the EU (primarily in geographic terms, but this could also be reflected in terms of a sphere of influence).

- For example, the indicators of resource concentration (RCPI and RCPAI) are largely concerned with an external factor whereas the load balancing failure-related indicators (DEPCM and the flexibility margin) are largely concerned with the electricity system within the EU.

ii ) The potential impact of energy insecurity is more likely to manifest itself as physical unavailability of energy, or as a price-based impact.

- For example, the RCPI and RCPAI are designed to act as a proxy for each of these separately. Similarly, the indicators targeting the insufficient investment in new capacity root cause (average load factor, capital intensity and required new capacity) are more likely to result in price-based impacts, whereas those related to load balancing failure will more likely result in physical unavailability impacts.

iii ) The root-cause or possible impact of energy insecurity occurs over the short-term or long-term. This could be in relation to the immediacy of the potential impacts and/or the timescales over which any potential mitigation action needs to be implemented to be effective.

- For example, resource concentration issues (and their associated indicators) are likely to change more slowly over time. However, increasing the diversity of supply options (for example, in the case the RCPAI by developing new pipeline supply routes or LNG infrastructure) may take a considerable length of time before becoming fully operational. By contrast, the nature of extreme events is such that their timing is largely unpredictable, but as per resource concentration possible options to mitigate their impact may take time to develop.

iv ) The root cause of energy insecurity or its likely impacts reflect overarching political/institutional issues (i.e. the market or legislative structure in place) or primarily technical/technological issues.

- For example, the former is reflected directly in the inadequate market structure root cause and indicators of insufficient investment. The overarching policy environment across Member States may or may not be sufficient to help mitigate against the potential impacts (e.g. insufficient investment is less likely to occur when support mechanisms minimise the risk for investors). By contrast, the availability of gas storage (considered in indicators such as the SRA of primary fuel in the case of extreme events) may, in some respects, be considered more of

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a technical issue. Less so in terms of the overall level of storage developed, which is also driven by market issues and the incentives provided, but in terms of technical factors such as the ability to withdraw and transport gas from available storage at a sufficiently rapid rate to meet any supply shortfall.

8.4 Areas for further work

This study has outlined quantitative approach to assessing the impact of climate policy on energy security, based around a series of bespoke indicators. It has also implemented these within a spreadsheet tool and applied the approach in practice to analyse the effect of the new climate package on energy security, as well as a number of other policies within 3 case-studies.

Refining non-PRIMES data

As discussed in section 5.2.3, data availability was carefully considered as part of the design of the indicators, with a balance struck between this and the robustness of the indicators - in terms of their ability to act as suitable proxies for vulnerability to energy insecurity.

In addition to the PRIMES scenario data it was therefore necessary to incorporate a range of ‘other input data’ and fixed assumptions, which are outlined in detail in section in section 6.2 and are also contained within separate sheets in the main spreadsheet tool. The original objectives of the project highlight the focus on providing a methodology that can serve as a base ground for further analysis, and therefore access to sufficient data within the timescales of this project was not seen as a barrier.

With the exception of the two indicators for which it was not possible to provide the underlying PRIMES data70, all indicators (albeit not all Stages) have been analysed for the baseline, climate package and CCS PRIMES scenarios. However, in many cases the results should be viewed as illustrative, given the high-level estimates and fixed assumptions needed with respect to the other input data. It is recommended that further work is undertaken on this in two areas:

• To improve the overall robustness of this ‘other input data’.

• Where appropriate, create separate scenarios for the data such that they are more consistent with both a baseline and a with policy scenario71.

70 Due to confidentiality issues - these are the capital intensity and required new capacity (in €M) indicators.

If possible, t is recommended that these are analysed with respect to the climate package policy as part of

any further work. The underlying structure for these indicators is already contained within the spreadsheet

tool. 71 An example being the ESMC (energy security market concentration) calculations which underpin the RCPI.

In the event of a new global agreement on emissions, the future evolution of international fossil suppliers

will likely be quite different – affecting the ESMC. It may therefore be more appropriate to match this

alternative scenario to the with climate package analysis (at least in the case of the package with a 30%

target).

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Key amongst those areas described in section 6.2 is:

• Closer linking of the impact of an extreme winter event to the change in daily peak demand relative to a typical winter.

• Assessing the operation of gas plant as peaking plant within Member States

• Assessing the range of likely hourly wind swings across Member States.

• Updating the projections of future resource concentration in international markets.

• Estimating the change in the supplier countries of origin of gas imports for each Member State.

• Refining the values for capacity credit of electricity generation.

Of the remaining input data, many areas would benefit from further refinement. However, given the difficulty of obtaining robust projections for some of these parameters the creation of illustrative scenarios as part of overall sensitivity analysis may be most appropriate. In particular, for parameters such as political stability or the evolution of the EU’s gas markets.

Testing of additional components

As described in section 6.3.2 it was not possible to explore the impact of a number of components of the proposed indicators due to limitations on resources and high-level data availability. It is recommended that these are explored further as part of further work:

• Insufficient investment metrics at Stage III (see section 5.5.5) the measure of (spare) Peak Interconnector Margin. It may be possible to obtain historic data on cross border flows at specific hourly points from sources such as ENTSO-E72, which could then be contrasted against available Net Transfer Capacities between MSs73. However, a key issue is how this varies under different projection scenarios given the significant impact of climate policies on the evolution of the electricity system. PRIMES provides annual net imports of electricity, but it would be necessary to obtain maximum interconnector loads (if available) based on PRIMES’ representation of (single bus) interconnectors between MSs.

- Note that if this information is available it could also be used to enhance the calculation of the DEPCM-based indicators.

• For both the RCPI and RCPAI indicators at Stage IV the minimum primary fuel demand accounting for multi-firing capability in heat and power generation (although the RCPI Stage IV is still adjusted to reflect available gas storage capacity). An initial examination of available PLATTS data74 shows that

72 http://www.entsoe.eu/resources/data/exchange/ 73 E.g. see http://www.entsoe.eu/resources/ntcvalues/ntcmap/ 74

http://www.platts.com/Analytic%20Solutions/UDI%20Data%20&%20Directories/World%20Electric%20Pow

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alternative fuels and capacities are listed for many of the individual generating units (how comprehensive this is across Europe is unclear). Using this and estimates on generating efficiency would allow an adjustment to be made to minimum primary fuel demand. However, this would only be for historic data as multi-firing capacity is not provided as part of PRIMES projections, although comparable information may be available from other modelling exercises.

• For the (RCPI) Resource Concentration Price Indicator (markets characterized by an effective price mechanism) at Stage III (see section 5.7.3.3) the potential fuel quality flexibility parameter and liquidity parameter. These parameters would simply add a scaling factor to the earlier ESMC value and so carry through proportionally to the final value of the indicator.

- It is difficult at this stage to examine the significance of the impact without examining available data in more detail. However, it is clear that these parameters do not form part of the underlying workings of the PRIMES model, so any projection data would have to come from alternative sources.

Other

In addition, to the elements described above, other areas for further work could include:

• Further analysis of the current (and alternative) approach to normalizing the indicators (discussed in section 6.8), in particular by incorporating a more extreme range of high/low scenarios, within which to better ‘anchor’ the policies of interest.

• As part of the further refinement of non-PRIMES data and analysis of a wider range of PRIMES policy scenarios, it is recommended that further sensitivity analysis is undertaken. For example, the flexibility margin can currently be analysed under a cold or spinning reserve situation for each technology. Aside from refining the ramp rates themselves, it may be interesting to examine the case where a proportion of each technology can set to cold or hot start75.

• The development of (country grouping specific) benchmark values to help in the interpretation of the DEPCM-based indicators and the flexibility margin indicator.

• Provided that corresponding PRIMES scenarios are available (baseline and with policy), expanding the geographic scope of the indicators to cover countries with infrastructure that has some integration to existing EU MSs.

er%20Plants%20Database/ Note that a licence would need to be purchased for data from this specific

source. 75 In this case further modifications to the structure of the spreadsheet tool would be required.

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Appendix A Supply chain assessment tables

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Table A - 19 Supply chain assessment of energy security issues for oil

Root cause Extra-EU Intra-EU

Category Type International production/processing Imports Production Transport Processing Distribution Storage End-use

demand

Extreme weather

# Mostly occurs in extreme environments - arctic permafrost, at sea, deserts, etc. Such locations susceptible to extreme weather events, including deep freezes, dust storms and typhoons (e.g. major US processing facilities located in hurricane-prone Gulf of Mexico).


# Pipeline network inherently inflexible due to its bulk and cost. # Country pipelines are high capacity, so large accidents have a direct effect on large volumes of supply.

Extreme events

Acts of terrorism

# A majority of exporting countries are considered lesser developed and geopolitically instable. As a result, production facilities and infrastructure are at risk to extreme events, whether due to poor safety records, worker discontent or

# A number of strategic transit points around

# Much of EU production takes place in extreme climates and in remote locations. Damage done to production facilities results in costly, slow repairs.

# Pipeline network inherently inflexible due to its bulk and cost.

# Refineries dedicated to specific crude types reduce capacity to address upstream supply shortfalls. # Large scale and concentrated infrastructure hence disruption cause significant upstream impacts

# Distribution networks dedicated to given product grades.

# Often large scale and concentrated infrastructures. # Grade specification of product stored limits flexibility to meet shortfalls.

# Extreme and extended cold periods can increase use of primary fossil fuel for space heating. # Limits of short-term to medium term substitutability due to variety of different product types (e.g. petrol vs. diesel), tightening product specifications

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Strikes

local/international terrorism. the world are highly susceptible to terrorism, piracy and other forms of interference such regional conflicts. These include the Strait of Hormuz (through which ~1/3 of the world's oil trade passes), Strait of Malacca, Suez canal, Strait of Bab el-Mandad. # Oil export terminals typically represent large concentrated infrastructure and can be good targets of strikes.

Inadequate market structure

Insufficient investments in new capacity

# A number of oil-producing countries lack stability, uncertain regulatory environment (e.g. changing price controls) affecting the decision-making of both IOCs and NOCs. Corruption, threat of nationalisation and the potential of civil unrest further hinder business development.

# Lack of stability, uncertain regulatory environment (e.g. changing price controls), corruption, cross border tensions affecting ability to invest in international oil transport infrastructures.


# Petroleum resources are heavily concentrated in developing countries, with significant geopolitical risk (e.g. OPEC member countries and Former Soviet republics. Elsewhere, oil production has most often already peaked, resulting in ever growing reliance on these countries.

# Limited excess capacity in market to absorb potential supply shortfall.


# Refineries bound to a type of crude and product range increase reliance on particular suppliers.

(e.g. low sulphur fuel), # Limited availability of alternative technologies (e.g. dual fuel vehicles), # Limited availability of alternative vehicle fuel supplies (e.g. biofuels) and infrastructures. # Long term improvements in vehicle efficiency will reduce the vulnerability of the system.

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Table A - 20 Supply chain assessment of energy security issues for natural gas



demand

Extreme events

Extreme weather

# Large scale and high concentration of production infrastructure. Mostly occurs in extreme environments - arctic permafrost, at sea, deserts, etc. Such locations susceptible to extreme weather events, including deep freezes, dust storms and typhoons. # Temperature extremes affect pipeline-packing capacity.

# Pipeline network inherently inflexible due to its bulk and cost. # LNG regasification terminals have Inherent inflexibility due to constraints on berth sizes, gas grades, etc. # LNG liquefaction / shipping / regasification chains typically large scale and highly concentrated infrastructures.

# Much of EU production takes place in extreme climates and in remote locations. As such, any damage done to production facilities results in costly, slow repairs.



# Large scale and concentrated infrastructures.

# Extreme and extended cold periods can increase use of primary fossil fuel for space heating. # For electricity generation, alternatives can be accessed over the medium-long term. # For space heating short-term alternatives, e.g. electric heating and longer term alternatives

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# Pipeline network inherently inflexible due to its bulk and cost. # LNG regasification terminals have Inherent inflexibility due to constraints on berth sizes, gas grades, etc. # LNG liquefaction/ shipping/ regasification chains typically large scale and highly concentrated infrastructures. # Few high capacity cross-country pipelines, so large accidents have a direct effect on large volumes of supply.

Acts of terrorism

# Large scale and high concentration of production infrastructure.

# The few high capacity cross-country

(e.g. biomass is available). # Demand-side energy efficiency improvements can have a significant impact over the short term. Easier to reduce short term demand for gas in space heating by lowering internal temperatures and in some industrial sectors there are also agreements allowing for interruptible supply. # Limited substitutability in some sectors, primarily when it is used as a feedstock - e.g. in chemical production.

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Strikes

pipelines importing gas into EU are potential target as this would directly effect large volumes of supply. # LNG shipping travels through dangerous waters at risk of piracy.

Inadequate market

structure

Insufficient investments

in new capacity

# A number of gas-producing countries lack stability, uncertain regulatory environment (e.g. changing price controls) affecting the decision-making of both IOCs and NOCs. Corruption, threat of nationalisation and the potential of civil unrest further hinder business development. There is little transparency in market prices and a lack of depth in futures markets to underwrite investment.

# Lack of transparency over transit fees and access rights.

# Moving to a more liberalised energy market, it becomes more difficult to develop a regulatory regime which incentivises investment in excess capacity. # As a newly (and only partially in some places) deregulated market, there are still a number of uncertainties and an expectation of future regulatory changes. This may lead to insufficient investment in EU supply and transport infrastructure, particularly with respect to LNG development. # Distribution pipelines are a natural monopoly and therefore depend on sound regulation to encourage investments.

# In practice, storage is managed on a national level, not EU-wide. This impacts on the effectiveness of storage.

# Demand-side participation limited by specific distribution network. # Limited demand side participation in residential sector # Long term improvements in efficiency of electricity and heat generation will reduce the vulnerability of the system.

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concentration

# Limited number of exporting countries in market. A number of major gas producers located in unstable regions. # Over-reliance on a single supplier makes it possible for that producer to use gas supply as a political weapon (e.g. Russia-Ukraine disputes). # Increasing number of disputes over ownership of cross-border fields (e.g. Iran South Pars/Qatar North Field).

# Case of one gas producer supplying one pipeline, which sells to one buyer reduces flexibility and increases vulnerability. # Gas supplies often cross several countries, exposing supplies to disruption in any one country in the chain.

# Limited excess capacity in market to absorb potential supply shortfall.


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Table A - 21 Supply chain assessment of energy security issues for coal



demand

Extreme weather

# Extreme winter weather can affect road transport shipments.

# Extreme winter weather can affect road transport shipments.


# A number of large-scale coal producers (China, Indonesia) are known to have lower HSE standards, increasing the potential for accidents (e.g. coal seam fires). # Coal fires in China burn 109 million tons of coal each year, reducing supplies, shutting down nearby mines and unsettling land.

Extreme events

Acts of terrorism

# Coal is extracted and processed at large, centralized sites.

# The Strait of Malacca, where Chinese and Indonesian imports pass through, is prone to piracy.

# Extreme and extended cold periods can increase use of primary fossil fuel for space heating. # Limited substitution potential in the short term as coal's primary use is in electricity generation, and to a lesser extent heat production. # Over longer term substitution potential is higher and there remains a long-term trend to reduce coal use (e.g. due to environmental concerns) although this hinges to a large

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Strikes

# The mining industry is known for strong unionisation, increasing risk of supply disruption through striking.

# A good deal of coal transport is undertaken by rail. The rail industry has traditionally been prone to strikes.

# The mining industry is known for strong unionisation, increasing risk of supply disruption through striking.

# Large share of coal transport is undertaken by rail. The rail industry has traditionally been prone to strikes.

# Processing is often integrated at the mine head, therefore processing is also at risk for strikes.

# Final distribution undertaken by rail, risk of disruption due to rail industry strikes.

Inadequate market

structure


in new capacity

# A number of coal-rich developing countries lack stability, uncertain regulatory environment (e.g. changing price controls) affecting the decision-making, threat of nationalisation and the potential of civil unrest further hinder business development. # Limited global pricing mechanisms for coal and supporting mechanisms, such as futures markets, are still relatively new. This may discourages investment by new entrants and adversely affect investments.

# Overarching environmental concerns and regulatory requirements (particularly with respect to greenhouse gas and air pollutant emissions), planning issues, as well as uncertainty with respect to the development of new technologies such as Carbon Capture and Storage put a downward pressure on investment related to the coal industry (both mining and use) even when the economic and local availability of resources are more favourable.


# Production concentrated in a few countries, including China, Indonesia, and Australia.

extent on the development of new technology options in CCS. # Long term improvements in efficiency of electricity and heat generation will reduce the vulnerability of the system.

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Table A - 22 Supply chain assessment of energy security issues for uranium



demand

Extreme weather


Acts of terrorism

Extreme events

Strikes

# Uranium processing (enrichment) and spent fuel reprocessing facilities are large scale and concentrated operations.

Inadequate market

structure


in new capacity

# Few actors in the uranium market. Limited global integration with about 80% of trade under longer term (3-7 year) bilateral contracts directly between producers and utilities. This makes it difficult for the market to deliver clear long-term price signals for the development of new uranium mining and processing.

# Uncertain regulatory environment for nuclear power in the EU, with various countries imposing moratoria. # Planning constraints and delays associated with building new plant. Knock on implications for the supporting infrastructure, focused primarily on maintenance and decommissioning of existing reactors (and potential with alternative fuel cycles - such fast breeder reactors)


# Over half of production concentrated in Australia, Canada, and Russia.

# Fuel requirements per unit of electricity production (low compared to fossil fuels - extended period between refuelling e.g. 100+ "full-power days" for a typical Pressurised Water Reactor). # Fuel substitution possibilities depends on reactor type and proximity of reprocessing facilities for access to recycled uranium and plutonium from spent fuel as mixed oxide fuel, re-enriched depleted uranium tails, ex-military weapons-grade uranium, civil stockpiles, or ex-military weapons-grade plutonium.

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Table A - 23 Supply chain assessment of energy security issues for biofuels (including biomass)



demand

Extreme weather


# Extreme weather events and large scale accidents such as flooding, draught and fires affect crop yield.

# Extreme weather events and large scale accidents such as flooding, draught and fires affect crop yield.

Acts of terrorism

Extreme events

Strikes # Strikes and other episodes of civil unrest affect crop yield.

# Depending on the production route processing of biofuels particularly for transport can require fossil fuel inputs (e.g. ethanol and natural gas) leading to associated energy security impacts across the supply chain for these other fuels.

# Limited fuel substitutability potential in transport (e.g. flexifuel vehicles). # Long term improvements in vehicle efficiency will reduce the vulnerability of the system. # For electricity generation, alternatives can be accessed over the short-long term (e.g. reduce share in co-firing). # For space

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Inadequate market

structure


in new capacity

# The biofuel market is highly fragmented given the range of product types, and is focused at present primarily at a regional rather than global scale. Where international trade takes place this is predominantly under short-medium term bilateral agreements between supplier and end-user. # Support mechanisms have been uncertain given concerns over issues such as sustainability requirements. Given relative immaturity of the current market there are a lack of clear price signals to stimulate further investment, potentially leading to significant short-term price volatility.

# The biofuel market is highly fragmented given the range of product types, and is focused at present primarily at a regional rather than global scale. Where international trade takes place this is predominantly under short-medium term bilateral agreements between supplier and end-user. # Support mechanisms have been uncertain given concerns over issues such as sustainability requirements. Given relative immaturity of the current market there are a lack of clear price signals to stimulate further investment, potentially leading to significant short-term price volatility.


# At present most supply is primarily at a local level. However, given land limitations and the competitive advantage of warm climate crops (sugar cane, palm oil) many EU Member States may turn to imports of biofuels. Countries such as Brazil, Indonesia, and Malaysia may emerge as dominant exporters.

heating short-term alternatives, e.g. electric heating and longer term alternatives are available # Demand-side energy efficiency improvements can have a significant impact over the short term. Easier to reduce short term demand for gas in space heating by lowering internal temperatures and in some industrial sectors there are also agreements allowing for interruptible supply. # Long term improvements in efficiency of electricity and heat generation will reduce the vulnerability of the system.

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Table A - 24 Supply chain assessment of energy security impacts for electricity

Root cause Central generation (domestic and adjacent countries)

Category Category Thermal Intermittent renewable Hydro

Transmission/Distribution (cross border and domestic) Storage

End-use demand and distributed generation

Extreme weather

# Extreme hot weather raises river temperatures leading to a reduction in cooling water capacity. Requires steam turbines to run at reduced capacity. This can coincide with peak demand for cooling (e.g. air conditioning)

# Inability to run turbines in high winds (or alternatively where wind drops to a low level across a wide geographic area, output from wind turbines will drop in a correlated way) can lead to a reduction in available capacity in a short term (hourly) timeframe. # Extreme events can lead to damage to turbines, causing a longer term unavailability. Particularly for offshore facilities, weather conditions may prevent access to restore capacity for significant periods.

# Extended low rainfall will deplete reservoir, limiting ability to generate.

# Power lines can be knocked out due to ice/snow accumulation, lightning, or fallen trees.


Extreme events

Acts of terrorism

# An accident or terrorism can remove a large central generation unit from production.

# Accidents or acts of terrorism can take out key power lines.

# Limited short-term reserve fuel supplies for thermal plant and not-universally available across all plant. Connected to potential for physical supply impacts for all primary fuels.

# Extreme and extended cold and hot periods can increase demand for electric heating and cooling (e.g. A/C). # Limited short-term substitutability with respect to electricity demand. # Limited share of demand can take part in demands response due to lack of incentives (e.g. price controls) or adequate equipment (e.g. smart metering). # Long term improvements in end-use efficiency reduce the vulnerability of the system.

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Strikes

# A strike can reduce or prevent output from a large central generation unit for days to months - may be co-ordinated with strikes at other facilities, increasing the overall impact in a correlated manner.


in new capacity

# Uncertainty in future policy framework (e.g. with respect to carbon regulations), lack of credibility of EU or Member State policies. # New investment in generation limited by national or local planning regimes that either prevent new build entirely in certain locations, or impose sufficient delays in the process as to deter projects.

# Uncertain resource potential for intermittent renewables due to current scope of measurement sets, technological assumptions, and potentially to changes in weather patterns. This may lead to uncertain generation levels deterring renewable investments.

# Insufficient incentive for transmission asset owners to invest in new infrastructure can lead to an inability to connect new generation build on a firm basis, reducing effective generation capacity. # As investments in transmission infrastructure may have longer lead times than generation capacity, it can be necessary to plan prior to commitments on generation build. To the extent that planning is based on incorrect assumptions (or that these change over time), it may not be possible to connect new generation on a firm basis, and hence the amount of effective capacity is reduced.

Inadequate market

structure

Load balancing

failure

# Due to high penetration of intermittent renewables in generation mix difficult to predict what types of flexibility may be required from generation plant over time. This may mean that incentives for flexible plant (through expectations either of price volatility or of ancillary service contracts) are not sufficiently high.

# Hydro is typically very flexible, with an ability to ramp up/down very quickly, offering flexibility to the system. No access to hydro reduces balancing options.

# Interconnection between markets (albeit where capacity is not "held back" under long term contracts) provides flexibility to meet short term balancing needs but adds to complexity of system balancing. Without sufficient supporting technology and processes, this could lead to balancing errors.

# Distributed generation can add to the complexity of load balancing, but conversely may also reduce vulnerability to centralized generation disruptions.

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Appendix B Review of existing indicators of energy security

B 1 Vulnerability indicators – focusing on specific energy security issues

This section refers primarily to indicators with relatively simple formulae that aim, at least in theory, to target a specific aspect of energy security – e.g. particular root cause and/or Stage in the causal mechanisms. However, whilst the indicator itself may be regarded as ‘simple’ the approach to calculation of the underlying data that it is based on may be far more complex.

B 1.1 Infrastructure capacity and reserve indicators (Stage III)

B.1.1.1 Storage capacity and critical stocks of fuels

Example reference(s): IAEA (2005)

This indicator is defined by dividing the stocks of the critical fuels (oil, gas, etc) maintained by a country by the corresponding daily, monthly or annual fuel consumption – to provide an indication of the likely time stocks would last in the event of a disruption to supply.

For natural gas storage the key indicators are working gas capacity and deliverability. In most gas storage a base or cushion of gas is required to stay in the store at all times. Working gas capacity is then the difference between gross capacity and cushion gas. The deliverability of gas (withdrawal rate) from storage into the system is also critical, this is usually measured as a daily rate as peak daily demand is a key statistic for natural gas.

The indicator does not apply to electricity itself, as at present this cannot be stored in significant quantities.

Relationship to ES framework:

• The indicator does not measure any specific root cause.

• It targets Stage III of the causal mechanisms and hence provides a proxy for the flexibility of the rest of the supply chain to cope with a price / physical unavailability impact at an earlier stage. It also targets Stage IV via a measure of peak demand for energy.

• It targets the storage element of the energy supply chain for primary fuels (and also for refined products such as diesel and gasoline).

i ) Suitability: the indicator does not provide an assessment of the risks from energy security impacts by itself, but it is a potentially important indirect factor in determining the scale of the final energy security impact.

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ii ) Transparency: the indicator is transparent and objective as no weighting factors are needed. However, using peak demand as a measure of consumption may introduce a slight degree of subjectivity as this can be measured in a number of ways.

iii ) Availability of data: data on storage capacity is broadly available at a country level for oil76, gas77 and coal. Past data on peak daily demand for gas is readily available from national grid operators. However, accurate data on withdrawal rates is less readily available. Data on of storage78 of uranium and biofuels (primarily refined transport fuels) is less readily available.

iv ) Ability to forecast: future estimates of storage capacity are almost directly dependent on Government policy (and to a certain extent utility companies in the case of gas and coal), which adds to the uncertainty. Forecasts of peak daily gas demand are not generally available in modelling approaches such as PRIMES are only provided (over the relatively short-term) by some grid operators.

B.1.1.2 Load duration of back-up fuel supplies

Example reference(s): BERR (2006)

This indicator assesses the load duration (hours) of backup generation capacity assuming full output (average back-up fuel availability and maximum back-up fuel availability79) for electricity generation following disruption to primary fuel supplies.

The example, below shows in simplified form the number of hours that UK CCGT (combined cycle gas turbine) generation output could be maintained using alternative fuels, mainly distillate, stored on-site. The period of load duration will depend on the short-term ability to restock any back-up fuels.

Not all CCGT currently has this back-up storage capability. The level of reserve fuel storage capability currently in place depends, to a large extent, on whether the generator has firm or interruptible transportation gas supply contracts – as it is of more limited commercial benefit in the former case. However, some CCGT operators with firm transportation arrangements still choose to install back-up fuel capability in order to facilitate a response to relative gas, distillate and electricity prices – and so may be able to provide extended load duration. Similar indicators can be produced for other forms of electricity generation where fuel can be stockpiled.

76 E.g. from the Joint Oil Data Initiative http://www.jodidata.org/ 77 E.g. from http://transparency.gie.eu.com/ 78 As opposed to stocks of these fuels which could vary significantly on a daily basis. 79 I.e. full use of all available storage capacity

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Illustration B - 56 Estimate of load duration of UK CCGT back-up fuel supplies assuming full output

0

1,000

2,000

3,000

4,000

5,000

6,000

0 25 50 75 100 125 150 175 200 225 250 275 300 325 350 375 400

Duration (Hrs)

Cap

acity

(MW

)

Average Back-up Fuel Availability Maximum Back-up Fuel Availability

Source: BERR (2006)



• It targets Stage III of the causal mechanisms and hence provides a proxy for the flexibility of the rest of the supply chain to cope with a price / physical unavailability impact for primary fuels at an earlier stage.

• It targets the storage element of the energy supply chain for electricity.

i ) Suitability: as per the critical stocks indicator it does not provide an assessment of the risks from energy security impacts by itself, but it is a potentially important indirect factor in determining the scale of the final energy security impact.

ii ) Transparency: the indicator is transparent and objective as no weighting factors are needed.

iii ) Availability of data: data on storage capacity of back-up fuels is likely to be company/plant specific and difficult to obtain for the full EU-27.

iv ) Ability to forecast: in addition to difficulties in obtain comprehensive existing data, future arrangements for back-up supply are dependent on specific company/Government decisions and hence highly uncertain.

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B.1.1.3 Pipeline capacity and utilisation

Example reference(s): Various

Maximum levels of import of gas are constrained to a large extent by the availability of pipeline capacity (and to a lesser extent for oil). Indicators of pipeline capacity, injection and utilisation data are reported by operating companies/countries for all the major pipeline segments, on a daily or monthly basis. These can also be used in conjunction with other data to produce various simple indicators – e.g. percentage of imports met by gas pipeline, percentage capacity utilization to meet peak gas demand, etc.



• It targets Stage III of the causal mechanisms and hence provides a proxy for the flexibility of the rest of the supply chain to cope with a price / physical unavailability at an earlier stage.

• It targets the import, transport and distribution elements of the energy supply chain for gas and oil.

i ) Suitability: the indicator does not provide an assessment of the risks from energy security impacts by itself. However, it does impact strongly on the level of potential energy security impacts related to gas, given the pipeline network is inherently inflexible. But the measures of capacity and utilisation alone do not address the issue of contractual arrangements for pipeline use – which may restrict flows even where physical capacity is available.

ii ) Transparency: the indicator by itself is transparent and objective as no weighting factors are needed.

iii ) Availability of data: data on pipeline capacity and operation is carefully monitored by national/private operators, but availability may be limited in cases due to commercial confidentiality issues.

iv ) Ability to forecast: future estimates are more uncertain as they are dependent on significant assumptions about the expansion of pipeline capacity and an understanding of their geographical distribution.

B.1.1.4 Refining capacity and utilisation

Example reference(s): EIA (2009)

Crude oil needs to be processed into various petroleum products before it is available for use in final consumption, such as diesel and gasoline for road transport. Refinery capacity is therefore a key limiting factor in the oil supply chain. In addition, refineries are generally designed to process a specific grade of crude oil and supply a specific market's product specifications (e.g. for low sulphur fuel) - decreasing their flexibility to switch to a different processing setup.

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Various indicators of total refinery capacity and utilisation exist including by process type and deep conversion capacity (the ability to convert heavy and viscous residues that cannot be distilled further into lighter products).



• It targets Stage III of the causal mechanisms and hence provides a proxy for the flexibility of the rest of the supply chain to cope with a price / physical unavailability at an earlier stage.

• It targets the international production/processing and domestic processing parts of the energy supply chain for oil.

i ) Suitability: utilisation provides a measure of ‘spare’ capacity but only for the specific process setup (crude input and final product) that the refinery is adapted to. The final energy security impact will also be dependent on the flexibility of the refineries themselves to switch production – it is unclear whether a measure such as deep conversion capacity by itself adequately captures this.

ii ) Transparency: the measures of capacity and utilisation are transparent and no weighting factors are needed.

iii ) Availability of data: data on refinery capacity is publicly available e.g. EIA (2009) or the Oil and Gas Journal80, which gives various measures of capacity by unit/process.

iv ) Ability to forecast: future estimates of the expansion of refinery capacity are likely to be particularly uncertain (although this is similar across other large-scale, high-cost, energy infrastructure investments).

B 1.2 Measures of the importance of energy in the economy (Stage IV)

Example reference(s): various e.g. see SECURE (2008)

As discussed in section 2.2.3 the final price or physical unavailability welfare impact of energy insecurity, for a given energy source, ultimately depends on the importance of that particular form of energy within the economy – at its simplest level the lower the demand for that energy source the lower the impact, all else being equal.

A number of simple metrics have been proposed to assess this including:

• Share of a fuel in total consumption – e.g. oil use in transport, particularly as this use is highly inelastic and may serve as a suitable proxy for physical disruption. This is the simplest and hence most commonly used method for incorporating the demand-side – e.g. within net import dependence (see section B.1.3.1).

80 http://www.ogj.com

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• Energy intensity – either in terms of energy consumption per unit of GDP or per capita. In some cases benchmark values for energy intensity have been set against which country intensities can be compared (e.g. see ECN, 2007).

• Expenditure on energy – either per fuel, in total or as an intensity (e.g. energy expenditure per unit of GDP) – as this can provide an indication of the impact of price volatility.

• Price elasticity of demand – (either for energy in general or a specific fuel) as a measure of the responsiveness in the quantity demanded as a result of a change in price of the same fuel. A number of factors determine the elasticity, but it will generally be higher in the longer term:

- Substitutes: The more substitutes, the higher the elasticity, as consumers can easily switch from one good to another in response to relatively small price change81. In addition, the possibilities for substitution generally increase over the longer-term.

- Proportion of expenditure on a good: The higher the proportion of expenditure in a consumer’s total expenditure, the higher the elasticity, as a consumer cannot easily increase their total expenditure (at least in the short term) to account for price rises whilst maintaining consumption across a range of other goods.

- Necessity: The more necessary a good is, the lower the elasticity, as people will attempt to buy it no matter the price.


• The indicators do not measure any specific root cause.

• They are targeted at Stage IV of the causal mechanisms and hence provide a proxy for the final magnitude of the energy security impact.

• They target the end-use part of the energy supply chain for all energy sources.

i ) Suitability: as mentioned in section 2.2.3, aside from the total level of demand which is captured, the key elements that the indicators should reflect are a) the level of demand-side participation and b) the level of substitutability between fuels - over both the short and long-term:

- Neither the share of a fuel in total consumption, nor expenditure on energy adequately captures a) or b), but the former may provide a very limited proxy for the potential for substitutability (i.e. if the share in a specific end-use demand is low).

81 Related to this is the categorisation of the goods. In general, the broader the definition, the lower the

elasticity as this precludes substitution within the group – for example, energy in general versus electricity

generated via renewables.

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- Energy intensity may provide a limited proxy for a) in terms demand side participation over the long-term (such as the potential for efficiency improvements) if referenced against a benchmark value.

- Price elasticity of demand potentially provides the best proxy across both these elements in one indicator in the short-medium term, but is potentially a less useful proxy for their assessment over longer-term. For example, as long-term improvements in efficiency under a) may be driven by specific technical improvements, which are not adequately captured.

ii ) Transparency: the direct formulation of the indicators is straightforward and transparent, and no weighting factors are needed when the indicators are used on an individual fuel basis. However, when aggregating across fuels (e.g. based on the share of each in total primary energy) the physical energy measures implicitly assume that all fuels are weighted equally, which is unlikely to be true in terms of their substitution and demand-side participation possibilities. This is complicated further if trying to aggregate primary and secondary (e.g. electricity and heat) energy types. Even aggregating coal gas where both are destined for power generation can give potential problems for interpretation of as the average efficiency of gas and coal-fired plant is quite different. Adjustments may need to be made for delivered energy content rather than aggregating gross calorific content.

- Aggregation across fuels on a financial basis (e.g. via expenditure on energy) provides a slightly better proxy for these possibilities, hence improving the implicit assumption of equivalency across fuels. A separate issue is the comparison of energy intensity/efficiency against a benchmark value as this is a subjective decision (e.g. top 10% or 5%).

iii ) Availability of data: at a high level of aggregation data is widely available for the calculation of most of the indicators (from simple energy balances in Eurostat, PRIMES model, etc), with the exception of price elasticities. However, the other indicators (such as share of fuel in final energy demand) may only be regarded as useful proxies at a more detailed level of disaggregation – e.g. share of fuel in space heating for households, as opposed to share of fuel total household energy consumption – and data may be less readily available in this situation.

iv ) Ability to forecast: as the indicators are primarily based on standard energy balance data, this is relatively easy to forecast, however, projections of elasticities are generally more uncertain.

B 1.3 Dependence on non-domestic production

These are commonly used measures providing an indication of a country’s energy self sufficiency.

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B.1.3.1 Net Import Dependence

Example reference(s): IAEA (2005), APERC (2007), WEC (2008)

Net import dependence is a widely used single measure, which provides a broad indication of self-sufficiency of energy supply of the country. The indicator is calculated as the ratio of net imports to energy consumption if the country is a net importer, or the ratio of exports to production if the country is a net exporter. This can be undertaken on a fuel by fuel basis for oil, gas, coal and electricity (although it can also be applied to other fuels) or aggregated across all fuels.

The indicator is calculated on the basis of primary energy. Imports and exports are the amounts that have crossed the national territorial boundaries of a given country, whether or not customs clearance has taken place. A negative value for net imports indicates that the country is a net exporter.

• Oil: Quantities of crude oil and petroleum products imported or exported under processing agreements (i.e. refining on account) are included. Quantities of oil in transit are excluded.

• Crude oil, natural gas liquids (NGL) and natural gas are reported as coming from the country of origin; refinery feedstocks and petroleum products are reported as coming from the country of last consignment.

• Re-exports of oil imported for processing within the country are shown as exports of products from the processing country to the final destination.

• Coal: Imports and exports are the amount of fuels obtained from or supplied to other countries. Coal in transit is not included.

• Electricity: Amounts are considered as imported or exported when they have crossed the national territorial boundaries of a given country.

The value of net import dependence can then be combined with metrics of the importance of that fuel in the economy for example:

• Share of import in total primary energy consumption, or primary consumption of that fuel (see section B 1.2)

• The cost of imports as a share of GDP (see section B.1.3.2)

However, depending on the level of product breakdown that is being considered, some subtleties may be missed. For example, Iran is a net exporter of crude oil but due to its limited refinery capacity imports a considerable quantity of gasoline (IEA, 2007). Furthermore, the standard measure of net import dependence is an annual average. The timing of supplies is of particular relevance for electricity import dependence. The aggregate statistics may show that a country has no net import dependence, while it imports peak electricity and exports off-peak. The effective dependency during peak demand periods could then be high but goes unreported. There may also be a need to calculate net-import dependency at a supranational level, where two or more countries have a well integrated energy network. For example, when looking at Portuguese gas import dependency, the relevant statistic would be Iberian Peninsular import dependency as Spain and Portugal have a highly integrated network.

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• The indicator provides a proxy for the vulnerability of the energy system to physical unavailability risks (an upper bound in this case) of imported energy sources. The supply chain assessment in 2.6 shows possible energy security impacts associated with all root causes at the import stage.

• The indicator is targeted primarily at Stage II. It also includes a simple proxy for the impact on the demand sector at Stage IV (via the share in primary energy).

• The indicator targets the international production / processing, import and end-use elements of the energy supply chain for all energy sources.

i ) Suitability: the relevance of the indicator depends on whether a physical unavailability or price impact prevails for a given energy source. For example, in more liberalised market environments (particularly oil), price risk tends to dominate energy security concerns. Hence, energy security impacts from extreme events or resource concentration will ultimately manifest as price impacts on welfare and so the notion of import dependence is of more limited use. By contrast, for natural gas many regions of the world still have limited transportation options (primarily pipeline) and regulated natural gas prices or, as in the case of most OECD countries, prices indexed on oil. In this case, physical unavailability is a major concern and the notion of import dependence can be a useful measure of energy security (IEA, 2007) – see section B.2.5.2.

- Whilst the use of the indicator is normally associated with resource concentration issues, the targeting effectively provides an upper bound on physical unavailability risks at the import stage across all root causes, without giving any further information on the likelihood of an impact from a specific root causes (i.e. at Stage I).

- The indicator does not consider the flexibility of the rest of the supply chain at Stage III and only provides a very simple measure at Stage IV.

ii ) Transparency: the indicator is transparent and objective on a fuel by fuel basis as no weighting factors are needed. However, some implicit weighting is undertaken when aggregating across fuels on a primary energy basis.

iii ) Availability of data: basic data is relatively straightforward covering simple energy balances (primary consumption, domestic production, etc) from Eurostat for historic data and most energy models for projections (e.g. PRIMES, POLES). However, there are a number of potential complications:

- Additional data may be required for electricity to identify peak hour dependency. This data is available from national grid operators.

- When aggregating more than one fuel, additional data may be required, such as average generating efficiency by fuel. This is also available from Eurostat and PRIMES.

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- There is a need to distinguish reported statistics on the ‘paper’ trade of products from statistics on the physical movements. For example, in 2006 Spain reported significant pipeline natural gas imports from Norway, though no molecules of Norwegian gas would have transited to Spain.

iv ) Ability to forecast: as mentioned above most energy balance data is relatively straightforward to forecast and is available from existing EU models.

B.1.3.2 Net energy import bill

Example reference(s): WEC (2008)

This indicator attempts to measure the ‘vulnerability’ value of net imported energy as a share of GDP, as opposed to the physical quantity alone. It is affected primarily by the level of import dependence, but also by the energy intensity of the economy, and the cost of imports – including the effect of the € / $ exchange rate, given that global commodities such as oil are priced in $.

It is expressed as:

• EB = EVEB / GDP = NEID x INT x ASC x EXC

Where:

- EVEB = value of net energy imports expressed in €

- GDP = Gross Domestic Product in €

- NEID = net energy import dependence

- INT = primary energy intensity of the economy

- ASC = Average supply cost = $ value of energy imports / NEID

- EXC = exchange rate = € / $


• The indicator provides a proxy for the vulnerability of the energy system to both physical unavailability and price risks (an upper bound in this case) of imported energy sources. The supply chain assessment in 2.6 shows possible energy security impacts associated with all root causes at the import stage.

• The indicator is targeted primarily at Stage II. However, the proxy for impact on the demand sector at Stage IV is energy intensity and expenditure on energy as opposed to a simple share in primary energy (as is used for the net import dependence indicator).

• As per net import dependence, the indicator targets the international production / processing, import and end-use elements of the energy supply chain for all energy sources.

i ) Suitability: as per net import dependence, but the metric of energy intensity and expenditure provides a better proxy for the importance of demand in the economy. Whilst the inclusion of the cost of energy also captures some element of energy security price impacts it only applies this to the imported portion of

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energy expenditure. In competitive global markets such as oil, price spikes would carry through into the cost of energy across both domestic and imported supply, hence the indicator does not capture the full extent of these price impacts. In addition, the price of energy itself is not the primary cause of energy insecurity but its volatility (e.g. rapidly rising prices), which is not captured by the indicator. In a similar manner to net import dependence, the coverage of multiple root causes does not provide additional information on the relevant contribution/importance of the underlying root causes to the final measure of insecurity.

ii ) Transparency: the indicator is transparent and objective when used on a fuel by fuel basis as no weighting factors are needed for the additional components of the indicator. When aggregating across fuels the use of expenditure on energy (as opposed to share in primary consumption) is a slightly better representation of equivalency across energy sources.

iii ) Availability of data: as per net energy import dependence these are relatively straightforward. However, there are additional requirements in the form of projections of energy prices and exchange rates. Energy price projections are generally a key driver (usually assumed exogenously for primary fuels such as oil) within overarching energy models.

iv ) Ability to forecast: the need to forecast energy prices and exchange rates can add to the uncertainty of the final value, but these variables are the focus of standard modelling assessments.

B.1.3.3 Domestic production to consumption


This is a simple ratio of the level of current domestic production of an energy source compared to the level of gross inland primary energy consumption (final consumption for electricity) and is an indication of self-sufficiency of energy supply of the country. This can be calculated on a source-by-source basis or across all energy sources at once. It can be viewed as a similar measure to net import dependence, but from the domestic rather than external perspective.


• The indicator provides a proxy for the vulnerability of the energy system to physical unavailability risks (an upper bound again in this case) of domestically produced energy sources. However, for domestic production only the extreme events and inadequate market structure root causes are considered important.

• The indicator is targeted primarily at Stage II. It also includes a simple proxy for the impact on the demand sector at Stage IV (via the share in primary energy).

• The indicator targets the domestic production element of the energy supply chain for all energy sources.

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i ) Suitability: as per net energy import dependence - although at the domestic production stage there are fewer relevant root causes.

ii ) Transparency: the indicator is transparent and objective for an individual fuel as no weighting factors are needed. However, as per net import dependence implicit weightings are made when aggregating across fuels.

iii ) Availability of data: as per net import dependence these are straightforward covering simple energy balances (primary consumption, domestic production, etc) from Eurostat for historic data and most energy models for projections (e.g. PRIMES, POLES).

iv ) Ability to forecast: as mentioned above most energy balance data is relatively straightforward to forecast and is available from existing EU models.

B 1.4 Indicators of investment in adequate supply

As discussed in section 2.4 a key root cause of energy insecurity is insufficient investment in new capacity or infrastructure. A number of potential indicators related to this issue are outlined below.

B.1.4.1 General business environment

Example reference(s): Various – e.g. World Bank et al (2009)

As investment in new capacity is an issue in all sectors, including upstream energy production, an indicator of a country’s ease of investment environment can potentially provide an overarching proxy of the ability to realize new investment. A range of options already exists including the World Bank’s Doing Business rankings82, Standard & Poor’s Sovereign Ratings83, etc.


• It is a proxy for the root cause type - insufficient investments in new capacity.

• It is targeted at Stage I of the causal mechanisms, as it is indication of the likelihood of investment or lack thereof.

• The indicator does not target any specific element of the energy supply chain

i ) Suitability: whilst the indicator provides a high-level overview of the business environment it is not directly related to the energy sector84. Whilst a good

82 http://www.doingbusiness.org/economyrankings/ 83 E.g. see

http://www2.standardandpoors.com/spf/pdf/fixedincome/Euro_Soveriegns_June2006_Text_%20July%20Up

date.pdf?vregion=eu&vlang=en 84 The most relevant sub-index is Dealing with construction permits: Procedures, time and cost to obtain

construction permits, inspections and utility connections.

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business environment is important for ensuring investment is made, it is difficult to argue that this represents a close proxy for investment in energy infrastructure specifically. In addition, the indicator is not applicable as a measure of energy insecurity by itself, it would need to be related to some element of the energy supply chain (e.g. international production) to understand how likely this element would be to suffer from insufficient investment.

ii ) Transparency: the indicators contain a significant level of subjectivity in the assessment and weightings (when drawing on the aggregate index). In addition, a subjective judgement would need to be made for how this factor is applied to scale the risks of insufficient investment in a particular part of the supply chain (e.g. in a similar manner to political stability in section B.1.6.2).

iii ) Availability of data: this straightforward and based on existing published indices as mentioned above.

iv ) Ability to forecast: a key concern is the uncertainty with projecting these indices into the future, particularly over the long term.

B.1.4.2 Patents in energy technology sector


Vulnerability to energy security impacts may be a consequence of the inability of a country to control or develop advanced energy technologies. This covers the full spectrum of supply and demand-side energy technologies as well related ancillary technologies such as information and communication technology85.

The number of patents registered in a country by fuel or technology type may be an indirect proxy for this. The range of actors registering the patents is also of importance e.g. those by national energy companies may provide a better indication of technologies likely to be deployed commercially than patents covering academia as well (however, the overlap and relationships between the two types of actor vary across Member States).




• Depending on the disaggregation of the different types of patent being applied for the indicator could cover various parts of the energy supply chain including domestic production (e.g. electricity generation technologies) or end-use (e.g. energy efficiency or demand-side participation technologies).

85 Poor information management was highlighted as a particular issue during the Italian blackout of

September 2003 (WEC, 2008).

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i ) Suitability: the main issue with the use of patents as a proxy for insufficient investment is that there is no direct link between the number of patents filed and subsequent steps, including commercialisation and actual investment in infrastructure – and in particular given that much of the required investment is in existing as opposed to R&D-level technologies. Even where the patents filed are deemed relevant to the energy sector (or a specific technology such as biomass as shown in WEC (2008) breakdown) simple counts of patents provide no link between the technology and its potential long-term importance in ensuring supply meets demand. For example, multiple patents can be filed providing marginal improvements to an existing technology, whereas a single patent can be filed for a novel, but highly promising technology. Finally, the energy industry is international and the country of development often bears little relationship to where the technology is applied. At best, the total number of (unique) patents worldwide might be a proxy for general technological improvements.

ii ) Transparency: the simple counts of the indicator are transparent, however, the grouping of specific patents by e.g. technology type is likely to be arbitrary in many cases.

iii ) Availability of data: gathering sufficient information on number of patents filed and technology types across all EU countries is likely to be highly impractical – although filing with overarching bodies such as the European Patent Office is increasing. In addition, it is difficult to categorise the patents by sector or technology.

iv ) Ability to forecast: it is extremely difficult to project how this might evolve in future with any certainty.

B.1.4.3 Ratio of investments to turnover


The ratio of investments to turnover can represent a proxy of potential vulnerability in the energy sector when it is maintained under a certain level. In the electricity and gas sectors, this rate has been decreasing steadily over the past years falling from 10.3% in 1998 to 5.5% in 2004 within the European Union (WEC, 2008).




• It can in theory be targeted at any element of the energy supply chain – for example, examining the investment in international production capacity, domestic production capacity, transportation capacity, etc.

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i ) Suitability: by itself the indicator does not provide a direct measure of insufficient investment unless some benchmark can be set to determine what is considered ‘sufficient’ and is also strongly impacted by the impact of energy prices on turnover. This is also likely to vary considerably across different parts of the energy supply chain and under different circumstances. For example, new renewable electricity generation will likely require considerably higher investment in grid infrastructure compared to investment in an equivalent capacity of fossil generation (due to the differences in geographical location of the new capacity). One possibility is to examine the level of new investment required within various modelling scenarios such as those in PRIMES86 and contrast this against historic and near-term estimates of actual investment.

ii ) Transparency: the indicator itself is fairly transparent, but a significant element of subjectivity is likely to be introduced in determining, how likely investment is to occur in future (e.g. a 5-year investment programme which has just commenced can be viewed with higher certainty than one which is not due to start for several years time).

iii ) Availability of data: accurate data on actual investment patterns is likely to be difficult to obtain even within the EU, where it is largely dependent on sufficient reporting by private companies. Outside of the EU, data availability and transparency may be equally problematic – for example, in terms of understanding investment patterns in OPEC countries, but one benefit is that such investments are likely to be relatively few in number and large scale.

iv ) Ability to forecast: future estimates of investment are particularly problematic. As mentioned above most energy modelling scenarios deployed by Government assume that the investment takes place whereas our interest is in the level of spend that will actually take place. In the near term, potential options might be to extrapolate from historic trends in a top-down manner, or in a bottom-up compile estimates of planned and ongoing investment on a company-by-company basis (with obvious implications for data quality and availability).

B.1.4.4 Market price signals


In a well functioning market, price functions as a balancing mechanism for demand and supply. However, particularly volatile prices can nevertheless lead to negative short-term welfare impacts on the economy. Direct measures of price volatility have been used as a limited proxy for energy security, in particular for oil given it is globally traded and the dominant energy carrier in many parts of the world.

86 As these models operate under the assumption that sufficient supply is always available, i.e. the

investment will always take place, with only the cost of this varying. This can be determined either in terms

of a general equilibrium between supply and demand in CGE models or the ability of supply to meet a given

level of demand at least cost, in optimisation models.

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However, forward prices for energy can constitute a proxy for the market signals provided to energy suppliers to change their pattern of operation of existing infrastructure or invest in new capacity. For example, the forward spark spread87 is important for market participants with gas-fired electricity generation in determining whether it is profitable to generate (in the medium term by de-mothballing existing capacity) or for potential new entrants in terms of building new plant.


• It is a proxy for the price impacts of energy insecurity from the root causes of insufficient investment and supply shortfall associated with resource concentration

• It is targeted at Stage I of the causal mechanisms for these root causes.

• Forward prices target the specific element of the energy supply chain for which the prices are relevant; this primarily relates to international production / processing and imports, domestic production including electricity generation, and end-users.

i ) Suitability: given that forward energy prices, in theory, capture information related to a number of root causes of energy insecurity that lead to price impacts88 it is difficult to separate out the impact of insufficient investment only. The robustness of forward prices as an indicator is also based on the strong assumption of a deep, liberalised market, with a high degree of information availability89. This may be reasonable for markets such as oil and electricity (the latter only in some countries), but is not the case for all energy sources. Finally, it is difficult to consider the forward price by itself as a measure of energy insecurity without comparing it to some benchmark – e.g. the price that would be sufficient to promote sufficient investment in new capacity.

ii ) Transparency: the forward price is itself a simple measure, but its basis is complex. There have also been a number of concerns that spot and forward energy prices have been subject to market manipulation90, making their measure even less transparent. In addition, introducing a benchmark against which this is compared will introduce an element of subjectivity.

87 Theoretical gross income of a gas-fired power plant from selling a unit of electricity, having bought the

fuel required to produce this unit of electricity. All other costs (operation and maintenance, capital and other

financial costs) are covered from the spark spread. Similarly, for dark spread and coal electricity generation.

In addition, given the operation of the EU ETS there is need to account for the price of carbon - known as

the clean dark and clean spark spreads. 88 As well as other non-energy security related factors – such as a rise in the cost of fuel transportation, etc. 89 One of the main problems with the few forward energy markets is a lack of depth more than one year

out, in addition prices can also be set by only one or two dominant actors. 90 For example, see the UK Financial Services Authority work on market abuse in commodity markets

http://www.fsa.gov.uk/pubs/newsletters/cm_newsletter1.pdf

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iii ) Availability of data: Forward energy prices are widely available (although in some cases the information is proprietary). For example, the NYMEX91 and ICE92 exchanges report standard oil price volatility, which is a direct indicator of market volatility, as well as a range of forward prices.

iv ) Ability to forecast: the ability for forward prices to act as a suitable proxy for energy insecurity is less robust moving forward in time due to increasing levels of uncertainty, whereas insufficient investment is primarily a long-term root cause of insecurity. Over extended periods of time forward prices also tend to revert to mean long-term trends – making them a less useful indicator.

B 1.5 Measures of diversity

Example reference(s): Stirling, (1999) Jansen et al (2004), Awerbuch (2003, 2006) APERC (2007), IEA (2004, 2007), SEI (2006)

Diversity can, in theory, increase a system’s resilience by making it more robust. In the context of energy security a more diverse energy system can broadly be thought of as a hedge against ignorance – e.g. against a disruption in supply (Jansen et al 2004) or against the market power of energy suppliers (IEA, 2007). To-date work on diversity has followed two main types of quantitative approach:

• The first considers that given the uncertainty and ambiguity surrounding our knowledge of the energy system, ignorance ultimately prevails. The notion of diversity should therefore be applied in its purest sense as, with respect to the energy system, increasing diversity minimizes the risk of disruption to a single part of the system and increases the likelihood that an alternative technical solution may be found (Stirling, 1999).

• The second considers that past events offer a guide to understanding future risks using probability theory – most efforts in this area are based around Mean Variance Portfolio Theory (MVP). This aims at minimizing risk for a given level of return, or conversely, maximising returns for a given (accepted) level of risk (e.g. with respect to a portfolio of, rather than single, electricity generation options) (e.g. see Awerbuch (2003, 2006), SEI (2006)).

B.1.5.1 Measuring diversity in a context of ignorance

Stirling (1999) identifies three key elements of diversity that a quantitative index should ideally cover:

• Variety: referring to the number of categories into which the quantity in question can be partitioned.

• Balance: referring to the pattern in the apportionment (spread) of that quantity across the relevant categories.

91 http://www.nymex.com/index.aspx 92 https://www.theice.com/homepage.jhtml

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• Disparity: refers to the nature and degree to which the categories themselves are different from each other.

All three are crucial in fully explaining diversity, but only a number of effective dual property metrics exist capturing the elements of variety and balance. Stirling concludes that disparity is inevitably subjective and depends on the choice of particular performance criteria, but has developed an “Integrated Multi-Criteria Diversity index” to better formalize the assessment in this area, although issues of subjectivity still remain.

Variety and balance are captured in the family of indices, which include the:

• Shannon-Wiener Index (SWI) = ∑− i ii pp ln

• Herfindhal-Hirschmann Index93 (HHI) = ∑i ip2

In both indices pi can represent either the:

• Share of fuel i in the energy mix – i.e. the diversity of fuel supply in a market/country

• Share of supplier i in the total supply market for a fuel – i.e. diversity of supply to a specific market/country

The implications of the element of energy security being measured in each are very different and discussed further below.

The higher the value of SWI and the lower the value of HHI the more (dual property) diverse the system is. HHI varies between 0 and 194, whilst the SWI increases with an increasing number of options95.

In the absence of a suitable measure of disparity the categorization of options can influence the outcome of these indices. For example, classification of supply into fossil and renewables will lead to different results compared to the same energy mix classified as oil, coal, gas and renewables. The situation is similar if suppliers are treated individually or as a group, for example OPEC versus its individual member countries, and hence a potential option is to examine both cases as per IEA (2007).

Stirling (1999) favours the SWI as a ‘purer’ mathematical assessment of diversity for 2 reasons:

• It retains rank ordering under variations of logarithm base, whereas the rank ordering of different systems changes as the exponent of the HHI changes. As there is no fundamental argument why the exponent should be 2, this raises

93 Also known as the Simpson index in Ecology. 94 Moving from a very large amount of very small firms to a single monopolistic producer. Decreases in the

HHI generally indicate a loss of pricing power and an increase in competition, whereas increases imply the

opposite. 95 For ease of comparison the complement of HHI (1-HHI) can be compared against a normalised version of

the SWI (by dividing by the maximum index number, for i = 1,2.. N this equals ln(1/N) – such that both

indices vary from 0 to 1 with an increase in the score indicating higher diversity.

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doubts with regard to the firmness of the results obtained, since by changing an apparently non-related parameter, the outcome will differ.

• The SWI displays the property of additivity with respect to taxonomy. This means that when classifying options based on several criteria, the index score for the system classified according to criterion a, plus the index score for the system classified according to criterion b should amount to the same as the index score for the system classified according to the combined criterion ab. This is mathematically represented as f(ab)=f(a)+f(b), with a and b sets of options under different classifications and f the index or function in question.

Diversity of energy supply in a market/country

Various studies have applied the SWI or a variation of it to assess the diversity of fuel supply in a market (e.g. Grubb et al 2006, Jansen et al 2004, APERC 2007), by comparison far fewer have used the HHI (e.g. Neff 1997). Although both indices display large similarities as mentioned above Stirling (1999) favours the former on fundamental mathematical grounds.



• It is targeted at Stage IV of the causal mechanisms and hence provides a proxy for the final magnitude of an energy security impact earlier in the supply chain.

• The indicator is targeted at the end-use demand element of the energy supply chain – for all energy sources, as it measure the diversity of supply in a given market rather than to it.

i ) Suitability: this measure of diversity is inherently appealing and has attracted significant policy attention because of its highly precautionary approach (being a hedge against ignorance). However, diversification carries a cost of its own and it is unclear from a policy maker’s perspective what should be diversified (e.g. the fuel mix of a country, a given sector, the types of technologies used, etc) and to what extent.

The underlying assumption is that the greater the diversity in the market the lower the energy security impact. However, it is instructive to look at what the ES framework is ideally trying to measure at Stage IV in the causal links – in this case the extent of demand-side participation and fuel substitution capacity. It is arguable that diversity is an indirect proxy for substitution possibilities, but in reality, the specific risks and potential for substitution vary considerably between fuels and the types of end-use. As there is no formal measure of disparity in the indicator (to capture a factor such as substitution) it is not possible to capture this potential directly. This is particularly problematic when the indicator is categorised at a high-level (e.g. the diversity in total primary energy supply across a country), but may be less so at a more disaggregated

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level where the underlying assumption about substitution possibilities is more robust96.

An additional complication is that whilst the targeting of the indicator in end-use should theoretically capture both price and physical unavailability impacts earlier in the supply chain, some fuels can display correlations in their price development (e.g. oil and gas) with energy security price impacts spilling from one market to another. Hence, whilst greater diversity between two fuels may decrease the risks associated with physical unavailability (assuming realistic substitution possibilities exist) it may still not mitigate against price impacts.

ii ) Transparency: whilst the formulation of the indicator itself is relatively straightforward, the classification of the categories within it (e.g. the breakdown into different groups of fuel types) is ultimately arbitrary as there is no objective measure of disparity - and in practice, it may be also be limited by data availability.

iii ) Availability of data: this is relatively straightforward as they depend primarily on widely available energy balance information. The only potential complication is the level of disaggregation in the diversity categories – for example to measure diversity at a detailed sub-sectoral level by specific end-use.

iv ) Ability to forecast: straightforward as based on widely available energy balance data.

Diversity of energy supply to a market/country (i.e. resource concentration of suppliers)

As described in section 2.5 market concentration is a problem in terms of energy security because the resulting lack of competition entails market power for the large suppliers, thereby harming competition and efficient market. As market concentration is essentially a lack of balance and variety in suppliers, it can be measured by means of a dual property diversity index. The SWI and HHI can both be used to measure market concentration by taking the suppliers’ market shares in the fuel market under scrutiny as input for the indices.

In literature on (energy) market concentration, however, the HHI is almost exclusively used (Grubb et al. 2006, IEA 2004, IEA 2007, Neff 1997) – by drawing a parallel to the use of the HHI as proxy for market power in competition law and antitrust assessments in other markets97.

As with any dual-property index of diversity, much depends on the classification when applying one of the diversity indices described in the previous section. In particular, the supply role of international private companies in energy markets versus nation states, and the treatment of cartels versus individual countries, may yield very

96 E.g. long-term diversity in the share of fuels for domestic space heating – if categorised into electricity

versus other fuels as the former can be brought in as a substitute in both the short and long-term. 97 Although there seems to be no other obvious rationale for this, given the argumentation above for using

the SWI.

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different results. IEA (2007 – see section B.2.5.2) looks at the situation where OPEC is treated as a single supplier as well as where it is broken into its individual member countries.

In addition, it is often noted that there is no distinction between domestic and imported energy (particularly for oil) when viewed from an energy security perspective (Toman, 2002). However, this assumes no price differential between domestic consumption and export.

The level of supply available for export is also constrained by physical infrastructure (e.g. pipeline capacity). Hence, many studies (e.g. IEA 2007) use a measure of net export capacity to account for physical limitations on the level of supply that a country can provide for export.


• It is focused on the assessment of price impacts of energy security only from the resource concentration root cause

• It is targeted at Stage I of the causal mechanisms.

• The indicator is targeted at the international production / processing and import elements of the energy supply chain for all energy sources.

i ) Suitability: In contrast to diversity of supply within a market, diversity of supply to a market is a more precisely targeted application of the indicator. The indicator can be seen as directly relevant measure of one of the key root causes of energy insecurity – as there are clear links to the application of the HHI to assess market power in other areas. However, its focus on price effects means that it is only really applicable in the case of highly integrated liberalised markets where these manifest instead of physical unavailability impacts. It is therefore more relevant in the case of oil and coal, but only partially applicable in the case of gas where the current market is more fragmented. It also makes it difficult to assess the applicability of an indicator in other fragmented or developing markets such as for biofuels (which may be of increasing importance in the future). In addition, the targeting of this form of the indicator within the causal links takes no account of the flexibility in the rest of the supply chain or the importance of demand-side options in reducing the magnitude of the energy security impact.

ii ) Transparency: as per diversity in a market, whilst the formulation of the indicator itself is relatively straightforward, the classification of the categories within it is arbitrary as there is no objective measure of disparity. In this case whether market power rests at the individual country level, cartel level, or even in some cases at multinational company level – and hence the category groupings should be adjusted accordingly.

iii ) Availability of data: this is generally available at the country level, for example, IEA (2007) bases the former on the IEA’s World Energy Outlook scenarios.

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However, a potential issue is the identification of energy sources (or the share of such sources) which are genuinely applicable to the indicator (i.e. operate in what can be considered a competitive market). In IEA (2007) this is done for gas by separating out the share of gas demand from oil-indexed contracts, however, only high-level estimates (100%, 50/50%, 0%) were available.

iv ) Ability to forecast: the key issue related to the application of this indicator is the uncertainty in the assessment of the future supply of energy from particular countries at global level.

B.1.5.2 Probabilistic measures of diversity – Mean Variance Portfolio theory

As mentioned above MVP aims at compiling investment portfolios to minimize risk for a given return or maximize returns for an accepted level of risk. Risk in this context is defined as the standard deviation in expected returns. For the total risk of a portfolio, next to the weighted sum of the individual risks, the covariance of individual returns is taken into consideration.

For a simple 2-stock (or 2-technology) portfolio the Expected portfolio return is given by (from Awerbuch and Berger 2003 and Awerbuch et al. 2006):

• E(rp) = xi * E(r1) +x2 * E(r2)

Where:

• E(rp) is the expected portfolio return

• xi is the share of asset i in the portfolio

• E(ri) the expected return for asset i.

- Specifically; the mean of all possible outcomes, weighted by the probability of occurrence; e.g. for asset i: E(ri) = ∑piri, with pi the probability that outcome i will occur, and ri the return under that outcome.

The risk is a function of the individual asset-risks, as well as their correlation;

• 21122122

22

21

2 2 σσρσσσ xxxxip ++=

With:

• ρ12 the correlation coefficient98 between the two return streams,

• σi the standard deviation of the periodic returns of asset i.

Most efforts in the energy sector have focused on the MVP application to electricity generation (see Awerbuch et al (2003, 2006) and SEI (2006)). For example, it can be

98 The correlation coefficient between the portfolio components (the degree to which the (price) fluctuations

correspond) either dampens or amplifies the risk, depending on whether the correlation is positive or

negative.

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applied to the energy system to look at the change in expected return from the introduction of renewables, which may have higher average unit electricity generating costs but less variation in operating costs compared to fossil generation and volatile fuel prices, a trade-off, which may otherwise be overlooked.

A slightly different approach to the application of MVP (e.g. see Neff, 1997, Lesbirel 2004) distinguishes between risks related to the market as a whole and those to individual suppliers or sources. Risk measured as the (co)variance in either import prices or quantities can be disaggregated into a component that displays movements corresponding to the entire market, and a component, which is due to specific circumstances of individual suppliers. The latter can be reduced by diversification, whereas the former cannot.


• MVP is principally concerned with energy security price impacts. Depending on where it is applied in the energy supply chain it can in theory can account for all root causes that lead to price impacts up to this point (on the assumption that the risks of such price impacts are adequately captured by the standard deviation in expected returns)

• For most root causes the targeting of MVP is either at Stage I, II or a combination of the two depending on the factors that make up the assessment of risk. In addition, as MVP considers a portfolio of options it explicitly addresses Stage III (vulnerability of rest of supply chain to knock-on effects).

• The indicator can be targeted at various elements of the energy supply chain – for all energy sources. For example, in terms of electricity generation options or more general energy supply to a market at the international / domestic level).

i ) Suitability: the unique element of this approach, which also constitutes its main point of criticism, is that it assumes past data forms a sufficiently firm ground for future projections. Data on costs of fuels in the past are used to estimate the risk and magnitude of future price movements – and hence energy security impacts. This has been opposed by Stirling (1999), who argues that under conditions of ignorance, no basis exists to assume that historic patterns will repeat themselves99.In addition, there are limitations to the adoption of portfolio theory in the case of real assets. Portfolio theory assumes perfect market conditions, while such conditions do not exist in capital markets, the situation is even less applicable in energy infrastructure markets where technology is often long lived and comparatively inflexible. In addition,

99 More recently, an approach that aims at combining the probabilistic mean variance portfolio approach and

a more precautious method advocated by Stirling has been developed (Awerbuch et al. 2006). This method

consists of adding the outcomes of both methods on a weighted basis, where the weight factor represents

the level of trust in historic trends as a guide for the future.

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compared to financial securities, energy technologies are not indefinitely divisible so there are limits to the level of diversity within the portfolio (Awerbuch and Berger, 2003). One benefit of MVP compared to the standard dual-property diversity approaches in section B.1.5.1 is that it does capture correlation in price effects between energy sources.

ii ) Transparency: a key issue is that MVP is an optimization method rather than an indicator per se. It provides information on the ‘efficient frontier’, a limit in the cost-risk domain beyond which (energy) investment portfolios cannot be made less costly without increasing their risk, or cannot be made more risk adverse without increasing their cost. Moving along this frontier represents different trade offs between risk and cost. A metric on one aspect could be produced, but with a subjective judgment fixing the level of the other.

iii ) Availability of data: as the indicator is based on an analysis of historic data this is generally more readily available and less uncertain than projection data. However, a key difficulty is in accurately assessing covariance between prices or quantities, particularly when trying to separate out the component of market-wide risks versus risks between different types of energy source or supplier.

iv ) Ability to forecast: the indicator explicitly assumes that historic trends offer a suitable guide to future risks.

B 1.6 Other vulnerability indicators

B.1.6.1 Market liquidity

Example reference(s): IEA 2004

As discussed in section 2.2.4 market liquidity relates to the capacity of markets to cope with fluctuations in supply and demand. The more liquid a market is, the more its participants will benefit. Liquidity covers a variety of issues including transparent pricing and the ability to switch suppliers (of the same energy source) quickly with low transaction costs, whilst obtaining a similar price.

IEA (2004) introduced a measure of one potential factor that could constrain a country’s ability to switch suppliers; the size of its demand in relation to the size of the supply market. If there are constraints to this then the risks will by higher than, for example, would be suggested by a measure of market concentration alone (see section B 2.5).

The estimate of resource concentration as calculated by the HHI was multiplied (or scaled) by:

• fPe1

where Pf = ‘supply availability’, defined as the total supply available on the market divided by the consumption needs of the country considered.

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Hence, where market supply is high relative to the country’s demand the overall risk is brought back down to the level of market concentration alone as illustrated in the figure below, but it can never contribute to an overall reduction in the risks associated with market concentration.

Illustration B - 57 Relationship between concentration risk and liquidity

Source: IEA (2004)


• Market liquidity is not a measure of the impact of energy insecurity in itself and so does not target a specific root cause, but helps determine the magnitude of a price or physical unavailability impact – as a more limited ability to switch suppliers will exacerbate the impact.

• It targets Stage III of the causal mechanisms.

• It is not targeted at a specific element of the energy supply chain, but can broadly be applied to those indicators which examine supply at the International production / processing or domestic production level (such as the measure of market concentration described above) – for all energy sources.

i ) Suitability: the inability to easily switch to alternative suppliers of the same energy source is clearly a factor that can aggravate an earlier price or physical unavailability impact. As noted in IEA (2004), the formula above is only one possible way to represent this, however, it is unclear on what underlying basis this particular function serves as a proxy of the ability to switch suppliers. There is also the question of whether the indicator should only be applied to

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the total level of EU energy demand (given the single energy market) or at more disaggregated level (e.g. by Member State). In addition, as noted above the ability to switch suppliers is only one issue associated with market liquidity.

ii ) Transparency: the incorporation of this indicator into an aggregated indicator raises the issue of how its impact should be weighted against the other components of the indicator.

iii ) Availability of data: in this case the data requirements are effectively zero as this particular liquidity function adapts the existing data from another component of the indicator focusing on resource concentration.

iv ) Ability to forecast: as per availability of data.

B.1.6.2 Political stability

Example reference(s): IEA (2004, 2007), Jansen et al (2004), ICRG (2009)

The political stability of supplier countries can be of significant importance to the security of energy supply. Changes in Government can lead to physical disruptions or price impacts due to the re-evaluation of existing contracts. A number of studies have incorporated a measure of ‘political stability’ from various sources by using this to scale the risks of resource concentration (see B.1.5.1) for specific countries supplying to the market.

• IEA (2004) uses information from ICRG’s (2009) aggregated political risk indices100 to assign a value of 1 to 3 to each supplier country (1 being the lowest level of risk and hence leaving that country’s contribution to the HHI or SWI unchanged).

• IEA (2007) uses a similar approach, but with political stability based on two of the World Bank’s Worldwide governance indicators101; ‘Political stability and absence of violence’ and ‘Regulatory quality’ as these are deemed to be of most relevance from an energy security perspective.

• Jansen et al (2004) base their measure of long-term socio-political stability on the UNDP’s Human Development Indicator (HDI) 102. This is composed of 3 dimensions; life expectancy at birth; adult literacy rate combined with the gross enrolment ratio to measure the dimension ‘knowledge’; and GDP at purchasing power parity (PPP).


• Political stability is a proxy for the price or unavailability impacts from the root causes of resource concentration, extreme events (acts of terrorism, strikes) and insufficient investment in new capacity.

100 Comprised of measures such as: government stability; socioeconomic conditions; investment profile;

internal conflict; external conflict; corruption; military in politics; religious tensions; law and order; ethnic

tensions; democratic accountability; bureaucracy quality, etc. 101 http://info.worldbank.org/governance/wgi/index.asp 102 http://hdr.undp.org/

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• It targets Stage I of the causal mechanisms, given that it helps determine the likelihood of an energy security event occurring (the less politically stable the higher the likelihood).

• The indicator is targeted at the international production / processing and import parts of the energy supply chain.

i ) Suitability: it is self-evident that a lack of political stability has the potential to lead to disruptions in imported supplies - leading to physical unavailability or price impacts depending on the nature of the market for the fuel. However, given its inherently subjective nature, the key question is to what extent are available indices a suitable proxy for this issue? For example, Jansen et al (2004) argue that general indicators of well being such as the HDI are less vulnerable to “shifts in ideological denomination of political regimes and are better measures of long-term socio-economic stability”, whereas this is arguably less appropriate in the shorter term. In addition, even where the proxy is considered ‘reasonable’ many of these measures may simply give the wrong indication – a standard comparison being the UK and Saudi Arabia in the 1980s, where the former scored as more stable, but was subject to the coal miners’ strikes, whereas the latter had no supply problems.

ii ) Transparency: the indicators themselves are inherently subjective.

iii ) Availability of data: as described above a range of both publicly available and commercially confidential indices are available.

iv ) Ability to forecast: the key issue with this indicator is the uncertainty of projection into the future, particularly over the medium to long-term.

B.1.6.3 Resource/reserve to production ratios (RPRs)

Example reference(s): WEC (2007), IAEA (2005)

These indicators provide a simple measure of the amount of time a country’s existing reserves/resources could be sustained at current levels of production – either to meet domestic demand or to supply to the external market. This may cover non-renewable fuels such as oil, natural gas, coal and uranium.

There are three broadly accepted reserves definitions that can be used to reflect long-term potential, with increasing levels of uncertainty. These are Proven (or P90) which has at least a 90% chance of being recovered under current economic and political conditions with existing technology, Probable (P50) and Possible (P10). Both P50 and P10 take account of possible changes in economic and political conditions and potential improvements in recovery technologies. Resources include reserves, but also cover less tangible estimated additional resources and speculative resources.


• The indicator does not measure any of the root causes.

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• It can potentially be considered a measure Stage II – impact on primary production of fuels.

• It is targeted at the international production/processing and domestic production elements of the energy supply chain for all non-renewable primary fuels (i.e. excluding biomass).

i) Suitability: RPRs do not target a specific aspect of the ES framework. It is the resource concentration of the supply of existing fuels that is argued to be a key root cause of energy security as the extended timescales over which fossil depletion takes will be gradual enough for the economy to adapt – and hence will not lead to direct price or physical unavailability impacts. The effect of declining domestic production within the EU is captured by existing energy models – hence a lower level (as could be measured by an RPR) is not the root cause in itself, but one factor that exposes the EU to other root causes of potential energy insecurity, such as an increased reliance on imports.

ii) Transparency: the indicator is not particularly transparent given the uncertainty, and to some extent subjective, judgment surrounding what is recoverable under a given set of economic, political and technological conditions.

iii) Availability of data: the definition of reserves/resources varies on a sliding scale with increasing levels of uncertainty about the availability of the resource and its feasibility of extraction (covering both technical and economic factors). However, the interpretation of these definitions can vary significantly and can be problematic, particularly with respect to non-OECD National Oil Companies (there are limited incentives for a country/company to report the quantities accurately even where this is possible). Intra-EU RPRs are generally better understood and there is less ‘flexibility’ in interpretation. Data is not compiled for the EU by Eurostat, but key sources of information for this indicator are the third party Survey of Energy Resources (WEC, 2007), BP Statistical Review103, Cedigaz gas statistics104 and the US Geological Survey105.

iv) Ability to forecast: forecasting the RPR over the longer-term is difficult given the uncertainty surrounding changes in economic, technological and political conditions, as well as the discovery of new reserves.

B.1.6.4 Non-carbon share and CO2 content of energy

Example reference(s): APERC (2007), WEC (2008)

This is a simple metric representing the non-carbon share of energy in total primary energy consumption or the CO2 content of primary energy supply as a way of tracking

103 http://www.bp.com/productlanding.do?categoryId=6929&contentId=7044622 104 http://www.cedigaz.org/news_statistics/statistics.htm 105 http://www.usgs.gov/

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the shift towards renewables and nuclear. Whilst this shift may effect energy security impacts by reducing dependence on imports and increasing diversity, the link between these and the measurement of non-carbon share is almost completely co-incidental. Given that these other factors are addressed more directly by the indicators described above, this indicator is not considered further.

B.1.6.5 Crisis Capability index

Example reference(s): ECN (2006b, 2007)

This indicator deals with the risk of sudden unforeseen short-term supply interruptions and the capability to manage them. The CCI combines the risk of a country to be confronted with sudden supply interruptions and its potential impact (the Risk Assessment, RA) and the capability of that country to manage and mitigate these impacts (the Mitigation Assessment, MA). Each country is invited to make its own RA and MA on the basis of checklists with some simple scoring values.

For the RA each part of the energy supply system is scored against three types of risk

• Technical and organisational factors

• Human factors (including human failures and deliberate actions such as terrorist attacks) and political factors and

• Natural events.

Each individual cause for a sudden supply interruption risk listed in the checklist should be assessed on the basis of the probability of such a risk and the impact of this risk on the energy system and on society. The risk can be valued with a figure indicating no (0), low (1), medium (2) or high risk . Not all energy system elements are of equal importance in a country’s energy supply. For each element the risk assessment score is multiplied, dependent on the category, by the relative share in primary energy sources (PES), final energy demand (FED) or total energy import. Adding the individual values together and multiplying the total by 100/48 results in the Risk Assessment sub-index (a value between 0 and 100).

Similarly, under the MA mitigation measures such as fuel reserves, rationing procedures, fuel switch capabilities and generation reserves are evaluated on being implemented and/or tested and scored from 0 to 3.

If the RA is higher than the MA value, the CCI gets a value of less than 100. The CC Index methodology has been discussed and partially tested with Dutch and Irish security of supply experts.

This indicator, is a novel, if largely subjective measure of the ability of a country to cope with short-term supply disruptions. However, its purpose is primarily to help structure countries’ thinking about their short-term vulnerabilities and mitigation options rather than putting significant emphasis on the final indicator score. Therefore this indicator is not considered further.

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B 2 Vulnerability indicators - overall system and hybrid approaches

The indicators below provide a broader measure of energy security across an extended part of the energy system and tend to draw on a number of the specific vulnerability indicators from the preceding sections in a hybrid approach.

B 2.1 Adequacy of energy supply to demand

B.2.1.1 Peak capacity margin


The peak capacity margin is the percentage by which total nameplate generation capacity on the system exceeds the peak electricity demand. It is generally applied to electricity, put could be used in an analogous manner to other fuels (e.g. maximum gas supply capacity versus peak demand - see section on Energy Margin B.2.1.3).

Peak demand can be defined in a number of ways, for example, based on the ACS (average cold spell) in the UK. It should not be necessarily be viewed as surplus capacity since some margin is required to cover the risk of generating plant unavailability (e.g. breakdown) or higher than predicted peak demand (e.g. due to severe weather).


• The indicator provides a proxy for the vulnerability of the energy system to physical/price unavailability impacts resulting from inadequate market structure (both insufficient investment in new capacity and load balancing). The closer demand is to peak capacity the greater the vulnerability106.

• For insufficient investment the indicator is targeted at Stage II. For load balancing the indicator effectively targets stage I as it provides an indication of the likelihood of supply not meeting demand (i.e. this increases as the peak capacity margin lowers). In both case the indicator also includes a simple proxy for the impact on the demand sector at Stage IV, via the value for peak demand.

• The indicator targets the domestic electricity generation (centralized and distributed) element of the energy supply chain for electricity.

i ) Suitability: the indicator is a relevant and targeted proxy, however, it does not consider any other factors that may affect the ability of the installed capacity to meet peak demand (and thus avoid energy security impacts) such as outages or the impact of intermittent generation. The indicator also does not consider

106 For example, lower peak capacity margins may mean more (and more volatile) price spikes as

increasingly marginal plant needs to be brought onto the system as well as physical unavailability in more

extreme cases.

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flexibility in the rest of the supply chain or any demand side participation / substitution possibilities – as only the relationship to total peak demand at Stage IV is applied. But, the relative importance of these elements needs to be considered in more detail as, for example, the short-term substitution possibilities for electricity are limited – and hence it may be less important if the indicator does not capture this.

ii ) Transparency: the formulation of the indicator itself is straightforward, but given that there are various possibilities for determining peak demand (against which the capacity margin is assessed) this can introduce a slight element of subjectivity – as it can be used to make the overall indicator more or less pessimistic.

iii ) Availability of data: peak capacity margin by itself has relatively straightforward data requirements – nameplate capacity and an estimate of peak demand.

iv ) Ability to forecast: peak capacity margin is produced directly as an output from the PRIMES model. However, a complication for projecting this indicator is to what extent any modelling approach explicitly assumes that sufficient new capacity is developed to meet peak demand – and hence this may reduce the indicator’s suitability as a proxy for the root cause type of insufficient investment.

B.2.1.2 Peak de-rated capacity margin

Example reference(s): BERR (2007), Redpoint et al (2008)

A variation of the above is the de-rated peak capacity margin, which scales back nameplate capacity by the expected availability of each plant at peak demand, taking into account probability of forced outages107 and expected output from intermittent renewables. The de-rated peak capacity margin is a better indicator of security of supply than the peak capacity margin, although it does not generally capture factors such as the possibility of correlated forced outages108 (for example, nuclear type failures requiring several stations to be taken offline) or fuel supply risks.

The de-rating of intermittent renewables should take into account the ‘capacity credit’ they provide to the system, which is a probabilistic assessment of the equivalent firm output from the source over a given period. However, this is non-linear and will change with the total level of renewables on the system, its geographic distribution, its correlation with other renewable sources and temporal correlation with peak demand (e.g. wind output is generally higher in winter when demand is also higher). Broadly speaking, there are gradually diminishing returns to capacity credit from increasing levels of intermittent generation, as shown in the figure below.

107 For example, if there is a 10% probability of forced outage for a plant, its de-rated capacity will be 90%

of its nameplate capacity. 108 Although in theory this is possible.

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Illustration B - 58 Modelled capacity credit function for diverse UK onshore wind resource with 27% capacity factor

Source: Redpoint et al (2008)


• As per peak capacity margin, however, the ability to de-rate the capacity could be used to account for extreme events – e.g. for renewables and the availability of intermittent generation.

• As per peak capacity margin, but for extreme events the indicator would be targeted at Stage II.

• It targets the same elements of the energy supply chain as peak capacity margin.

i ) Suitability: as per peak capacity margin, but the de-rating makes the indicator a better proxy for the vulnerability of the system to energy insecurity associated with the load balancing root cause. The indicator in its current form is a more limited proxy for extreme events unless it accounts for the geographical distribution and concentration of infrastructure.

ii ) Transparency: as per peak capacity margin, but modelling of capacity credit is complex and lowers the transparency of the final figure.

iii ) Availability of data: as per peak capacity margin, but de-rating peak capacity, particularly with regard to intermittent renewables introduces far greater uncertainty and data requirements

iv ) Ability to forecast: as per peak capacity margin, but the de-rating process is particularly complex when projecting into the future. For example, the assessment of intermittent renewables would be affected by the geographical location of new capacity across the EU.

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B.2.1.3 Energy margin

Example reference(s): BERR (2006), DECC (2008)

This indicator provides an assessment of the maximum supply of energy to a country, for a given source from different supply options (domestic and imported) and storage facilities relative to the level of expected demand. It therefore provides a more general view of the energy margin (or excess available supply) for primary energy sources in a similar manner to the peak capacity margin for electricity.

The indicator is itself straightforward, but can be based on detailed underlying projections for how the supply of energy may evolve in future under different scenarios. For example, how the completion of specific infrastructure projects such as new pipelines or LNG facilities, increases maximum import capacity.

Illustration B - 59 Example of UK gas supply versus demand under a given scenario

Average summer demand (exc IUK exports)

Average January demand

Severe winter diversified demand (Day 1) - firm load

only

0

1

2

3

4

5

6

7

8

9

06/07 07/08 08/09 09/10 10/11 11/12 12/13 13/14 14/15 15/16 16/17 17/18 18/19 19/20 20/21 21/22 21/23 23/24 24/25 25/26

TWh/

day

Average summer demand (excluding IUK exports) Average January demandSevere winter diversified demand (peak day) - firm load only UKCS Production lower end range (90% availability)European Imports Norwegian importsLNG imports Rough storage facilityOnshore storage/LNG Highest demand to date

Source: BERR (2006)

Note: UKCS = UK Continental Shelf


• The indicator provides a proxy for the vulnerability of the energy system to physical unavailability risks of imported and domestic energy sources covering all root causes.

• The indicator is targeted primarily at Stage II. It can also be argued to include a simple proxy for the flexibility of the rest of the supply chain at Stage III (via the different supply and storage options) and impact on the demand sector at Stage IV (via the estimates of seasonal peak demand).

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• The indicator is similar to ‘domestic production to consumption’ in B.1.3.3 and net import dependence in B.1.3.1, but covers all routes for energy supply as it targets international production / processing and domestic production as well as end-use. It also covers the import part of the energy supply chain (by specifying particular import routes) and (maximum) storage capacity.

i ) Suitability: as per net energy import dependence, but the combination of domestic and international production effectively covers a wider range of physical unavailability impacts (although still not price impacts) within a single indicator. By dividing supply into its constituent components the indicator provides an assessment of the upper bound for physical unavailability impacts at each element of the supply chain. But it does not provide any kind of proxy for the importance of the various root causes of energy insecurity within each element. The inclusion of different import routes and storage capability also provides a simplified view of the infrastructure flexibility in remainder the supply chain – at least in terms of illustrating the possible trade-offs in maximum domestic production versus storage and international gas supply options to meet peak demand. However, the simple use of total peak demand means that the indicator does not capture any potential demand-side participation or substitution possibilities with other fuels. As price impacts are not captured this makes the indicator less relevant for fuels such as oil, which operate in a competitive market.

ii ) Transparency: the formulation of the indicator itself is straightforward, but given that there are various possibilities for determining peak demand (against which the capacity margin is assessed) this can introduce a slight element of subjectivity – as it can be used to make the overall indicator more or less pessimistic.

iii ) Availability of data: peak demand and estimated domestic production are relatively straight forward data requirements and covered, for example, by existing models such as PRIMES (although not in terms of intra-year seasonal demand peaks). However, more specific data is required on the actual availability of supplies: for example, increasing LNG re-gas capacity is of no benefit if there is no corresponding increase in liquefaction capacity for gas destined for the EU.

iv ) Ability to forecast: the key complication for this indicator is estimating future maximum imports and storage capacity on a country-by-country basis, as this will depend specific assumptions about large-scale infrastructure development. The investment horizon in the energy sector rarely goes beyond 10 years. As a result, long term supply-demand balances tend to show supply shortfalls from the eleventh year onwards, though these shortfalls may not occur in.

B 2.2 Net import dependence and diversity in a market

Example reference(s): APERC (2007)

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This study applied a combined measure of net energy import dependence weighted by the diversity of each the particular fuel within the country’s total energy consumption using an adapted SWI.

• ∑∑=

i ii

i iii

ppppm

NEIDlnln

• Where:

- mi = the share in net imports of primary energy supply for fuel i,

- pi = the share of primary energy supply i in total primary energy supply.

A higher value implies a higher risk of energy insecurity, but as the diversity of fuels appears in both numerator and denominator, it is more of a measure of import dependence weighted by diversity than the other way around.


• As per net import dependence the indicator provides a proxy for the vulnerability of the energy system to physical unavailability risks (an upper bound in this case) of imported energy sources. The supply chain assessment in 2.6 shows possible energy security impacts associated with all root causes at the import stage.

• As per net import dependence the indicator is targeted primarily at Stage II. However, the proxy for impact on the demand sector at Stage IV is the diversity of the imported fuels within primary energy consumption as opposed to a simple share of imports in primary energy.

• As per net import dependence (see section B.1.3.1) the indicator targets the international production / processing, import and end-use elements of the energy supply chain for all energy sources.

i ) Suitability: as per net import dependence. It is difficult to argue that weighting the import dependence by the diversity in primary supply adds significantly to the relevance of the indicator; beyond the simple highlighting of the share of net imports in primary consumption. This is because the diversity measure suffers from the issues discussed in section B 1.5. In particular, it offers only a very limited proxy for the actual substitution possibilities of the different fuel sources.

ii ) Transparency: the weighting of import share by diversity in primary supply is in effect a subjective decision. The indicator also suffers from the same issue as the diversity indicator whereby the classification of the energy categories is ultimately arbitrary as there is no objective measure of disparity.

iii ) Availability of data: these are straightforward and as per net import dependence.

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iv ) Ability to forecast: as above.

B 2.3 Measuring diversity in both supply to a market and within the market

Example reference(s): NEFF (1997)

This study offers two interesting variations to the diversity quantification approaches described in section B.1.5.1. First, it considers both total fuel mix diversity and import source diversity. Secondly, it applies MVP theory to supplier portfolios based on production data.

• The study first uses an HHI to compare the diversity of the fuel mix in various (Pacific region) countries to the mean global value based on a categorisation of fuel mix – coal, oil, gas, nuclear and hydro. I.e. to examine the diversity of the fuel mix in a country whilst ignoring the difference between imports and exports.

• Within this an HHI is used to assess the diversity of supply sources for oil and uranium to the country by comparing the maximum theoretical diversity of supply sources based on total global exports to the countries’ diversity of supply based on actual import requirements.

• To account for the risk of different supply sources (both domestic and imported) MVP is then applied to supplier portfolios for a given fuel based on historic production trends (as opposed to more conventional price trends) using the example of the correlation of oil production data between individual OPEC countries during the 1990-1991 Gulf War. The study also considers systematic market risks (those which affect the entire market) following a probabilistic approach, by correlating total market variances with that of supplies from individual countries, as these are the risks that cannot be mitigated by diversification.


The coverage of the indicator effectively combines that of all three indicators of diversity in B 1.5 – HHI measures of supply within and to a market, and MVP.

• By using a proxy of diversity amongst suppliers at the import stage this effectively narrows the focus of the indicator to the impacts of resource concentration only.

• The indicator is targeted at Stage I and IV of the causal links (the latter via the measure of diversity in the market). The separate MVP analysis also targets stage III by looking at a portfolio of supply options.

• It covers the international production / processing, imports, and end-use elements of the energy supply chain.

i ) Suitability: this effectively combines the advantages and disadvantages of all three indicators. The measure of diversity of suppliers is a reasonable proxy for

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the price impacts of resource concentration, but only for liberalised markets where this effect dominates (in the study only oil and uranium are assessed). The assessment of diversity of supply sources to diversity within the country lacks a measure of disparity between the different energy categories and so does not provide a sufficiently robust indication of substitution possibilities. This lack of disparity is not relevant for the MVP analysis of different supply options (domestic and imports) for the country, but suffers from the same limitations that analysis of past data may not serve as a useful guide for future energy security impacts.

ii ) Transparency: the measures of diversity within and to a market suffer from the arbitrary classification of energy categories, and for MVP the indicator ultimately provides only a view of the trade-off between risk and production (as opposed to cost in most MVP analysis) rather than a single measure of energy insecurity. Combined, these serve to limit the transparency of the overall measure of energy security.

iii ) Availability of data: the measure of diversity in a country has relatively simple data requirements with two exceptions: the measure of diversity of suppliers suffers from the uncertainty of future supply from these countries, and for the MVP the assessment of co-variance between the categories adds additional data requirements and uncertainty.

iv ) Ability to forecast: the key issue related to the application of this indicator is the uncertainty in the assessment of the future supply of energy from particular countries at global level. For the MVP component it implicitly assumes that historic data is a sufficient guide for future trends.

B 2.4 Long-term energy security indicator

Example reference(s): Jansen et al (2004)

This study defines a macro indicator for long-run energy security. The analysis rests extensively on the work of Stirling (1999) discussed in section B 1.5 and combines 4 main elements based around a core SWI:

• Diversification of energy sources in energy supply of the country.

• Diversification of net imports to the country with respect to imported energy sources (i.e. resource concentration of suppliers) also based on a SWI.

• Long-term political stability in import regions based on the UNDP’s HDI

• The resource base in regions of origin, including the home region itself - based on RPRs (see B.1.6.3).

The resulting indicator for steps i) to iii) is of the form:

• ∑−= i iii ppcI ln3

where:

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- )/1(1 max,mi

miii SSmc −−= and ∑−= j ijijj

mi mmhS ln

• With:

- pi = share of primary energy source i in total primary energy supply

- i = 1,2,3,..,M: the number of primary energy sources

- mi = share of net import in primary energy supply of source i

- Sim = SWI of import flows of resource i;

- mij = share of imports from region j in total import of source i

- j = 1,2,3,…N: Index for (foreign) region of origin. N regions are distinguished.

- Sim, max = maximum value of Shannon index of import flows of resource i.

(= -ln{1/N})

- hj = the extent of political stability in region j, ranging from 0 (extremely unstable) to 1(extremely stable).

Additionally, resource depletion is taken into account through the inclusion of a depletion index, covering both the different exporting regions as well as the home region. This index rests on the assumption that markets will respond to information on RPRs if these drop below a value of 50.

The indicator accounting for resource depletion maintains the same standard formulation:

• I4 = − ci4 pi ln pii∑

But where

- ci4 = {1− (1− rik )(1−mi )} *{1−mi (1− Si

m** / Sim**,max ) and

- Sim** = − rijhjmij lnmijj∑

• With

- rij = depletion index for resource i in import region j, subject to;

- rij = Min{[(R / P)ij

50]a;1} (a ≥1) 109

- rik = depletion index for the home region k, for which the indicators are determined.

- R/Pij = proven reserve to production ratio for resource i in region of origin j

109 Jansen et al (2004) propose “the convenient –but admittedly arbitrary- value of a=2”

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The relationship to the ES framework is complex as the indicator aggregates the elements of the diversity of supply in and to a market (see section B 1.5) the latter combined with an indicator of political stability (see section B.1.6.2), along with a measure of net import dependence B.1.3.1. The measure of RPR as discussed in section 2.2.1 falls outside of the framework.

• The measure of diversity in energy supply (coupled with political stability) to the country provides a proxy for the assessment of price impacts of energy security from the resource concentration root cause. The net import dependence part of the indicator provides a proxy for the vulnerability of the energy system to physical unavailability risks (an upper bound) of imported energy sources – across all root causes.

• The indicator is targeted at Stage I (likelihood of resource concentration risks), Stage II (vulnerability to import risks), Stage IV (diversity of supply in a country).

• The aggregate indicator covers the international production / processing, imports and end-use elements of the energy supply chain – for all energy sources.

i ) Suitability: the suitability of the indicator is somewhat difficult to assess given the combination of a range of different factors. One issue is the application of diversity of suppliers to net imports across all fuels. Diversity of suppliers is a useful proxy for the price impacts of resource concentration, whereas net import dependence focuses on physical unavailability. For fuels where the price impact is more dominant (in a competitive global market such as for oil) price effects would likely carry through to oil in both imports and the domestic market, whereas the indicator focuses on diversity in the share of imports only. Secondly, the application of an RPR alongside resource concentration has been argued to be of limited relevance in section 2.2.1, and so falls outside of our ES framework, but still impacts directly on the final results of the indicator. The HDI is a less suitable proxy of political stability for energy security in the short to medium term, but as argued in Jansen et al (2004) it is a potentially better gauge of a country’s overall stability in the long term. Finally, the indicator‘s assessment of demand side impacts via the diversity of primary supply within the country only provides a limited proxy of possible substitution options.

ii ) Transparency: While the approach is instructive for its attempt to combine a number of energy security concerns into an aggregated indicator, it inevitably raises the question of the weighting of the importance of the different factors against each other (fuel diversity both in and to the market, net import dependence, political stability and depletion). This is always to some extent arbitrary, but may require expert judgement on a country-by-country basis to make the indicator more relevant. In addition, there are a number of explicit

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assumptions made within the indicator such as the categorisation of regions and the 50-year threshold in the RPR. The overall impact of these issues is to slightly reduce the transparency of the results from the indicator.

iii ) Availability of data: data availability is reasonably good and as per the relevant individual components of the indicator.

iv ) Ability to forecast: the most uncertain projected elements of the indicator are: the assessment of a country’s reserves, the projection of global future supply from each country and the long-term estimate of the HDI.

B 2.5 IEA Energy Security Index

Example reference(s): IEA (2004, 2007)

Work has also been undertaken by the IEA (2004, 2007) to provide a quantitative framework to assess the interaction between energy security and climate policy.

B.2.5.1 Original 2004 study

The original 2004 study developed the following two indicators.

• Geopolitical Energy Security (GES) proxy measure. GES is a composite measure for a particular country reflecting several aspects of the economic risks associated with energy markets and attempts to account for supply concerns (for fossil fuels) related to:

- The risks related to physical supply shortfalls occurring between production and consumption due to infrastructural failure.

- The risks of price distortions occurring due to strategically motivated control of supply linked to the concentration of energy resources.

The indicator is based on a combination of:

- A measure of the concentration of market power of suppliers based on a modified HHI - weighted by the share of fuel type for the relevant consumer country.

- The political stability of the relevant countries based on ICRG rating.

• Power system reliability proxy measure. This specific measure is focused on long-term generating capacity requirements – in relation to ability of the power system to reliably produce electricity to a required statistical level of availability in the presence of intermittent generation. It is defined as the back-up capacity required (based on CCGT technology) to fill the gap between the average rate of electricity production of intermittent plant (each type of energy generation is assigned a capacity credit specific to that country110) and the rate of generation that can be relied on during periods of peak load on an

110 Or load factor – defined as how much each additional unit of capacity added to the system can be relied

upon so that the probability of peak load shedding does not increase. For technologies such as wind this will

depend on the annual available wind resource in each country.

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annual basis. Hence, this is similar to the indicator of de-rated peak capacity margin in B.2.1.2.

The 2004 paper also included measures of Market liquidity (see section B.1.6.1 and RPRs B.1.6.3). However, it was decided to exclude these from the 2007 report, primarily due to the subjectivity of weighting the importance of these different factors against each other and because it was not clear the extent to which these constitute legitimate ES concerns – as discussed in the relevant sections above.

B.2.5.2 Development of indicators under the 2007 study

The subsequent 2007 study built upon the GES measure to focus on measuring the evolution of resource concentration as the primary energy security concern. Two separate indicators were defined to measure the different price and physical availability components of energy security more explicitly.

Energy Security Index (Price)

The first indicator focuses on energy security price concerns. It is concerned with market concentration in each fossil fuel markets in terms of their net export capacities. The assessment of market concentration is done using an HHI. A measure of political stability is also included as an option, giving extra weight to politically instable countries. It is primarily concerned with fuels traded in liberalised international markets.

• ∑ −=f

ffpolprice TPESCESMCESI ]/*[

where:

- Cf / TPES = the share of each fuel (Cf) in Total Primary Energy Supply for the given country

- ESMCpol-f =∑i

ifi Sr )*( 2 = a measure of the Energy Security Market

Concentration of each fuel based on the political risk/stability rating see (section 1.2.8.2)111 of each supplier country (ri) and the square of the share of each supplier in the market for each fuel (Sif

2) – where the size of the market is defined by net export capacity112.

111 N.B. as indicated in the report this factor is optional. Based on two of the World Bank’s Worldwide

governance indicators the score is weighted from 1 for the most politically stable countries to 3 for the least.

These scores remain fixed over time, motivated by the inability to predict how governance will change in the

future. In order not avoid bias to events in a specific year, the average of the values from 2002 to 2005 is

taken. 112 Rather than the level of production - as the external supply of energy to third parties may be constrained

by factors such as the physical capacity of pipelines or contractual arrangements.

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In the case of gas, however, the situation is a more complicated as much depends on whether gas prices are set competitively or indexed to oil. When natural gas prices are set competitively, this is the same as in the case of oil and coal. In the case where gas is indexed to oil, however, it is effectively susceptible to the energy security price risk in the oil market. In this case, the gas share of the fuel mix should also be exposed to the oil market price risk. It should be noted that in some cases in Europe, the gas market is partly based on oil-indexed contracts and partly spot-based. In such cases, the share that is spot-based is exposed to gas market ESMCpol while the share that is oil-indexed is exposed to oil market ESMCpol. In the equation above, therefore, for coal, Cf /TPES is simply the share of coal in the energy mix. In the case of oil and gas, much depends on how the gas market is structured.

Overall a country importing a large quantity of a fuel that is concentrated in the hands of small number of politically unstable supplier countries would receive a higher ESIprice

than the situation where smaller quantities were imported from a wider range of more politically stable suppliers.

Energy Security Index (Volume)

The second indicator deals with physical unavailability, which is applied to markets where prices are regulated or indexed to oil. The latter situation still applies to parts of the European market. This indicator is defined as the share of a county’s total energy demand met by pipe-based gas imports purchased through oil–indexed contracts. The rationale behind this is that pipelines generally do not allow consumers to switch to other suppliers in case of a supply disruption, as opposed to LNG based trade. Secondly, the oil indexing of gas prices prevents market forces from mitigating supply disruptions.

• ]/)([ TPESgasPipeimpESI indexedoilvolume −= where:

- Pipeimp(gas) is the net import of gas via a pipeline purchased through oil-indexed contracts – as a share of Total Primary Energy Supply.

- Hence ESIvolume ranges from 0 the most secure to 1 the least secure. 0 would occur in the case of: 100% gas-based pricing and no-oil-indexed imports; no-pipeline based imports (i.e. 100% LNG) or self-sufficiency in gas (no imports).

The combined application of the two indicators across fossil fuels is outlined in the figure below.

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Illustration B - 60 Overview of IEA approach to quantifying the energy security implications of resource concentration


• It is focused primarily on energy security concerns related to the root cause of resource concentration. The risks of physical unavailability and price impacts of this in regulated and competitive markets, respectively, are clearly separated into the two indicators by dividing out the share of gas imported in pipelines via oil-indexed contracts for ESIvolume.

• The ESIprice indicator targets Stage I and IV, whilst the ESIvolume indicator targets Stage II (the vulnerability of the energy system due to gas pipeline import dependency), Stage III (via a simple measure of the flexibility in the rest of the supply chain – e.g. by allowing the expansion of LNG to reduce the security risk) and Stage IV.

• The aggregate indicator covers the international production / processing, import elements of the energy supply chain and also includes a simple measure of end-use demand (via the share of the imported fuel in total primary energy).

i ) Suitability: the indicator is useful proxy as it aims to target a single root cause of energy insecurity. The splitting of fuels to account for separate physical unavailability and price impacts is naturally a simplification as many of the energy sources will still share elements of both, but follows a clear chain of logic to reach this conclusion. There is only limited accounting for flexibility in

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the remainder of the supply chain113 and only a simple measure of the importance of each energy source (share of fuel in total primary energy) and no accounting for demand side participation or substitution possibilities.

ii ) Transparency: the main goal of having two separate indicators is simplicity and transparency. Arbitrary weighting is needed via the inclusion of a rating of political stability under ESIprice, however, it is emphasised that this is optional. As per diversity of supply to a market discussed in B.1.5.1, whilst the formulation of the indicator itself is relatively straightforward, the classification of the categories within it is somewhat arbitrary as there is no objective measure of disparity – and needs to reflect a view about whether market power in this case rests with companies, countries or cartels.

iii ) Availability of data: the share in primary energy for the fuels is a relatively straightforward data requirement (e.g. available from PRIMES), but the measure of diversity of suppliers suffers from the uncertainty around future supply from these countries and would need to be based on an additional source – e.g. the IEA’s WEO data. The other key data requirement is the share of gas demand in each country that is met by oil-indexed contracts – for the 5 case study countries the shares are currently base on simple illustrative estimates114.

iv ) Ability to forecast: key uncertainties in projections revolve around the diversity of supplier countries, the projection of political stability (if used), and how the share of gas met by oil-indexed contracts will change in future.

B 2.6 Supply / Demand index

Example reference(s): ECN (2006, 2006b, 2007)

The Supply / Demand Index (SDI) is part of a framework of 2 indicators, which also includes the Crisis Capability Index (CCI) (see section B.1.6.5). These were developed to assess EU Member States’ security of supply status with a view to using them to base future energy policy decisions. The CCI estimates the exposure to short-term supply disruptions and a member states’ ability to manage these whilst the SDI aims to review and assess energy security of supply in the medium and longer term. This has been used to assess the impact of climate policies on energy security (ECN, 2006).

It is calculated by means of a model covering final energy demand, energy conversion and transport and primary energy sources. It uses four types of inputs, one objective and three of a more subjective nature.

113 Whilst ESIvolume accounts for the use of LNG in gas supply it effectively models a single pipeline into a

country, and hence does not capture the energy security benefits of multiple pipeline import. 114 100% oil-indexed for all of the case-studies, with the exception of the UK where the split is 50/50.

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• Shares of fuel types in supply and sectors in demand (i.e. for demand: industrial use, residential use, tertiary use and transport use; for supply: oil, gas, coal, nuclear, RES and other).

• Subjective values characterising the, adequacy, capacity, reliability, etc, of each element in the model.

• Subjective weights that balance the different elements of the model on the basis of perceived vulnerability (e.g. supply side options to reduce energy security risks versus demand side options).

• Scoring rules to determine the different values for each of the individual contributing elements (e.g. political reliability of supply from different geographical regions)

The structure is outlined in the figure below. The elements coloured in red use objective shares from energy balances to set their values (e.g. based on Eurostat/IEA for historic data and PRIMES for projections), whereas the blue objects depend on expert judgments in their attribution of weights.

Illustration B - 61 SDI model structure

Source: ECN (2007)

The values of each of the individual elements are determined by scoring rules that are simple functions of shares, supply origins, efficiencies, reserve factors, network capacity, refinery and storage capacity to name a few. In case data is not available, a default value of 100 is used. The functions are deliberately kept simple in favour of transparency, which translates to mostly linear and step-functions where arguably more complex dynamics play a role. The higher the final index value the better the

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perceived security of energy supply. Sensitivity analysis is then taken on the more uncertain elements.

The main difference with the other indicators considered here is that the SDI attempts to grasp the whole energy spectrum, including conversion, transport and Demand. This stems from the adaptation of a consumer perspective on supply: whether a disruption occurs at the source, along the transportation route or during the conversion process is irrelevant to the end user. Demand (measured as the energy intensity relative to a benchmark) is also included, since lowering of the energy use lowers the overall impact of supply disruptions.


• Given the fundamentally different approach of the indicator it is difficult to link it to a specific set of root causes of energy insecurity – as the SDI essentially applies a (largely arbitrary) set of energy security concerns or vulnerabilities across the entire energy supply chain. However, some linking with respect to physical unavailability can be identified, including: load balancing in the electricity system (via the measure of ‘adequacy’ in this part of the model) and resource concentration in regulated markets via the adequacy of imports.

• Targeting of the causal links is at Stage I for load balancing (i.e. adequacy of electricity supply provides a proxy of the likelihood of a problem), whilst for resource concentration at Stage II (as it measure vulnerability of the system due to import dependence). Two other interesting points of note from the model in relation to the ES framework are the attempt to capture the flexibility of the remainder of the supply chain more explicitly (Stage III) via the measure of adequacy. For example, for gas this considers the potential for storage and LNG (peak shaving) facilities in the scoring rule. Secondly, the impact of the demand side (Stage IV) on the indicator score is considered more explicitly by comparing the energy intensity in various end-use sectors against benchmark values and scoring an improvement in the indicator relative to this.

• The SDI covers all elements of the energy supply chain from imports to end-use.

i ) Suitability: the first issue with respect to the relevance of the indicator is that it attempts to assess a wide, and fairly arbitrary range of potential energy security issues without adequately explaining their direct role in the assessment of energy security. For example, efficiency of conversion is applied as sub-branch of the energy chain for electricity. However, it is not linked directly to the impact on reduction in demand for primary fuels (and any energy security impacts associated with them), but is instead considered independently and given a score out of 100 in relation to a benchmark of 50% conversion efficiency. Similarly, reliability for gas and electricity networks and

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electricity generation is also included, but again it is unclear to what extent this reflects a specific energy security concern as opposed to a fairly generic operational concern. A score out of 100 is applied as default in most cases, where the generation is considered ‘reliable’ without any further metric or proxy for assessing reliability.

Secondly, where more direct energy security concerns are incorporated their relative importance is in effect arbitrary, given the various scoring rules and weightings across different parts of the supply chain. A key example is the issue of demand. Whilst it is rightly recognised that an indicator of energy security needs to account for the importance of energy demand within the economy, the impact of this on the indicator’s result is separated in the structure, with a value of 30% for demand side ‘energy security vulnerabilities’ versus a 70% weighting for those on the supply side.

The overall effect is to make it difficult identify what the most relevant and important energy security issues being addressed by the indicator are.

ii ) Transparency: The SDI is the most ambitious and extensive indicator discussed here, but as a result suffers from a lack of transparency. Whilst relative changes are easily identifiable it is less clear what this means overall for energy security and the aggregation means that opposing trends are masked without detailed sensitivity analysis. In addition, whilst the subjective weights and scores are made public, the extensive use of these factors throughout the SDI reduces the final objectivity of the results.

iii ) Availability of data: the main data requirements are relatively straightforward and based on energy balance data. However, a number of additional data sources are required, for example with respect to estimating benchmark values of energy intensity for different end-use sectors and gas / refinery infrastructure capacity, share of CHP in electricity generation. The remaining data requirements are then based on expert judgements to set the weights and scoring rules on a country-by-country basis.

iv ) Ability to forecast: the underlying energy balance data is relatively straightforward to project and widely available from existing energy models. Expert judgements are more uncertain when estimated over the long-term.

B 3 Outcome-based indicators

B 3.1 Expected energy unserved

Example reference(s): BERR (2007b), DECC (2008), Redpoint et al (2008)

The expected energy unserved indicator is the probability weighted average level of energy demand for a specific source/fuel, which would not be met (due to demand exceeding supply) over a given period.

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This measure captures in a single figure the probability of involuntary interruptions and the likely size of those interruptions, and as such, is a proxy for the costs and consequences of energy insecurity to society. It has been applied primarily to electricity and gas in the above references, but could in theory be used for any energy source. The assessment depends directly upon a set of underlying assumptions about possible supply and demand options and the probability distributions applied to them (see BERR 2007b for a more detailed overview). It can also draw upon other indicators as part of the assessment, for example, with respect to electricity a smaller peak capacity margin (ceteris paribus) will lead to a higher expected energy unserved115.

Unlike many of the other indicators in this section it is a measure of the outcome of energy insecurity (with respect to physical unavailability), rather than an input or risk which contributes to the potential for energy security impacts.


• The indicator focuses on a probabilistic assessment of the actual physical unavailability of energy and so covers all relevant root causes – i.e. excluding resource concentration in competitive markets where price impacts are likely to dominate.

• As it is an outcome based measure (rather than a vulnerability measure) it targets the outcome of a specific stage and all the preceding stages up to this. Examples based on detailed dynamic modelling for BERR have been targeted at stage IV – i.e. providing the expected physical unavailability of energy due to a cause of energy insecurity (at Stage I and II) and accounting for the flexibility of the rest of the supply chain at Stage III and demand side response at Stage IV. Alternatively, a slightly simpler assessment of actual physical unavailability can take place at an earlier Stage in the causal mechanisms – e.g. an estimate of energy unserved from imports at Stage II, without considering the impacts on Stage III and IV on the magnitude of the final energy security impact.

• The coverage of the elements of the energy supply chain depend on the scope of the modelling work underpinning the indicator but in currently applied cases tends to include international production / processing, imports, domestic production, storage and end-use.

i ) Suitability: the indicator is a measure of the likely outcome of physical unavailability impacts of energy security, and so is more relevant than those process-based indicators, which only provide a proxy for the potential risk/magnitude of an energy security impact, should it occur. It is therefore a key, all encompassing, measure of the likely final impact on consumers. One potential issue is that the focus on physical unavailability means it is less

115 For the UK expected energy unserved in electricity is close to zero in situations where the derated peak

capacity margin exceeds 10% (as has been the case historically), but increases significantly where de-rated

peak capacity margins fall below this level (Redpoint, 2008)

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relevant measure for fuels and root causes where price based energy security impacts dominate.

ii ) Transparency: whilst the indicator itself provides a simple high-level measure, it hides the detailed modelling and numerous assumptions that underpin it. In particular, the selection and application of probability distributions to both supply-side options and energy demand.

iii ) Availability of data: the modelling of demand/supply options can vary in detail depending on the exercise, and hence could draw directly on energy system data available from Eurostat/PRIMES, etc. However, it is the selection and application of probability distributions which would in reality vary significantly depending on the situation (e.g. by country, by supply route, by energy source, etc) and for which adequate data is not readily available.

iv ) Ability to forecast: as per availability of data the key uncertainty is projecting how the probability distributions are likely to vary over time.

B 3.2 A Security of supply function for the MERGE model

Example reference(s): Bollen (2008)

In this study a ‘willingness to pay function’ (WTP) for implementation in the MERGE116 model was designed. As energy insecurity leads to welfare impacts the function is designed to represent what percentage of GDP a country is willing to spend in order to lower the risk. It builds on the assumption that higher import quotes lower welfare, as imported energy displays higher risk of supply disruptions. Additionally, the share of a certain energy carrier in the energy mix is taken into account, as higher shares will lead to higher welfare losses in case of a supply disruption. Lastly, the overall energy intensity of the economy is taken into account, as high energy intensities will lead to high welfare losses, because impacts are higher in case of disruptions.

The function implemented is:

• γβαrtrtrtrt EciAIMP ,,,, ∗∗∗=

• Where

- IMP = the WTP to avoid a lack in security of supply [% of GDP],

- i = import ratio

- c = the share of fuel under scrutiny in TPES

- E = Energy intensity

- A = a region specific calibration constant, relating to the SOS at t=0

- α, β, γ = 1.1, 1.2, 1.3, respectively.

116 http://www.stanford.edu/group/MERGE/

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The function is implemented for oil and natural gas, as these are considered the main sources subject to potential SOS risks. The exponents α, β and γ are convex, based on the assumption that the energy security risk increases faster as the dependency increases. For instance, an increase from say 50 to 60% carries larger SOS risks than an increase from 30 to 40%. The function is calibrated based on the investments nations have made in order to improve their energy security. Specifically, France’s investment programme in nuclear energy starting in the 1970s serves as reference point for estimating WTP to avoid energy security risks.

The MERGE model assumes competitive markets, where price changes are a consequence of supply and demand. Hence it only considers energy security welfare impacts from physical unavailability of energy and cannot technically assess the impact of uncompetitive or highly volatile prices.

In practice, the indicator of energy security in this case combines a measure of net import dependence on an individual fuel basis, with a measure (energy intensity) of the importance of the fuel in energy demand. The function acts as a constraint in the model as it attempts to maximize welfare under a given scenario. For example, with the constraint in place, developing higher cost domestic resources may be welfare maximizing given the WTP to avoid the welfare impacts (assumed to occur as a result of energy insecurity) from a greater dependence on imported fuel.

The key issue with this approach is therefore in the calibration of WTP function and a calibration made on unique historic cases may be highly questionable. In addition, as discussed in previous sections the use of import dependence as the fundamental measure of energy security may not adequately capture all potential risks – as this focuses on physical unavailability risks, as opposed to price risks which are likely to be of greater importance in the case of oil.

This is an example of the integration of the assessment of energy security directly within a dynamic model and so is not considered further, as this project is focused on the application of results from existing modelling studies to assess the impact on energy security.

B 3.3 Cost failure of the electricity system


The concept of ‘cost failure’ for the electricity system was defined by Electricity de France (EDF) to help determine the value117 of maintaining peak electricity capacity margin as part of service obligations (to minimise the risk of energy unserved). This is done by estimating the macroeconomic cost of non-supplied marginal kWh due to production failure. This parabolic function takes the form of:

• γ = af2 + bf + c

117 E.g. as part of the cost of a public service obligation and to determine the cost that can ultimately be

passed through to consumers.

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• Where

- γ is the failure cost (non-supplied marginal kWh)

- f = the “depth of failure”, defined as the ratio of non-supplied electricity to electricity demand

- While a, b, and c are parameters to be estimated.

An optimal probability of failure can be defined (for instance about 4 to 5% in France). In some tense situations, when the high price observed on the spot market is representative of this marginal failure cost, operators are prepared to charge high prices (1,000 - 10,000 euros/MWh) for the marginal kWh to prevent failure. In 1995, EDF estimated this marginal kWh at approximately 10,000 euros at current prices (WEC, 2008).

As per the function for the MERGE model this indicator aims to directly link the energy security impact (physical unavailability of supply) to the actual welfare effect. The calibration is again crucial, and will likely vary significantly from country to country. Again, due to the focus in this project on the application of existing modelling results this approach is not considered further.

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Appendix C Overview of Impact Assessments for new climate change policies

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Table C - 25 Approach to analysis of impact of climate policy on energy system in EC Impact Assessments

Policy Approach to Impact Assessment (IA) of policy

ESD / 20% GHG Target and

A range of modelling tools were used to examine the combined GHG/RES package: PRIMES, GAINS, GEM E3, PACE and POLES. Scenarios examined are the:

- Baseline

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RES 20% Target

- EC Proposal without RES trading

- EC Proposal with CDM without RES trading

- EC Proposal with RES trading

- EC Proposal with CDM with RES trading

- Cost efficient reference scenario; - Cost efficiency scenario with CDM - High oil and gas prices baseline - Cost efficiency scenario with high prices In addition, a pure carbon and a pure RES scenario were also quantified. These scenarios refer to the GHG and RES targets alone. Note: the impact of proposed changes to the EUETS in Phase III (21% reduction in the cap by 2020 compared to 2005 emissions for the ETS sectors) is included in all scenarios excluding the baseline. The results from the IA show that emission reduction under the pure-GHG case induces more RES leading to a RES share of 15.9% in 2020, up from 12.7% in the Baseline scenario. The deployment of the RES under the pure-RES case leads to GHG emission reduction of 9.3% in 2020 from 1990, down from 1.5% in the Baseline scenario. In both cases, energy security increases because imports of fossil fuels are reduced. Regarding energy security, the IA focuses on resource concentration issues outside Europe, i.e. the impacts of these policies on oil and gas imports. Reducing greenhouse gas emissions and increasing renewable energy, reduces energy consumption and induces shifts to domestically produced energy (i.e. renewable energy). This makes the EU less dependent on imports of oil and gas and less dependent on geopolitical factors that affect the supply of these fuels and may affect the prices of these fuels. See Table 2 for the impact on oil and gas imports of six policy variants to meet the 2020 GHG reduction and RES target. It is noted that effects will vary from member state to member state depending on their reliance on oil and gas imports. - Table 2 Reduction in oil and gas imports in 2020 (in billion euro)

Reference documents: - General http://ec.europa.eu/environment/climat/climate_action.htm - Baseline http://ec.europa.eu/dgs/energy_transport/figures/trends_2030_update_2007/energy_transport_trends_2030_update_2007_en.pdf - IA http://ec.europa.eu/energy/climate_actions/doc/2008_res_ia_en.pdf &

http://ec.europa.eu/environment/climat/pdf/climat_action/climate_package_ia_annex.pdf - PRIMES scenario modelling results:

http://ec.europa.eu/environment/climat/pdf/climat_action/analysis.pdf & http://ec.europa.eu/environment/climat/pdf/climat_action/analysis_appendix.pdf

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CCS The modelling tool used was PRIMES. Four scenarios were considered:

- Option 0: No enabling policy for CCS at EU level, including no inclusion of CCS in the EU ETS

- Option 1: Enable CCS under the EU ETS

- Option 2: In addition to enabling under the ETS, impose an obligation to apply CCS from 2020 onwards and assess the impact on the potential positive externalities not captured by the carbon market. Four principal sub-options were considered:

a) Making CCS mandatory for new coal-fired power from 2020 onwards b) Making CCS mandatory for new coal- and gas-fired power from 2020 onwards c) Making CCS mandatory for new coal-fired power from 2020 onwards, together with retrofit of existing plants (built between 2015 and 2020) from

2020 d) Making CCS mandatory for new coal- and gas-fired power from 2020 onwards, together with retrofit of existing plants (built between 2015 and

2020) from 2020.

- Option 3: In addition to enabling under the ETS, apply a subsidy so as to internalise the positive externalities not captured by the market. Energy security is quantitatively assessed (albeit very indirectly) via the analysis of the impacts of the various options on primary fossil fuel consumption (solids, oil and gas). The results are briefly illustrated in the table below (of most interest is the comparison relative to Option 0):

Change in fuel consumption in the power sector over option 1 (% in 2030):

Policy Option 0 2a 2b 2c 2d 3 Solids -38% -6% 8% -6% 12% 1%

Oil -19% 4% -3% 4% 0% -3% Gas 7% 15% -6% 25% 4% 0%

Reference documents: - General http://ec.europa.eu/environment/climat/ccs/eccp1_en.htm - IA http://ec.europa.eu/environment/climat/ccs/pdf/ccs_ia_jan2008.pdf - PRIMES results http://ec.europa.eu/environment/climat/ccs/pdf/primes.pdf

EUETS PIII

Modelling done with the E3ME model (results not published). The three E3ME scenarios modeled were broadly

- Continuation of Phase II cap and allocation (baseline)

- Within a declining 21% EUETS cap, 3 scenarios: 1. 100% auctioning 2. 100% continued free allocation 3. Hybrid where auctioning is partly combined with benchmarking (100% in power sector and declining share over time in other sectors).

The impacts of energy security were briefly and qualitatively mentioned in the Impact Assessment in the paragraphs illustrating the results of the analysis on CCS and offsets. The report, however, does give a more quantitative discussion of the impacts on fossil fuel consumption in the chapter on power

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sector. This shows the impacts on solids, gas and oil consumption derived from the scenarios Reference documents: - General http://ec.europa.eu/environment/climat/emission/ets_post2012_en.htm - http://ec.europa.eu/environment/climat/emission/pdf/com_2008_16_ia_en.pdf

Aviation in EUETS

Models used include AERO, TREMOVE, and PRIMES Modelling of the energy system to 2020 suggests that in the long term a 46% GHG emissions reduction (attributable to the inclusion of aviation in the ETS) is possible compared to BAU (mainly due to efficiency improvements). This has a direct impact on fossil fuel consumption which will decrease, will some qualitative discussion about the improvement to energy security. However, the modelling shows that even assuming a full pass through of EU ETS compliance costs onto air transport users, this will only have a modest effect on transport demand and thus traffic, compared to business as usual growth. References documents: - General http://ec.europa.eu/environment/climat/aviation_en.htm - IA http://ec.europa.eu/environment/climat/pdf/aviation/sec_2006_1684_en.pdf

CO2 LDVs Modelling based on TREMOVE has been used in the IA to assess economic and energy impacts. The impact of the policy on energy security has not been analysed in the IA. Reference documents: - General http://ec.europa.eu/environment/air/transport/co2/co2_home.htm - http://ec.europa.eu/environment/air/transport/co2/pdf/sec_2007_1723.pdf

Fuel Quality

No specific energy system modelling has been carried out as part of the IA, and only a minor qualitative analysis of security of supply. Reference documents: - General http://ec.europa.eu/environment/air/transport/fuel.htm - IA http://ec.europa.eu/environment/air/transport/pdf/ia_sec_2007_55_en.pdf


The policy options examined in the IA differed slightly to those originally proposed in the Directive and are outlined below.

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Modelling based on TREMOVE, IPTS transport technologies model (Poles) and TRANSTOOLS. Three scenarios: - Option A: No intervention - Option B: Uniform excise rate on commercial diesel across Member States - Option C: Enhanced approximation of excise duties applicable to commercial diesel (3 further subcategories)

No specific assessment of impacts on energy security in the IA Reference documents: - IA http://ec.europa.eu/taxation_customs/resources/documents/taxation/excise_duties/energy_products/SEC(2007)170_en.pdf

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Appendix D Case-studies

D 1 Renewable electricity support schemes (UK and Spain)

D 1.1 Background

This case study examines the effects of renewable energy legislation in Spain and the UK on the country’s energy security using the indicator framework.

Spain

Spain adopted feed-in-tariffs (FiTs) to encourage installation of renewable energy on both small and large scales. The FiT was introduced to help Spain meet its national target of providing 12% of total primary energy demand from renewable sources by 2010. Spain’s law was first introduced in 1997 for a limited number of RE sources. It was expanded in late 1998 and updated again in 2004. The first update, by Royal Decree 2818, allowed the sale of full production onto the grid at wholesale electricity price plus a premium, and generally made the scheme more accessible.

We consider the period 1990 to 2007, looking for the effects of the expanded FiT policy starting in 2000. The analysis of forecasted data (until 2010) was not possible due to the incompatibility of the datasets between historical data and forecasts.

The initial FiT policy guaranteed a variable premium payment for all electricity fed into the grid from renewable sources, however there was no long-term guarantee until the 2004 modification. The price premium was dependent upon the renewable technology. For some technologies, such as hydro-electric generation, size of plant was limited. The 2004 amendment to the Royal Decree 2818, improved the predictability of electricity prices, premiums and incentives as well as setting more detailed rules for each of the renewable technologies.

UK

In the UK, to stimulate uptake of renewable energy, the government introduced the Renewables Obligation (RO) scheme in 2002, this replaced the previous Non-Fossil Fuel Obligation (NFFO). The RO requires electricity suppliers to source a percentage of their total energy supplied from accredited renewable energy suppliers. This required percentage rises every year, beginning with just 3% required in 2002. By 2010, it will be 10.4% in England and Wales. Scotland has a higher target of 18% by 2010, while Northern Ireland’s is much lower at just 4% by 2010.

Electricity suppliers receive one Renewable Obligation Certificate (ROC) for each MWh of renewable energy purchased. Price is determined by the market at quarterly auctions. Where there is insufficient renewable energy supply at reasonable prices, companies can choose to pay “buy-out” fees. However, these fees are then shared amongst energy suppliers who did purchase ROCs, so there is a strong incentive to purchase ROCs even if the price is higher than the government-set buy-out price.

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The legislation is currently being updated to take into account cost variations in new, developing renewable technologies and provide greater long-term stability to renewable energy producers.

The legislation came into force in April 2002 and had an immediate effect on the renewables market. Changes would therefore be expected in 2002 and beyond. Given the stepped nature of the obligations, it is expected that the system would promote continuous growth in renewable energy generation until at least 2015.

D 1.2 Counterfactual (without policy) scenario

For both countries there was no satisfactory without policy scenario available and therefore an alternative ‘counterfactual’ scenario was created118 to estimate what would have happened in the absence of the RES policies.

In this counterfactual scenario, the installed capacity of renewables by type is assumed to increase based on the historic growth rates seen between 1990 and 1997 for Spain, and between 1990 and 2000 for the UK. Hence, the growth rates moving forward from these points are lower than the actual outturn (i.e. the historic data) once the new renewables policies had been implemented.

The overall level of electricity demand in both the outturn and counterfactual scenario is assumed to be the same. The gap in electricity capacity under the counterfactual is then assumed to be filled by gas plants. It is difficult to determine the alternative investment patterns under the counterfactual scenario, but it is assumed that for cost-effectiveness reasons generators would want to make maximum use of their existing plant before building new capacity. By looking at the gap in capacity and gas plant utilisation rates (both with RES policy and the additional output required without it) it has been assumed that in both cases it is not necessary to construct new gas plant.

Estimates of the additional primary gas requirements for these plant have been calculated based on the average gas plant efficiency rates in each year. All the additional primary gas needed is assumed to be imported from the same mix of suppliers and the same countries in the years analysed.

118 For example, projections developed by BERR for the UK in 2000 (Energy Paper 68, available at;

http://www.berr.gov.uk/files/file11257.pdf) assumed a target for renewables in 2010 and therefore already

had an enhanced level of renewable policy already included to help meet this.

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D 1.3 Results

The legislation in both countries have an effect on the capacity for electricity production, electricity generation and imports relative to gas, as it was assumed that this would be the fuel of choice if no renewables had been introduced. The effects on energy security have therefore been investigated only with respect to this fuel.

D.1.3.1 Extreme weather

There are a number of ways in which extreme weather can impact energy security. For the purposes of this report, it was determined that the greatest potential impact of extreme weather would be a period of extreme cold weather resulting in sustained period of peak demand. Both the Renewable Obligation and Feed in Tariffs are aimed at the production of electricity which in turns is susceptible to all impacts on the supply chain. For this reason an extreme weather event can affect all the stages of the causal mechanisms of energy insecurity.

Here we discuss the effects on energy security on stage by stage basis of the indicator in an attempt to quantify the level of impacts on a scenario with renewables and one without.

Stage II (there is no Stage I component for this indicator) represents the daily peak supply shortfall (DPSS) of gas during a period of increased demand. This is shown in the graphs below, where the DPSS in the UK and Spain during a typical winter peak and during an extremely cold winter peak is reported.

Under the “with renewable policy” scenario (“actual” in dashed lines), the shortfall is less severe than in the baseline (“counterfactual” in plain lines). In both countries the shortfall tends to increase due to the increase in peak demand.

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Illustration D - 62 SRA (primary fuels) indicator – Gas – Stage II – Typical winter


0

20

40

60

80

100

120

1990. 1995. 2000. 2005.

Year

ktoe

Counterfactual - SpainCounterfactual - United KingdomActual - SpainActual - United Kingdom

Illustration D - 63 SRA (primary fuels) indicator – Gas – Stage II – Extreme winter


0

50

100

150

200

250

1990. 1995. 2000. 2005.

Year

ktoe


►▬▬▬

In

cre

asi

ng

vu

lnera

bil

ity ▬▬►

The next figure represents stage III of the indicator which indicates the number of days covered by gas supply considering the storage capacity and the daily withdrawal rate. While the UK number of days remains more or less constant, it drops

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considerably in Spain. In both cases the number of days is higher under the actual scenario than under the counterfactual. The storage capacity is considered fixed between 1990 and 2007, but in Spain this is expected to increase in the future. This should increase the number of days covered by the supply of gas and decrease the vulnerability of the system.

Illustration D - 64 SRA (primary fuels) indicator – Gas – Stage III – Typical winter


0

200

400

600

800

1000

1200

1990. 1995. 2000. 2005.

Year

days


Stage IV of the indicator illustrates the complement of share in total primary energy consumption. As gas has increased its share in both Spain and the UK the vulnerability of the energy system has also increased in the recent years. However, in both countries the level of vulnerability decreases under the actual scenario where more renewables are in place. In the UK this is marginal as in the current energy system the uptake of renewables has been limited especially compared to Spain.

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Illustration D - 65 SRA (primary fuels) indicator – Gas – Stage IV


50%

55%

60%

65%

70%

75%

80%

85%

90%

95%

100%

1990. 1995. 2000. 2005.

Year

%


◄▬▬▬

In

cre

asi

ng

vu

lnera

bil

ity ▬▬◄

Overall the indicator of short run availability of gas shows that the scenario with renewables is less vulnerable than the one without renewables in both Spain and the UK.

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D.1.3.2 Other extreme events

The illustration below shows the ability of the electricity market to react to both extreme weather and an additional extreme event, lower values indicate increasing vulnerability. In the UK, the greatest potential threat would be a large-scale accident or terrorist activity resulting in the loss of power production from, the nation’s largest coal-fired power plant. In Spain, it would be the loss of the nuclear plant in Almaraz. In both countries this would put a strain on the gas system which would be used as a replacement fuel to meet the demand. A higher penetration of renewables helps reducing the impacts from these events. Due to the lack of data it was possible to analyse the impacts of extreme events only from 2000 onwards.

Illustration D - 66 SRA – De-rated Electricity Peak Capacity Margin (DEPCM) indicator – Stage II – Typical winter and loss of largest plant


-5%

0%

5%

10%

15%

20%

25%

30%

2000. 2001. 2002. 2003. 2004. 2005. 2006. 2007.

Year

%


In more of a “worst case” scenario, shown in the next figure, the loss of the largest electricity supplier happens during the coldest period of the year when demand for electricity reaches its peak. Not surprisingly also in this case the indicator is impacted more negatively under the counterfactual scenario (BAU) where fewer renewables are in place (as the underlying assumption is that existing gas plant are utilized more, rather than new capacity being constructed).

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Illustration D - 67 SRA – De-rated Electricity Peak Capacity Margin (DEPCM) indicator – Stage II – Extreme winter and loss of largest plant


-15%

-10%

-5%

0%

5%

10%

15%

2000. 2001. 2002. 2003. 2004. 2005. 2006. 2007.

Year

%


However, at Stage II the indicator does yet not account for the knock on effects of a shortage in gas during an extreme winter event limiting the availability of primary fuel for gas electricity generation. The impact on the DEPCM at Stage III in this situation (extreme winter and loss of the largest plant) is shown below.

317


Illustration D - 68 SRA – De-rated Electricity Peak Capacity Margin (DEPCM) indicator – Stage III– Extreme winter and loss of largest plant

Further de-rating due to short-run availability of gas only for power generation

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

2000. 2001. 2002. 2003. 2004. 2005. 2006. 2007.

Year

%


It is worth noticing that where the DEPCM indicator is negative it indicates that there is not sufficient capacity to meet demand should such an event occur, however, this does not account for the ability of interconnectors to meet peak demand or demand-side response. In addition the knock-on effects of gas availability on the DEPCM are subject to high-level assumptions outlined in section 6.2.1. In particular, that gas operates as peaking plant for 50% of each day, lowering this would reduce the lost capacity due to a primary gas shortfall

Finally, some gas plant has a small amount of distillate back-up capacity on-site to offset any disruption to primary gas supply. In the UK this amounts to ~5GW of capacity (see Appendix B - B.1.1.2), which effectively increase the DEPCM

D.1.3.3 Insufficient investment

Data is not available to calculate the capital intensity or cumulative required new capacity in €M indicators. However, in the UK it is assumed that the Renewable Obligation would have required additional investment in the grid. The UK’s strongest renewable potential is to be found in remote areas – the western coast of Scotland for off-shore wind and the southern Irish Sea for tidal power. Additional grid investment would be required to transport this power to more populous areas and the grid itself would need to be strengthened to deal with variable power supplies. However, the UK already has sufficient gas power plants to act as back-up power for added renewables use. As such, the additional required investment would not be as large as it would for some countries.

318


The Renewables Obligation did not set aside funds for grid improvement. In the UK, National Grid is responsible for high-voltage electricity transmission and balancing, it is assumed that any necessary grid improvements will be made and the costs passed on to energy suppliers, and eventually consumers.

Capital intensity would also have increased as a result of the legislation. Many renewable energy production plants are highly capital intensive, with low operating costs. With targeted investment in wind turbines, the capital intensity of the energy system would be expected to increase more than the business-as-usual case.

Similarly in Spain, the capital intensity is expected to increase more than in the BAU scenario, also, as the FiT legislation does not address grid investment directly, the responsibility to get the renewable electricity to consumers is in the hands of the private sector.

Calculations have, however, been made for the average load factor and cumulative new capacity in MW indicators.

The graph below illustrates the average load factor in electricity production from all plants. In both countries the scenario without RES policies shows a higher load factor due to an increase in use of gas plants to produce the electricity displaced by the renewables.

Illustration D - 69 Average Load Factor – Stage I – all electricity plants

Load factor

35%

40%

45%

50%

55%

60%

1990. 1995. 2000. 2005.

Year

%


Similarly the following graph shows how in absence of renewables the average load factor of gas plants only, is increasing in both the UK and Spain, albeit less in the UK due to the lower uptake of renewables compared to Spain. This indirectly links to one

319


of the key underlying assumptions of the counterfactual scenario – that there is sufficient underutilization of gas capacity for this to be expanded and still meet peak demand (in the absence of the new renewables capacity)

Illustration D - 70 Average Load Factor – Stage I – gas plant only

Load factor -gas plants

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2000 2001 2002 2003 2004 2005 2006 2007

Counterfactual - Spain

Counterfactual - United Kingdom

Actual - Spain

Actual - United Kingdom

The figure below shows the cumulative installed new capacity due to the uptake of renewables in the actual scenario. In the counterfactual there is no additional capacity needed as the existing plants are sufficient to cover for the demand. In Spain the curve is much steeper than in the UK showing that the uptake of renewables is much more aggressive than in the UK as the result of differences in the RES scheme implementation (both the overall level of support and the difference in investor risk).

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Illustration D - 71 Cumulative required new capacity (MW) – Stage I

Cumulative installed new capacity

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

MW

Counterfactual - Spain

Counterfactual - United Kingdom

Actual - Spain

Actual - United Kingdom

D.1.3.4 Load balancing failure

Contrarily to other indicators, an increasing dependence upon renewable energy sources increases the vulnerability of the system to a load balancing failure. The De-rated Electricity Peak Capacity Margin under a typical (winter) peak situation has already been shown in section A.1.1.1 – albeit with the loss of the largest plant. The situation without this loss is shown below, increasing the DEPCM.

321


Illustration D - 72 De-rated Electricity Peak Capacity Margin (DEPCM) – Stage I


0%

5%

10%

15%

20%

25%

30%

35%

2000. 2001. 2002. 2003. 2004. 2005. 2006. 2007.

Year

%


Renewable energy production tends to be highly variable with power being produced in bursts or only at certain times of the day. In addition, they are also subject to unexpected shutdowns as a result of unfavourable weather patterns.

This is demonstrated in the graph below where the negative impacts on flexibility margin is greater in Spain where more renewables are in place compared to the UK where the impacts are relatively smaller. The system becomes more vulnerable in time as the uptake of renewables increases in the BAU scenario.

322


Illustration D - 73 Flexibility margin – Stage I – maximum likely loss of wind generation and spinning reserve ramp rates

Flexibility margin

0%

50%

100%

150%

200%

250%

300%

2000. 2001. 2002. 2003. 2004. 2005. 2006. 2007.

Year

%


The effects are smoothened somewhat, when the capacity margin is scaled by the complement share of electricity in total final energy consumption at Stage IV (to provide a measure of the vulnerability at the demand side) as illustrated below. This is because although the share of electricity is increasing in both cases, the rate of increase is lower than the rate of decline of the flexibility margin.

323


Illustration D - 74 Flexibility margin – Overall indicator – maximum likely loss of wind generation and spinning reserve ramp rates

Overall indicator

0%

50%

100%

150%

200%

250%

2000. 2001. 2002. 2003. 2004. 2005. 2006. 2007.

Year

(Sta

ge I

V s

cale

d) %


D.1.3.5 Resource concentration (effective price mechanism)

In this study in was assumed that renewables would be totally replaced by gas in both Spain and the UK, and therefore any measure of the vulnerability of the energy system will be focused on the gas markets in those countries. Not surprisingly, the risk of resource concentration is lower in the scenario with renewables as primary energy demand for gas increases.

324


Illustration D - 75 Resource Concentration Price Indicator – Overall indicator - Gas – no political risk ratings

Overall indicator (gas exposed to gas price risk + gas purchased under oil-indexed contracts exposed to oil price risk)

0

500

1000

1500

2000

2500

3000

3500

1990. 1995. 2000. 2005.

Year

(Sta

ge I

V s

cale

d) E

SM

C


D.1.3.6 Resource concentration (no effective price mechanism)

Similarly to above, also in case of no effective price mechanisms, a move to renewables will decrease the need for imported gas, reducing vulnerability, as shown in the illustration below.

It should be noted that the indicator reflects the annual share of gas not purchased on spot or spot-derived markets (i.e. oil indexed), not covered by gas storage – in the case of the UK the drop to zero means that sufficient gas storage is available to cover this quantity of gas.

325


Illustration D - 76 Resource Concentration Physical Availability Indicator – Overall indicator – no political risk ratings

Overall indicator

0

100

200

300

400

500

600

1990.

1991.

1992.

1993.

1994.

1995.

1996.

1997.

1998.

1999.

2000.

2001.

2002.

2003.

2004.

2005.

2006.

2007.

Year

(Sta

ge I

V s

cale

d) E

SIC


D 1.4 Summary

In both countries the uptake of renewables helps improving the energy security in all cases except in case of load balancing failures (reflected in the DEPCM and flexibility margin indicators). However, this is linked to the underlying assumption within the counterfactual that in the absence of the renewable energy schemes and as electricity demand increases, generators would first increase utilization of their available gas plant before building new capacity.

Over the period of interest (1990 – 2007) it is assumed that there is sufficient spare utilization for the plants to still meet peak electricity demand even as this increases. But, there will come a point where new capacity is required and hence the indicators related to the primary consumption of gas (resource concentration, short-run availability) will only change marginally (due to the modest change in gas consumption) whereas the electricity related indicators (DEPCM and flexibility margin) will show a decreased vulnerability as new capacity comes onto the system.

The impact on energy security of the Spanish energy system could be more severe than what has been discussed here due to the integrated nature of the Spanish system with the Portuguese one. Portugal is a net importer of gas and has little storage capacity, at the same time gas does not have a major share in primary fuel consumption. A more detailed analysis of the Iberian energy system would refine the results a little better and give a more complete picture of the effects of the renewable legislation in that region.

326


D 2 Expansion of Combined Heat and Power (CHP) (Netherlands)

D 2.1 Background

After 1987 climate change became a major topic on the political agenda. This stimulated the Dutch government to not only focus its energy policy on energy savings and costs of energy but also on the reduction of CO2. The government signed voluntary agreements with almost all industrial sectors aiming to reduce industrial energy demand by 20%. At the same time, also the electricity sector came to an agreement with the Dutch government to reduce CO2 emissions by means of an environmental action plan. Industries and electricity companies found each other in joint ventures that deployed new CHP capacity. As such, CHP formed the main link between the voluntary agreements and the environmental action plan. Due to the interests of the electricity companies, CHP plants no longer needed to be designed to meet the electricity demand. This led to increased electrical capacities of the newly built plants and an explosion of new CHP capacity in the 1990s. In this case study we will be focused on the period between 1990 and 2000.

327


D 2.2 Counterfactual (without policy) scenario

Illustration D - 77 trend in the development large scale (>2 MWe) CHP capacity

Illustration D - 78 trend in the development of small scale (<2MWe) CHP capacity

Legend for figures 1 and 2 Dutch English WKK vermogen CHP capacity Gebouwd 1990-2000 Built between 1990 and 2000 Uit bedrijf 1990-2000 Closed between 1990 and 2000 STEG Natural gas combined cycle (NGCC) Stoomturbine Steam turbine tuinbouw Greenhouse horticulture

328


diensten Tertiary sector Industrie + overig Industry + other

CO2 emissions in the Netherlands totalled 222 MtCO2eq in 2000. The additional CO2 reduction attributable to CHP between 1990 and 2000 (i.e. excluding CHP that was already operational in 1990) is between0.7 and 7.3 MtCO2eq (depending on the reference119)120.

The figure below shows the baseline scenario (based on historical data) and the counterfactual scenario representing the absence of growth in decentralized CHP capacity. The growth of CHP has led to:

• Reduced load hours for coal and gas fired power plants

• Accelerated shut down of a number of gas and coal fired power plants

• Cancellation of the construction of a new coal-fired plant

119 The CO2 reduction of 0.7 Mton refers to a reference situation based on a 55% NGCC, whereas the 7.3

Mton is based on the average power production efficiency in 1990 (40.5%). 120 List of data sources:

ECN (2002) Effect energiebesparingsbeleid CO2-emissies 1990-2000.

SEP (1990) Electricity Plan 1991-2000.

SEP (1992) Electricity Plan 1993-2002

SEP (1992) Notes to Electricity Plan 1993-2002





www.energie.nl (statistical data)

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Illustration D - 79 Coal and natural gas consumption trend in the actual (with CHP) and counterfactual scenarios.

Primary fuel consumption with and without CHP policy

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

ktoe

/y

Counterfactual - gas

Actual - gas

Counterfactual - coal

Actual - coal

Counterfactual - total

Actual - total

For the construction of the counterfactual the following assumptions have been made:

• In absence of CHP, the heat would have been produced by a 90% natural gas fired boiler

• In absence of CHP, 50% of the electricity would have been produced by a mixture of existing and new coal fired power plants (40% efficiency on average); the other 50% would have been produced by old natural gas fired plants (40% efficiency). Under this scenario no additional capacity is needed to cover demand needs, so no new gas plants are assumed.

• In absence of CHP, the net installed capacities of primary fuels (oil, gas and solid fuels) as well as hydro (lakes) is are assumed to be increasing linearly from 1990 to 2000 based on the growth rate between 2000 and 2005121.

D 2.3 Results

As CHP increases the efficiency of energy production the main effects will be reflected on the primary fuels. Here the main fuels affected are assumed to be gas and coal.

121 This is due to the fact that no disaggregated data was available at primary fuel level and therefore

PRIMES data was used. PRIMES gives net capacities from 2000 onwards only.

330


The discussion will therefore be focused on those two fuels and their relative indicators.


The CHP legislation promotes a more efficient way to produce electricity and heat which in turn are vulnerable to any disruption to the supply. An extreme weather event can affect all the stages of the causal mechanisms of energy insecurity. All stages if the indicator will be investigated to illustrate the effects of the CHP policy.

Stage II (there is no Stage I component for this indicator) represents the daily peak supply shortfall (DPSS) of gas during a period of increased demand. This is shown in the graphs below, where the DPSS in the Netherlands during a typical winter peak and during an extremely cold winter peak is reported.

As illustrated in the graph, there is no clear advantage in implementing the CHP policy (scenario “actual” in dashed lines) compared to a case without CHP (scenario “counterfactual” in plain lines). It appears that at least initially the scenario with CHP is less vulnerable to DPSS, only to become more vulnerable after 1997 (compared to a scenario without CHP). This indicator depends on net capacity data. As explained in the previous section, these were derived for primary fuels, including gas, and therefore the change may be due to the linear correlation rather than a real worsening of the shortfall.

Illustration D - 80 SRA (primary fuels) indicator – Gas – Stage II – Typical winter


0

10

20

30

40

50

60

70

1990. 1991. 1992. 1993. 1994. 1995. 1996. 1997. 1998. 1999. 2000.

Year

ktoe

Counterfactual - NetherlandsActual - Netherlands

Root cause Extreme events Indicator Short Run Availability (primary fuels) Energy type Gas Stage II Y-axis Value

Data

Scenario

Member State

As expected in case of an unusually cold winter the shortfall is more severe, with values doubling over the period. The trend is however similar to that of a typical winter. The results are exacerbated when, in the worst case scenario, also the main

331


gas supplier is taken out, but the trend remains unchanged with the scenario with CHP performing better than the counterfactual scenario until 1997, and than worse (see Illustration D - 82).

Illustration D - 81 SRA (primary fuels) indicator – Gas – Stage II – Extreme winter


100

105

110

115

120

125

130

135

1990. 1991. 1992. 1993. 1994. 1995. 1996. 1997. 1998. 1999. 2000.

Year

ktoe



Data

Scenario

Member State

Illustration D - 82 SRA (primary fuels) indicator – Gas – Stage II – Extreme winter without main gas supplier


130

135

140

145

150

155

160

1990. 1991. 1992. 1993. 1994. 1995. 1996. 1997. 1998. 1999. 2000.

Year

ktoe



Data

Scenario

Member State

In stage III, the number of days covered by gas supply is higher in the scenario with CHP making this scenario less vulnerable to extreme event. Again the switch in

332


behaviour in 1997 can be attributed to the way the net capacities of gas were derived in the counterfactual scenario.

Illustration D - 83 SRA (primary fuels) indicator – Gas – Stage III – Typical winter


75

80

85

90

95

100

1990. 1991. 1992. 1993. 1994. 1995. 1996. 1997. 1998. 1999. 2000.

Year

days


Root cause Extreme events Indicator Short Run Availability (primary fuels) Energy type Gas Stage III Y-axis Value

Data

Scenario

Member State

Stage IV of the indicator illustrates (below) the complement of share in total primary energy consumption. In the scenario without CHP the share of gas in total energy production is lower than in the scenario with CHP. This is because coal plays a greater role in the scenario without CHP.

333


Illustration D - 84 SRA (primary fuels) indicator – Gas – Stage IV


49%

50%

51%

52%

53%

54%

55%

56%

57%

1990. 1991. 1992. 1993. 1994. 1995. 1996. 1997. 1998. 1999. 2000.

Year

%


Root cause Extreme events Indicator Short Run Availability (primary fuels) Energy type Gas Stage IV Y-axis Value

Data

Scenario

Member State

Overall the indicator of short run availability of gas shows that the scenario with CHP is less vulnerable to SRA in extreme weather until 1997, then it is more vulnerable.

Illustration D - 85 SRA (primary fuels) indicator – Gas – All stages

Overall indicator

0%

1000%

2000%

3000%

4000%

5000%

6000%

1990. 1991. 1992. 1993. 1994. 1995. 1996. 1997. 1998. 1999. 2000.

Year

(Sta

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cale

d) d

ays


Root cause Extreme events Indicator Short Run Availability (primary fuels) Energy type Gas Stage All Y-axis Value

Data

Scenario

Member State

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The illustration below shows the ability of the electricity market to react to both extreme weather and an additional extreme event, lower values indicate increasing vulnerability. In the Netherlands, the greatest potential threat would be a large-scale accident or terrorist activity resulting in the loss of power production from the nation’s largest refinery plant in Pernis. The scenario with CHP shows less vulnerability to such an event.

Illustration D - 86 SRA – De-rated Electricity Peak Capacity Margin (DEPCM) indicator – Stage II – Typical winter and loss of largest plant


-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

1990. 1991. 1992. 1993. 1994. 1995. 1996. 1997. 1998. 1999. 2000.

Year

%


Root cause Extreme events Indicator Short-Run Availability - De-rated Electricity PeaEnergy type Electricity Stage II Y-axis Value

Data

Scenario

Member State

To illustrate the worst possible case, in the next figure, we show the loss of the largest electricity supplier is in addition to an increased demand for electricity due to extreme weather conditions. Not surprisingly also in this case the indicator is impacted more negatively under the counterfactual scenario (BAU) with no CHP.

335


Illustration D - 87 SRA – De-rated Electricity Peak Capacity Margin (DEPCM) indicator – Stage II – Extreme winter and loss of largest plant


-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

1990. 1991. 1992. 1993. 1994. 1995. 1996. 1997. 1998. 1999. 2000.

Year

%


Root cause Extreme events Indicator Short-Run Availability - De-rated Electricity PeaEnergy type Electricity Stage II Y-axis Value

Data

Scenario

Member State

However, at Stage II the indicator does yet not account for the knock on effects of a shortage in gas during an extreme winter event limiting the availability of primary fuel for gas electricity generation. Since Stage III represents the DEPCM for gas and stage II responds to changes to gas in this case study, the two indicators are the same. For this reason Stage III is not shown.

336


Illustration D - 88 SRA – De-rated Electricity Peak Capacity Margin (DEPCM) indicator – All Stages

Overall indicator

-40%

-30%

-20%

-10%

0%

10%

20%

30%

1990. 1991. 1992. 1993. 1994. 1995. 1996. 1997. 1998. 1999. 2000.

Year

(Sta

ge I

V s

cale

d) %


Root cause Extreme events Indicator Short-Run Availability - De-rated Electricity PeaEnergy type Electricity Stage All Y-axis Value

Data

Scenario

Member State

As mentioned in previous case studies where the DEPCM indicator is negative it indicates that there is not sufficient capacity to meet demand. However, the inputs from interconnecting pipelines to meet peak demand or demand-side responses are not accounted for. In addition the knock-on effects of gas availability on the DEPCM are subject to high-level assumptions outlined in section 6.2.1. In particular, that gas operates as peaking plant for 50% of each day, lowering this would reduce the lost capacity due to a primary gas shortfall.


There is insufficient data to calculate the capital intensity or cumulative required new capacity in €M indicators. The capital intensity of a CHP plant is similar to that of a conventional plant, with the advantage of lower operating costs due to higher efficiencies. In addition CHP is often combined with district heating thereby introducing another revenue stream which makes it more advantageous than conventional power plants.

The load factor for all electricity plants is not being reported as it is not informative. In both the actual and counterfactual scenarios the final energy consumption remains unchanged, and therefore the load factor is the same in both cases. However the load factor for gas plant shows that the scenario with CHP is less vulnerable than the other, this is because less fuel input is needed to produce the same amount of electricity in CHP plants.

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Illustration D - 89 Average Load Factor – Stage I – gas plant only

Load factor -gas plants

0%

10%

20%

30%

40%

50%

60%

70%

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90%

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Counterfactual - Netherlands

Actual - Netherlands


The De-rated Electricity Peak Capacity Margin under a typical (winter) peak situation has already been shown in section A.1.1.1 –with the loss of the largest plant. Here we report the DEPCM without this loss, which increases it as shown below. The scenario with CHP policy not surprisingly shows less vulnerability to that without CHP.

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Illustration D - 90 De-rated Electricity Peak Capacity Margin (DEPCM) – Stage I


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Unlike the RES case study, here the penetration of CHP in the energy system helps decreasing the vulnerability. This is shown below, where the flexibility margin is higher, and therefore more resilient to disruptions, in the scenario with CHP.

Illustration D - 91 Flexibility margin – Stage I

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By scaling the capacity margin by the complement share of electricity in total final energy consumption at Stage IV (to provide a measure of the vulnerability at the demand side) the effects are buffered (see figure below).

Illustration D - 92 Flexibility margin – Overall indicator – maximum likely loss of wind generation and spinning reserve ramp rates

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The CHP policy has an effect on the coal and gas market primarily has it tends to make the use of these fuels more efficient. When analysing the coal market, the resource concentration indicator shows that the vulnerability increases in a scenario without CHP, thus reaffirming that a better and efficient use of resources contributes to energy security. However, when looking at the gas market it appears that the scenario with CHP is the most vulnerable of the two. This is counterintuitive but can be explained by the fact that, in the scenario without CHP, the share in primary energy of gas under oil indexed contracts is smaller and therefore this scenario is less vulnerable to changes in concentration in the oil market.

This also explains why, overall, the resource concentration for all primary fuels indicates a higher vulnerability in the scenario with CHP policy in place. In any case the difference between the two scenarios is still marginal, therefore suggesting that the uptake of CHP does not significantly increase the risk of energy insecurity.

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Illustration D - 93 Resource Concentration Price Indicator – Overall indicator - Coal – no political risk ratings

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Illustration D - 94 Resource Concentration Price Indicator – Overall indicator - Gas – no political risk ratings

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Illustration D - 95 Resource Concentration Price Indicator – Overall indicator – Primary fuels – no political risk ratings

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The indicator reflects the annual share of gas not purchased on spot or spot-derived markets (i.e. oil indexed), not covered by gas storage. In the Netherlands, the indicator is zero in all years showing that there is sufficient gas storage to cover the quantity purchased. The indicator is therefore not shown.

D 2.4 Summary

Overall the analysis of the vulnerability of the energy system in the Netherlands when the CHP policy is in place shows that the scenario with policy helps reducing the energy insecurity in some years. The variability in the response to the indicators reflects how on one hand new CHP is dominantly gas-fired, on the other, new CHP also replaces less efficient existing gas-fired power plants.

The CHP policy reduces potential energy security risks for coal as coal is relatively less vulnerable to resource concentration, in addition the construction of new coal fired power plants were cancelled in the 1990s.

In any case the effect of the uptake of CHP on the risk of load balancing failure is in general reduced compared to the scenario without CHP. This is also reflected in the flexibility margin which is less vulnerable with CHP. As mentioned the heavy reliance of CHP plants on gas makes the scenario with CHP at time more vulnerable to resource concentration and extreme events (especially if the latter are accompanied by the failure of the largest electricity plant). Nonetheless, despite the scenario with CHP shows more vulnerability to some of the root causes, this impacts are only marginally higher than in the scenario without CHP.

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D 3 Large Combustion Plant Directive (LCPD) implementation (UK and Poland)

D 3.1 Background

This case study focuses on the impact of the Large Combustion Plant Directive (LCPD)122 on the power generation sector, where the legislation primarily affects coal and oil plant. The Directive aims to reduce acidification, ground level ozone and particles throughout Europe by controlling emissions of sulphur dioxide, nitrogen oxides and dust from large combustion plants in power stations, petroleum refineries, steelworks and other industrial processes.

The impact of the LCPD in the electricity sector is particularly interesting given the range of alternative generation technologies, and hence we focus on electricity in this case study. New power stations must meet the Emission Limit Values (ELVs) given in the LCPD. Existing plant, however, faced a range of choices when the legislation was introduced. They were required to determine whether they would “opt in” or “opt out”. Plant that opted in could either comply directly with the ELVs (requiring the installation of Flue Gas Desulphurisation (FGD) equipment for coal stations) or participate in a national plan under which aggregate emissions across those plant within the plan were limited. Whilst not a direct climate change policy, the LCPD has indirect linkages, such as the effect on generating efficiency or the reduced incentives for coal generation, which will affect both air pollutant and GHG emissions.

Plant that have opted out are restricted to 20,000 hours of operation between 1 January 2008 and 31 December 2015, after which they must close. The choice of whether to opt-out was an economic one, based on the cost of installing equipment to comply with the LCPD compared to expected future revenues. The impact varied widely across EU countries. In some countries, significant levels of plant decided to opt out. This was particularly true of the UK and Poland, with approximately 10GW of opted out plant in each market (about 12% and 30% of the total capacity in each market respectively). This level of plant retirement in a relatively short period could have an impact on security of supply, particularly in terms of the system’s vulnerability to short-term supply shortfalls and extreme events in the electricity sector. We have therefore chosen these markets as the focus of the case study.

D 3.2 With and without policy scenarios

We evaluate the LCPD’s impact on energy security for the UK and Poland through the construction of two scenarios, one with the LCPD in place and a second in which the LCPD no longer applies. To create this ”no LCPD” case, we consider the opted-out generation plant to determine which plant might otherwise have remained open. (Opted-in plants are operational under both scenarios.) We then take a view on the 122 Directive 2001/80/EC http://ec.europa.eu/environment/air/pollutants/stationary/lcp.htm

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retirement choices of these plants, as well as the changed investment signals and decisions for other generation, from 2009 to 2020. These modelled decisions are consistent with economic choices by existing plant owners, new plant investors over the period, and (for Polish lignite) closures associated with mine depletion.

The modelling for the electricity sector has been conducted independently of the PRIMES Baseline run (for which only partial data and results were available), and uses some different assumptions to ensure internal consistency. For metrics that incorporate overall energy system results (such as resource concentration), we translate the changes in retirements and investment into a series of incremental adjustments to the corresponding PRIMES baseline data, for example, in the consumption of primary fuels. In this step, we have made the underlying assumption that the PRIMES baseline includes the impact of the LCPD. As part of this case study is derived from the PRIMES data and part is derived from separate assumptions, it is difficult to ensure complete consistency. However, the directional and relative nature of the results is indicative of the trends one would expect, and thus serve to illustrate the use of the proposed metrics.

Table D - 26 Key assumed differences in electricity capacity under LCPD vs no-LCPD scenarios (GW)

UK Poland

LCPD No-LCPD LCPD No-LCPD Type

2010 2015 2020 2010 2015 2020 2010 2015 2020 2010 2015 2020

Solids fired 28.7 23.8 20.8 28.7 29.4 32.8 28.8 29.7 33.7 28.8 31.1 35.3

Gas fired 38.3 42.7 46.2 38.3 42.7 37.2 1.7 1.8 1.8 1.7 1.8 1.8

D 3.3 Results

Modelling for the counterfactual reveals that many opted-out Polish coal and lignite plant would still retire in the absence of the LCPD. For some plant this is due to their age (many are over 40 years old) and, for others, the closure of the corresponding lignite mines as they become depleted. Given these closures, we determined that neither the generation nor investment landscape of Poland would fundamentally change without the LCPD.

In the United Kingdom, the modelling suggests that, without the LCPD, opted-out coal and oil plant would find it economic to remain open, leading to a corresponding reduction in CCGT investment that is currently expected. This results in a significantly improved capacity margin in the short-term, although investors eventually respond by reducing build, particularly in new CCGT. The materiality of the impact on other metrics, such as resource concentration, is smaller, as we show below.

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D.3.3.1 Extreme weather

The figure below shows the SR Availability metrics for primary gas and oil123. The ability of either Poland or the United Kingdom to cope in an extreme cold weather event with regard to oil and gas would not be greatly impacted in the absence of the LCPD. In Poland, as gas and oil consumption would be unchanged, there is no impact on SR Availability. In the United Kingdom, the gas metric improves somewhat without the LCPD due to the decreased reliance on gas124. There is a marginal impact on the oil metric due to a small reduction in oil use under the LCPD.

Illustration D - 96 SR Availability - Stage III - Primary gas and oil

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The impact on the electricity sector as shown below, however, is quite the opposite125. The vulnerability of the electricity sector to an extreme weather event is reduced in the “no LCPD” scenario, with the derated capacity margin in the United Kingdom showing a marked improvement in 2015126. This improvement, however, is expected to decline slightly as new investment slows in response to coal plant remaining on-line127.

123 These metrics are shown for “Extreme” winter impact on peak oil and gas respectively, and without loss

of largest supply chain component. 124 This metric compares the Daily Peak Supply Shortfall (DPSS) for gas against both total storage capacity

and the max daily withdrawal rate. If the DPSS is greater than possible withdrawal, the SRA days coverage

is set to zero. 125 This metric is shown for “Extreme” impact on electricity (including a reduction in gas-fired generation

availability due to lack of gas supply) and without the loss of the largest plant. 126 Electricity peak demand numbers used in the electricity market modelling, and used as an input to the

extreme events, load balancing and flexibility margin metrics, differ from the PRIMES baseline data. The

focus in this Case Study should therefore be on the relative changes in these metrics, not the absolute

levels. 127 It should be noted that the greater interconnectivity of Poland with neighbouring countries will play an

important role in load balancing that is not accounted for in this metric.

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Illustration D - 97 DEPCM – Extreme winter peak - Electricity

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With the differences between scenarios with and without the LCPD being of greater significance for the electricity sector, this section considers vulnerability to the loss of the largest electricity plant in Poland (Belchatow at just under 4400 MW and over ten percent of current capacity) and the United Kingdom (Drax at just under 4000 MW and under 5 percent of current capacity). The resulting metrics are shown in the figure below128. This metric is similar to the load balancing failure metric (see Section D.3.3.4 below) and represents a ‘stress test’ against the de-rated capacity margin. This would indicate (under these assumptions) that Poland is vulnerable to the loss of its largest generator, with the derated capacity margin becoming negative where all units are off-line (although the interconnected nature of the market needs to be considered, as discussed below). This vulnerability is reduced in the “no LCPD” case In the United Kingdom, the “no LCPD” case shows a reduced the risk, although a reasonable margin is maintained in either case.

Illustration D - 98 DEPCM – typical winter peak – but with Loss of Largest Electricity Plant

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128 Metric shown for “typical” winter weather condition combined with loss of largest plant.

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In general, in the absence of the LCPD, one would expect the requirement for new investment to fall, although this reduction is felt more keenly in the United Kingdom as opposed to in Poland, whose opted-out plant are quite old and likely to close in the years leading to 2020 regardless of the LCPD. In the United Kingdom, the LCPD tends to create a sudden reduction in capacity margin because of the ‘cliff-edge’ effect of the 2015 deadline, whereas under the counterfactual the ability of opted-out plant to remain on-line smoothes the evolution of the generation mix. There is thus a much more marked impact on required investment.

As capacity cost data from PRIMES was unavailable, we have made a number of simple assumptions on costs to produce approximate metrics, using the United Kingdom as an example (given the more significant difference in capacity build compared to Poland).

Under the LCPD scenario, there is approximately an additional 10GW of capacity built by 2020, increasing capital costs over this period by around £8 billion. In both cases there is about 20 GW of renewables build (driven by the Renewable Obligation), but with the LCPD around an additional 8 GW of gas plant is built, driven by the earlier retirement of the coal and oil plant.

The capital intensity of build both with and without the LCPD is high, because of the dominance of the renewables build (which, aside from a small volume of biomass, has a capital intensity of close to 100%). With the LCPD, capital intensity falls somewhat as the proportion of CCGT technology within the mix of new build capacity increases. CCGTs have the lowest capital intensity of the main generation technologies. The results are shown below.

Illustration D - 99 Insufficient investment metrics (UK)

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As we discuss in our main report, the availability of interconnector capacity could mitigate the impact of insufficient investment – both in the longer term, where transmission upgrades could offset the need for new generation build, and in the shorter term where there is a potential surplus of existing capacity. The former would

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be very difficult to measure, but the latter could be incorporated into a Stage III metric. We have not attempted to quantify this, but it may be noted that GB typically imports across its main interconnector (with France) whereas Poland is a net exporter of power. This implies more flexibility for Poland to import as a mitigator of insufficient investment than the UK.


The metrics associated with load balancing failure improve in the scenario without the LCPD. In the United Kingdom, this creates a short-term surge in available capacity, although this then drops back as new investment between 2015 and 2020 is reduced compared to the LCPD scenario. This affects both the derated capacity margin and the flexibility margin metrics.

Consider first the former, shown in the figures below. On the left-hand side, we show the “Stage I” indicator (the de-rated peak capacity margin), and on the right-hand side we show the overall indicator, normalised by the complement of the share of electricity in total final energy consumption129, to illustrate the impact at the demand-side. In Poland, the impact of opted-out plant staying on-line has the greatest impact between 2010 and 2015. Between 2015 and 2020, investment remains largely unchanged, so that the decreased vulnerability across time similarly remains unchanged. However, in the United Kingdom, the impact on investment post 2015 results in a reduced gap between the derated capacity margin with and without the LCPD in 2015 and 2020, although the vulnerability is still lessened.

Illustration D - 100 Derated Capacity Margin

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129 In both the UK and Poland the importance of electricity (reflected by its share is final energy) is

increasing over time, and this increase is assumed to remain the same under both the LCPD and no-LCPD

scenario.

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in a system with a lower vulnerability to rapid swings in demand or output from intermittent generation. This improvement is seen in the following figures (where we again show the Stage I indicator on the left and the overall indicator on the right)130.

Illustration D - 101 Flexibility Margin

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The metric for vulnerability to resource concentration with an effective price mechanism is depicted in the figure below131. As the scenario without the LCPD in the United Kingdom implies less reliance on gas and increased reliance on coal and oil, two markets which represent greater insecurity given higher concentration of suppliers, energy security will be adversely impacted. However, in Poland, there is no change the generation mix in terms of output between the scenarios, and thus there will be no impact on energy security as measured by this resource concentration metric.

Illustration D-102 Resource Concentration Price Indicator (Effective Price Mechanism)

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130 Metric is shown for Maximum Intermittent Output Fall, SEL Ramp for Gas, Coal and Oil, and Cold Ramp

for Biomass waste. 131 Metric is shown without political stability included, and with OPEC treated as a single supplier.

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Conversely to resource concentration with an effective price mechanism, reduced dependence on gas in the “no LCPD” scenario in the UK reduces exposure to this metric (the only fuel included)132. With the generation output of Poland unchanged, its exposure is similarly unchanged.

Illustration D - 103 Resource Concentration Physical Availability Indicator (No Effective Price Mechanism)

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D 3.4 Summary

The case study examined the use of the proposed metrics in assessing the security of supply impact of the LCPD in the UK and Poland (which have a high volume of ‘opted-out’ plant). A scenario to 2020 with the LCPD was compared to a counterfactual without. In Poland the differences were relatively small as it was determined that many plant would have closed in any case. In the UK, a much higher proportion may have elected to stay open and hence the counterfactual has a much smoother evolution of the capacity profile. As a result, the LCPD tends to increase vulnerability with respect to load balancing failure. This comes about in the UK because it creates a situation where a high volume of capacity is taken off the system in a short timeframe. There is less impact on other metrics – despite a significant change in fuel use for generation, this remains a relatively small change within the overall energy system.

As the focus of this particular case study has been the electricity sector, and it is also worth considering how the de-rated peak capacity margin is used in this context. The meaning of the “absolute” level of the metric (and in particular at what point there may be a significant security of supply concern) will vary from country to country, and

132 It should be noted that as per the analysis of the climate policies, the underlying ESIC (concentration of

gas imports by country of origin) calculation at Stage I is fixed using 2008 data. Stage IV is the share in

primary energy of imported gas (not purchased on spot or spot derived markets) not covered by storage.

To 2020, gas consumption is increasing, but (under our assumptions) the share not purchased on spot

markets is declining as the gas markets mature, and available storage capacity is increasing in both

countries.

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will be influenced by factors such as the level of interconnection and any demand-side response.

The UK is a relatively isolated market (with current interconnector capacity equivalent to around 3% of total installed capacity) compared to Poland (at around 10%). In consequence, a lower capacity margin in Poland is mitigated by greater import flexibility (assuming that interconnector capacity is used efficiently). As discussed in our main report, one could envisage adapting the metric to include interconnector capacity, although in practice it would be difficult to find an objective way in which to de-rate this as a contributor to meeting peak demand. A more objective alternative would be to apply the metric at a regional level (grouping countries together).

Significant demand-side response can also mitigate security of supply concerns where the capacity margin is tight. Some demand-side response may already be implicit within the definition of peak demand: where this is derived from historical measurements, this would already include any commercial interruption that was occurring at the time. It may be expected that the materiality of demand-side response will increase over the next 10 years as demand-side policies are introduced, technology enables smaller customers to participate, and price signals become more targeted. Where demand-side response volumes can be estimated or modelled, this could be included in the metric as an adjustment to peak load.

ECOFYS INTERNATIONAL BV, A PRIVATE LIMITED LIABILITY COMPANY INCORPORATED UNDER THE LAWS OF THE NETHERLANDS HAVING ITS OFFICIAL SEAT AT UTRECHT AND REGISTERED WITH THE TRADE REGISTER OF THE CHAMBER OF COMMERCE IN UTRECHT UNDER FILE NUMBER 24343898


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