Energy Scenarios and Methodological...

Energy Scenarios and Methodological Developments

Prof. Ruud Egging, NTNU

12 June 2018

41st IAEE International ConferenceGroningen, Netherlands

Ruud Egging 2018 IAEE Groningen 2

Lies, Damned Lies, and … Scenarios?

Free after Mark Twain

According to an analysis by XYZ…• Forbidding CCS may double the cost of the energy transition• Shale gas has larger global warming impact than coal• Domestic electricity bills in Norway will increase by 25% if it would

become the green battery for Europe• An ETS floor price of € 20 per ton will hamper recovery of the EU

economy • To meet climate mitigation targets we must all become

vegetarians• A sudden influx of one million refugees in Germany will damage

economic growth and cause a major budget deficit• Great Britain can negotiate better bilateral trade deals than as

part of the EU• It is very unlikely for Ajax to not qualify against Rosenborg over

two matches in EL qualifications• If I had massaged my scalp with magic oil every day over the last

two decades I would still have had a full head of hair

Ruud Egging 2018 IAEE Groningen4

Before reading an article, see who has written it,

and what is their interest

Mrs. Inge van Beek, my English teacher in high school, about 30 years ago

Outline

• Energy scenarios– Scenarios– What is a scenario?– What is a good scenario?– How to develop a good scenario?

• Methodological developments - Linking models– Model types and characteristics– Levels of model linkage and data exchange– Benefits and challenges of model linking

• Linking in practice– LCE21 SET-Nav Navigating the Roadmap for Clean, Secure and

Efficient Energy Innovation

– Model linking in SET-Nav


ENERGY SCENARIOS

Stanford Energy Modeling Forum

• Comparing scenario results with (partially) harmonized input data:


Russian Share of Western European supply in 2020 – eight different models


52%

13%

EMF Report 23 Sept 2007

Western European Prices relative to 2005


170%

78%


US supply sources 2020, relative to 2006



Actual Cons will likely be around high end ☺

But net trade only few %, possibly exports , not 15%-30%

2006

SHELL – Temperature increase for three scenarios


Mountains and Oceans explore alternative socio-political pathways (…) with emissions as an open-ended outcome. Sky (…) progressively driven (…) to achieve net-zero emissions by 2070

shell.com/energy-and-innovation/the-energy-future/scenarios.html

Shell asked MIT to check scenario Sky on temp increase, it levels out below 2C after 2060 ☺

Sky developed TO MEET 450ppm…!

World Energy Outlook (WEO) 2017 p.145


New Policies Sustainable Development

To meet climate targets, consumption of the most polluting fossil fuel has to decrease most…

WHAT DOES ALL THIS MEAN?

IEA World Energy Outlook 2017 p. 40

• IEA scenario results (…) sometimes mis-characterised as forecasts. They are not.

• Each scenario depicts an alternative future, a pathway along which the world could travel (…)

• (IEA does provide short to medium-term forecasts (…) but) there are no longterm IEA forecasts; (…) simply too many variables in play (…)


WHAT IS A SCENARIO?

What is a scenario?

• A postulated* sequence or development of events. (Oxford dictionary)

• A modelling scenario:

– A choice of exogenous input values consistent with someone's perspective on a possible future development + ???

+ the endogenous model outcomes

+ interpretations of model results…?

+ all model modifications in development, testing …?

+ all data modifications in compilation, validation, calibration, exchange, …?

• case, case study, experiment, simulation, what-if analysis, pathway, storyline, …?


What is a good scenario?

• Provides relevant insight for good decision making• Backed up by a coherent qualitative storyline• Consistent assumptions and boundary conditions• Logical and transparent parameterization• Developed with model strengths and weaknesses in mind• High quality model data sets and implementation• Try not answer questions model is not suitable for• Does not repackage assumptions as new results• Is compared against a good and clear benchmark• Is one of several scenarios exploring a wide enough range of

futures• Model, inputs and results documented transparently• Results verifiable


STORY LINES

Story line

• Qualitative setting and underpinning for quantitative scenario.

• geopolitical, cultural, societal context, main trends/developments, major events

• Auer & Zach 2009.

– story lines: clusters of uncertainties


Story lines

• Reference

– base case, benchmark, business as usual, continuing trends, current policies, …

• Contrasts:

– Scope out a wide enough range of possible futures based on dimensions spanning extreme outcomes of fundamental drivers


European Energy Futures


Credit: Bruggink May 2005, The Next 50 years – Four

European Energy Futures, ECN

Penetration of renewables…


Credit: Auer and Zach, 2009, Definition of a Consistent Scenario Framework for ….

SET-Nav Energy Transition Pathways

National champions

Directed vision

Diversifi-cation

Local-ization

CooperationD

isru

pti

ve

Inn

ova

tio

n

Path

dep

en

den

cy

Isolation


Some terms adjusted for clarity

How to develop a good set of scenarios

• What is scope of desired insights and contributions?• What models would be suitable to provide such insight?• Brainstorm on events and drivers affecting relevant

outcomes• Open discussion on

– logical grouping of events and drivers: contrasting dimensions & coherent storylines

– Parameterizations (probably need some iterations)• Numerical and qualitative analysis and interpretation• Present insights in a way suitable for intended audience

– Give a fair account, no cherry-picking– Explain main drivers, incl. assumptions and boundary

conditions.


Pix credit: https://pixabay.com

https://pixabay.com/

METHODOLOGICAL DEVELOPMENT - MODEL LINKING


Three of the main model types


I.A.M.

C.G.E.

P.E.

CAP endow

Income from CAP

Income (Y)

Expenditure good 2

LAB endow

Income from LAB

Expenditure good 1

Production good 2

Production good 1

Integrated Assessment ModelComputational General EquilibriumPartial Equilibrium Pix credit: Dreamstime.com

Characteristics of some model types used for policy support and analysis

IAM CGE P.E.

Perspective Holistic Economy (Energy) sector

Metrics and relations

Aggregate economic, technical & physical

Aggregate economic

Detailed economic & physical

Climate + - -

Environment + - -

Carbon cycle + - -

Land use & forestry + - -

Oceans & water + - -

Economy & trade + + -

Spatial detail - + +

Technical detail - - +

Temporal detail - - +


For illustration only

Let each model type do what it’s good at

• Isolated– Define consistent parameterizations for all models– Run models – Explain to audience why results are inconsistent

• Integrated– Develop one model combining all strengths of all models– See it fail miserably trying to find a solution

• (Soft / Hard) Linked– Decide relative strengths of each model and which

(exogenous) inputs are based on (endogenous) outputs of others

– Define consistent parameterizations– Iteratively run models and exchange data until results are

consistent


Linking models - why

• No single model can covers all aspects in all desired detail.• Need consistent & coherent quantitative representations across

models to analyze scenarios• Models have different

– Approaches– Foci: strengths & weaknesses– Scopes, scales / granularities & information structure– Exogenous (parameters) vs endogenous variables / boundary

conditions– Physical vs monetary representation; units of measurement→Features give complementary strengths but pose challenges for exchanging data

• One model’s results are another’s boundary conditions


Using relative strengths:


I.A.M. C.G.E. P.E.

▪ Climate▪ Land use▪ Global trade▪ Emission ceilings / costs

▪ Energy demand

▪ Energy prices▪ Outputs▪ Prices

Challenge: Spatial granularity

• Aggregating geographical regions:

– Extremes become less extreme

• Uncorrelated high and low values cancel out each other

– Transportation losses and bottlenecks disappear

• Less or no intra-regional transport

– More aggregate model will show lower need for flexibility & storage.

source: www.vecteezy.com

source: www.worldatlas.com


http://www.vecteezy.com/

http://www.worldatlas.com/

Challenge: Temporal Granularity

• Aggregating time periods:– Extremes become less extreme

• High & low values cancel out each other over longer periods

• More aggregate model will show lower need for flexibility & storage.

• “Supply side”– Reduces demand flexibility due to shiftable loads– Reduces the number of “cycles” affecting life

expectancy of batteries

• Related: reconciling stochastic model outputs with inputs for a deterministic model


Challenge: Technical granularity

• Having only one type of coal-fired power plant, one type of gas-fired, etc.. gives a much harsher merit order.

• Grid / Entry-exit capacities NTC vs loop flows

• …


Challenge: units of measurement

• P.E. Energy

– TJ, MWh, QBtu, bcm, Mtoe

– Bcm: • Density? Calorific value Groningen gas - scm

• Heating value? Lower HV-Higher HV

• Liquefied?

• CGE: Monetary

– USD, Euro, Yen,

– of what year?

– …


Challenge: version control

• Models & data sets

• Requires professionalization and discipline

• More complex for shared, open-access models & data


Challenge: convergence

• Model(s) dependent– Some theoretical results may apply, but..– Not all models may be optimization or equilibrium

models– Exchange potential with models not overly sensitive for

input changes and no erratic result behavior– Test this early– Cycling? Intervene …

• NTNU PhD candidate P.I. Helgesen extensive research on linking & integration regional energy systems & regional CGE– TIMES Norway – REMES CGE– It always converges


Linking models – how?

• Middleware

– all models exchange with one central DB:

– Reduce number of links from M(M-1) to 2(M+2)

• Upfront agreement on

– Technical, Spatial, Temporal resolutions

– Standardized data table formats

– Units of measurement

– Unique identifiers for data sets and entries

• Automated scripts for hard-linking


SET-NAV

Navigating the Roadmap for Clean, Secure and Efficient Energy Innovation

SET-Nav EC H2020 grant 691843

SET-Nav Key pillars

Ruud Egging 2018 IAEE Groningen

• Combining theory of

technology innovation,

diffusion & spill-overs with

large-scale energy-

economy-engineering

models.

• Developing

methodological framework

& infrastructure for

effective model

integration to adequately

capture interdependencies

across levels, energy

carriers, and sectors.

• Twelve case studies

• Four pathways

Enhancing

modelling

capacities

Stakeholder

dialogue &

dissemination

Strategic

policy analysis

enhancing innovation

towards a clean, secure and efficient

energy system

39

Case: centralised vs. decentralised electricity supply

TEPES

Enertile

Case Study

Analysis

Electricity generation, Capacities,

Region definition

Capacities, Investments,

Region definition, NTCs,

Investment Options

for the gridElectricity

generation, Investments,

Costs, Emissions,

Fuel consumption

Detailed grid costs

Challenges: next slide


Two power system models:

• Enertile: power system optimization• Tepes: power transmission expansion planning• Enertile→ Tepes:

– electricity capacities – electricity generation

• Tepes→ Enertile:– detailed transmission grid expansion results– unit expansion costs

• Challenge:– Tepes considers individual lines– Enertile works with regions– need sophisticated approach to aggregate /

disaggregate exchanged data


TEPES

EMPIRE

Analysis

Electricity generation,

Electricity capacities

RAMONA

Grid Investments

EGMM

Gas production cost,

Gas infrastructure

Gas demand, Gas prices

NEXUS security

Gas operation,Gas infrastructure

Electricity generation, Electricity capacities

Gas operation, infrastructure,

investments, costs, emissions

Electricity generation, capacities, investments,

fuel mix, costs, emissions

System security/reliability indices electricity/gas

Detailed grid costsGrid investments Gas demand,

Gas prices

Case: unlocking flexibility in power & gas supply systems


Cases▪ All models calibrated to PRIMES decarboniz scenario (E3MLab & IIASA 2016)

▪ Nuclear restricted [current cap levels; no replacement]

▪ No CCS

▪ Flexi-Grid - Transmission

▪Transmission expansion allowed (Electricity)

▪EMPIRE: intra-day demand response + curtailment

→ Grid expansion & hydro main sources flexibility

▪ Flexi-1 – electricity storage

▪Transmission is restricted [current levels + 10Y ENTSO-E NW plan]

→ More electricity storage and gas thermal plants.

▪ Flexi-2 – gas infrastructure

▪Transmission is restricted [idem]

▪Electricity storage expensive

▪Demand response (current levels)

→ Gas thermal plants main flexibility provider.

E3MLab and IIASA (2016). Technical report on Member State results of the EUCO policy scenarios. EUCO 27scenario. Energy Modelling, European Commission. https://ec.europa.eu/energy/en/data-analysis/energy-modelling

EU power generation mix

gas

solar pv

wind onshore

nuclear

coallignite

hydro

w-offs

Gas primarily base-load in 2025; in 2050 balancing.

Utilization 64 (+/-1)% in 2025; 15% in 2050.

Can we learn from others?

• We need openness and transparency to create credibility and accountability

• Climate modeling community is much further on this than energy modeling community

• Social sciences it has become normal to make data sets accessible to reviewers

45Ruud Egging 2018 IAEE Groningen

Discussion points

• Projections more than two decades into the future are meaningless & create a false sense of understanding and control.

• There are too many scenarios. Every five years the modeling community should establish a reference scenario that everyone will use as benchmark.

• All our models are black boxes. We customize assumptions, inputs and scenarios to create the message that we want to create.

• Every published paper & report should have accessible online– All raw & processed & calibrated inputs & raw output data – An extensive sensitivity analysis, and an “executable” so that

experiments can be replicated by anyone.– And after ten years: a retrospective analysis explaining why

results were wrong & needed improvements.• Funding agencies should only fund projects which adhere to this


Pix credit: www.advocatesouth.co.nz

THANK YOU

[email protected]

Acknowledgement: SET-Nav EC H2020 grant 691843& Special thanks to post.doc dr. Pedro Crespo del Granado

mailto:[email protected]

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