FINAL REPORT
FOR THE
QUANTITATIVE VIABILITY ANALYSIS OF
ELECTRICITY GENERATION FROM NUCLEAR
FUELS
Nuclear Fuel Cycle Royal Commission
Authored by: DGA Consulting Carisway
Date of issue: 05/02/2016
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Document Control
Customer Details
Customer Name: Nuclear Fuel Cycle Royal Commission
Customer Address: Level 5, 50 Grenfell Street,
Adelaide, SA, 5000
Contact Person: Ashok Kaniyal
About this Document
Title: Final Report
Date of Issue: 5/02/2016
Prepared by: Dave Lenton (DL)/Robert Riebolge (RR)
Approved by: Dave Lenton (DL)/Robert Riebolge (RR)
Rev No. Date Reason for Issue Updated by Verified by
0.1 21/12/15 Draft for Review DL/RR DL/RR
0.2 21/01/16 Recast Draft for Review DL/RR DL/RR
1.0 27/01/16 Recast Draft for Review DL/RR
2.0 05/02/16 Draft Report for Public Consultation DL/RR DL/RR
Confidentiality
This Report may contain information that is business sensitive to DGA Consulting/Carisway or the Nuclear Fuel
Cycle Royal Commission (NFCRC). No part of this Report may be used, duplicated or disclosed for any purpose
unless by express consent of the Nuclear Fuel Cycle Royal Commission. As such the use of the information in
this Report is regarded as an infringement of DGA Consulting/Carisway’s intellectual property rights.
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CONTENTS
EXECUTIVE SUMMARY ........................................................................................................................................ 8
1 Introduction..................................................................................................................................................... 16
1.1 Study objective ......................................................................................................................................... 16
1.2 Study approach ........................................................................................................................................ 16
2 Projecting South Australian Demand .............................................................................................................. 19
2.1 Historic electricity demand in South Australia .......................................................................................... 19
2.2 Projecting methodology ............................................................................................................................ 24
2.3 Projection parameters in the 2030 and 2050 models ............................................................................... 25
2.4 System demand by 2030 and 2050.......................................................................................................... 27
3 Renewable Generation in South Australia ...................................................................................................... 29
3.1 General methodology ............................................................................................................................... 29
3.2 Renewable generation output .................................................................................................................. 30
3.3 Interconnector capacity ............................................................................................................................ 34
3.4 Scenario selection for renewable generation ........................................................................................... 34
3.5 System demand compared with projected renewable generation ............................................................ 37
4 Generation and Dispatch in South Australia ................................................................................................... 40
4.1 Generating capacity of new plant ............................................................................................................. 40
4.2 Hierarchy of plant dispatch ....................................................................................................................... 40
4.3 Hierarchy of generation supply for South Australian generators .............................................................. 41
4.4 Example of generation dispatch ............................................................................................................... 43
4.5 Output for the operation of nuclear/CCGT option ..................................................................................... 47
4.6 Summary of demand and technology inputs to economic modelling........................................................ 49
4.7 Renewables and generation mix for selected scenarios .......................................................................... 53
4.8 The importance of an enhanced interconnector ....................................................................................... 57
5 Generator Cost and Benefit Assumptions ...................................................................................................... 62
5.1 Approach to the economic model ............................................................................................................. 62
5.2 Economic scenario assumptions .............................................................................................................. 62
5.3 Derivation of the key parameters ............................................................................................................. 65
5.4 Sensitivity based key parameters ............................................................................................................. 69
6 Viability Assessment of Generator Options .................................................................................................... 72
6.1 Review of NPV results ............................................................................................................................. 72
6.2 Review of LCOE results ........................................................................................................................... 72
6.3 Breakdown of component costs ............................................................................................................... 73
6.4 Breakdown of revenue and LPOE ............................................................................................................ 76
6.5 Internal rates of return .............................................................................................................................. 77
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6.6 Carbon amelioration benefits of the technologies .................................................................................... 77
7 Sensitivity and Monte Carlo Analysis ............................................................................................................. 79
7.1 Overview .................................................................................................................................................. 79
7.2 CCGT with CCS ....................................................................................................................................... 79
7.3 Small nuclear ........................................................................................................................................... 80
7.4 Large nuclear ........................................................................................................................................... 81
7.5 Combined cycle gas turbine ..................................................................................................................... 82
8 Impact of Alternative System Scenarios ......................................................................................................... 84
8.1 Approach .................................................................................................................................................. 84
8.2 Scenario 1 - Medium growth demand and renewable penetration ........................................................... 84
8.3 Scenario 2 - High demand growth with low renewables penetration ........................................................ 84
8.4 Scenario 3 - High demand growth and high renewables penetration ....................................................... 85
8.5 Load following mode ................................................................................................................................ 86
8.6 Social discount rate .................................................................................................................................. 88
9 Game Changing Events ................................................................................................................................. 90
9.1 Introduction .............................................................................................................................................. 90
9.2 Game changers........................................................................................................................................ 90
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Glossary
Term/Abbreviation Description
AC Alternating Current
AEMC Australian Energy Market Commission
AEMO Australian Energy Market Operator
AER Australian Energy Regulator
AETA Australian Energy Technology Assessment
B/C Benefit Cost Ratio
BIS Baseline Climate Change/Action Scenario
BREE Bureau of Resource and Energy Economics
CGE Computational General Equilibrium Model
CCGT Combined Cycle Gas Turbine
CCS Carbon Capture and Storage
CCS/SC Carbon Capture and Storage/Supercritical
CPP Critical Peak Price
DC Direct Current
DG Distributed Generation
DGS Distributed Generation and Storage
DLC Direct Load Control
DNSP Distribution Network Service Provider
DRED Demand Response Enabling Device
DS Demand Scenario
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Term/Abbreviation Description
EPRI Electric Power Research Institute
EST Eastern Standard Time
EV Electric Vehicle
EY Ernst & Young
FGF Future Grid Forum
FOM Fixed Operation and Maintenance
FV Future Value
GJ Giga Joule
GW Giga Watt
GWh Giga Watt hour
HH Half Hourly
HV High Voltage
IGCC Integrated Gasification Combined Cycle
IoT Internet of Things
IRR Internal Rate of Return
IT Information Technology
IS2 Moderate Climate Change/Action Policy Scenario
IS3 Strong Climate Change/Action Policy Scenario
LCOE Levelised Cost of Electricity
LNG Liquefied Natural Gas
LPOE Levelised Price of Electricity
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Term/Abbreviation Description
LRET Large Scale Energy Renewable Target
LV Low Voltage
MLF Marginal Loss Factor
MWe Mega Watt electric
MWh Mega Watt hour
NFCRC Nuclear Fuel Cycle Royal Commission (the Commission)
NEL National Electricity Law
NEM National Electricity Market
NER National Electricity Rules
NOAK Next of a Kind
NPV Net Present Value
p.a. per annum
ppm Parts per million
PB Parsons Brinkerhoff
PHWR Pressurised Hot Water Reactor
PV Photovoltaic or Present Value
RIT-T Regulatory Investment Test - Transmission
RRP Regional Reference Price
SEMAAC Socio Economic Modelling and Assessment Advisory Committee
SMU/Candu PHWR Small Modular Reactor/Canada Deuterium Uranium Pressurised Hot Water
Reactor
SoW Statement of Work
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Term/Abbreviation Description
SRET Small Scale Renewable Energy Target
STP Solar Thermal Plant
TNSP Transmission Network Service Provider
ToU Time of Use
TS Technology Scenario
TUoS Transmission Use of System
V2G Vehicle to Grid
VOM Variable Operation and Maintenance
WACC Weighted Average Cost of Capital
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EXECUTIVE SUMMARY
Study objective
The key objective of this Study was to quantify the relative economic viability of integrating nuclear power generation technologies
as one of a suite of renewable and fossil fuel power generation technologies within the National Electricity Market (NEM) in the
years 2030 and 2050.
The modelling needed to undertake a comparative assessment of market entry for four generator options sited in South Australia,
which comprised:
Small nuclear plant - 285MWe (consisting of 6 x 47.5MWe small modular reactors)
Large nuclear plant - 1,125MWe AP1000 reactor
Combined cycle gas turbines (CCGT) with carbon capture and storage (CCS) - 327MWe
CCGT - 374MWe
The analysis required an assessment of the costs and benefits of these generator options under a variety of electricity demand and
renewable generation scenarios in South Australia. The results needed to be presented as a net present value (NPV) calculation that
included sensitivity analysis with a range of possible outcomes.
Study approach
The Study approach was broken down into a number of defined activities that were combined to produce the economic assessment
for each of the generator options as shown in Figure 1. More detail on each of these activities is provided the Sections that follow.
Figure 1: Overview of the Study approach
Projecting demand and generation
The model used half hourly (HH) data sets of demand and renewables generation supplied courtesy of SA Power Networks for the
following consumer categories and renewables:
Demand
Major customer category
Business consumer category
Residential consumer category
Hot water load
Renewables
Photovoltaics (PV)
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Wind generation
The importance of this level of granularity is that the HH data provides for more accurate categorisation of the load characteristics
of: peak demand values and durations; temporal (i.e. week days or weekends) and seasonal variability of the loads; and
disaggregated consumer and renewable generation characteristics. The finer granularity means that load shapes more closely mimic
the real time demand shapes, thus providing greater confidence in projecting the demand shapes leading to ‘statistically’ more
credible projections. An example of the projected demand profiles of individual consumer categories with the additional new
category of electric vehicles (EV) for the whole of the South Australian grid is illustrated in Figure 2. These profiles contrast the
significant difference of a low demand day with a high demand day.
Figure 2: Projections of major customer category demand profiles for days of minimum/maximum demand in South Australia1
To meet the projected system demand, account had to be taken of the likely sources of generation that would be present in the
system in the time horizons of 2030 and 2050. The model allowed for likely technologies pertaining to distributed generation and
storage, solar thermal, combined cycle gas turbines (CCGT) and nuclear to be selected by the user, and dispatched the generation
fleet so that it met the projected demand profile as shown in the example graphs in Figure 3. Surplus generation could be exported
to the NEM via the interconnector(s).
Figure 3: Projections of generation mix to meet the system demand for days of minimum/maximum demand in South Australia2
Generation dispatch for new South Australian generators
The model applied the demand projections, renewable generation projections and interconnector constraints to calculate the
amount of electricity that could be provided by each of the generator options to supply South Australia, with any surplus power
1 Legend in this Section: For the time horizon 2030: T30ev = electric vehicle demand; T30res = residential consumer category demand; T30bus = business consumer category demand; T30hw = Hot water load; T30mjc = major customer category demand.
2 Legend in this Section: foss = fossil fuel plant; stp = solar thermal plant; evs = storage release from electric vehicles with V2G (vehicle to grid) installations; winds = wind generation paired with grid storage; pvs = photovoltaics paired with battery storage; nuc = nuclear plant; windo = wind generation without storage; pvo = photovoltaics without storage.
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exported to other parts of the NEM. In determining these generation requirements the model was set up to assume that the nuclear
generator (or the new CCGT options) could be dispatched immediately after any PV and wind that had no storage facility. These
resulting generation profiles provided the basis for the economic assessment of the viability of the nuclear option or its CCGT
alternatives.
Impact of the enhanced interconnector
Within the modelling an interconnector and network upgrade was included for the Base scenario without any of the direct costs of
the interconnector and network upgrade being allocated to the nuclear/CCGT options. The evidence presented by ElectraNet in the
public sessions3 suggested that there are several locations in South Australia where a single large nuclear generator of capacity of
the order of 600MWe could be installed without any upgrades to the transmission network being needed. However, the installation
of new generation capacity may require an upgrade of the 275kV high voltage backbone and an expansion of interconnector capacity
to the eastern regions of the NEM. Any upgrade would improve the effective capacity factor of renewable generators located in
South Australia as well as the capacity factor of the large nuclear option. The impact of such an interconnector upgrade was tested
against a range of scenarios for renewable generation and demand and is shown in Table 1.
Table 1: Percentage of nuclear and renewable generation not able to be exported with a large nuclear option operating in baseload mode under a high interconnector constraint (650MWe)
Scenario4 Demand:
Rate of
increase
Renewables:
Rate of Increase in
Installed capacity
Electric Vehicles:
Market penetration
(%)
% reduction in large
nuclear energy
exports with high
(650MWe)
IC constraint5
% reduction in
renewables exports
with high
(650MWe)
IC constraint
Base IS3 and
medium
IS3 and low 20% 52% 42%
1 Medium Medium 28% 28% 56%
2 High Low 28% 17% 28%
3 High High 28% 20% 63%
4 IS3 and
medium
IS3 and low and no
more wind capacity
20% 19% 34%
The figures in Table 1 show that the maintenance of the interconnector capacity at 650MWe6 will lead to 56% of renewable
generation being constrained (Scenario 1 assumed to include 3,000MW of wind, medium penetration of grid storage, saturation
penetration of rooftop PV and the installation of a 280MW Solar Thermal Plant) in 2030. With the current level of interconnector
capacity the large new nuclear generator (1,125MWe) would face a constraint of 52% to its exports. Under the high scenarios of
demand with low or high new installed renewable generation capacity in South Australia between 17% to 20% of generation from a
large nuclear generator operating in baseload mode would be constrained. Indeed, this level of additional supply could significantly
depress wholesale electricity prices at the South Australian regional reference node and lead to a significant increase in the
frequency of negative price events as noted by Dickinson RR in evidence presented to the Royal Commission.
3 http://nuclearrc.sa.gov.au/app/uploads/mp/files/videos/files/150918-topic-2-day-1-transcript-full-nfcrc.v5.pdf
4 Defined under ‘Cost and benefit assumptions of generator options’ below.
5 Operating in baseload mode.
6 ElectraNet have stated that the Heywood interconnector is being upgraded to 650MW. There is additional capacity from the Murraylink interconnector, which should allow the combined capacity to be 870MW (ElectraNet Network Vision Discussion Paper, The future of South Australia’s regulated transmission network, December 2015). The modelling tests a worst case scenario where the Murraylink interconnector is not operational and the capacity is limited to 650MW.
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Under all scenarios for growth in installed capacity, the viability of renewable and/or nuclear generation in South Australian would
be enhanced with a significant new source of demand. This could include the development of flexibly operated power to fuel
technology7 and/or a significant expansion of the interconnector capacity to replace ageing capacity and fossil fuel intensive capacity
in the eastern regions of the NEM. Here, it is important to note that while the expansion of interconnector capacity and transmission
upgrades would generate opportunities to export surplus renewable and nuclear generation from South Australia to the eastern
regions of the NEM, the cost of the interconnector and transmission network upgrade is material. An assessment of the viability of
an interconnector upgrade is subject to a regulatory investment test (RIT-T) and is contingent upon the upgrade delivering net
market benefits across disparate regions of the NEM. This is outside the scope of the present Study.
Cost and benefit assumptions of the generator options
The key modelling assumptions impacting the cost of the generator options are provided below split into capital cost and operating
cost. All costs are in real 2014-15 dollars and all generator options have a real discount rate of 10% applied with a valuation date
aligned with the commissioning date of the generator.
There is considerable uncertainty on the climate change/action policy that will be adopted and the modelling has assessed three
different climate change/action policy scenarios. These scenarios impact costs (i.e. the carbon price and gas prices) as well as the
wholesale price of electricity and are composed of:
Baseline Climate Change Scenario (BIS) – Based on current government targets and mechanisms for emissions reduction
with a carbon price from 2030.
Moderate Climate Change/Action Policy Scenario (IS2) – Assumes a carbon price from 2020 to achieve current emissions
reductions targets.
Strong Climate Change/Action Policy Scenario (IS3) – Includes the expectation of a more dramatic reduction in emissions
of 40% to 60% by 2030. This carbon emissions reduction target for 2030 is consistent with a target of 1.5 degree centigrade
of average warming by 2100.
Capital cost
Capital costs are the most important cost element for the nuclear options and are equivalent to between 69% of the present value
(PV) of costs for the nuclear generators once pre-construction and interest costs are included in 2030/2050. The costs for the plant
were applied on an agreed profile with interest during construction being included for all options. This was based on a 5 year
construction schedule for the large nuclear option, 3 years for the small nuclear option and 2 years for other options. An overview
of the cost breakdown for the different options is provided in Table 2.
7 Dickinson RR. Evidence to the Nuclear Fuel Cycle Royal Commission. 4 September 2015.
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Table 2: Construction costs of generator plant options in 2030
Plant options Overnight
capital cost
($/kW)
Preconstruction
cost
($/kW)
Interest during
construction
$/KW
Total
cost
($/kW)
% of PV of plant
lifetime costs
CCGT with CCS 2,567 92 271 2,930 27%
Small nuclear 8,822 1,638 2,316 12,775 69%
Large nuclear 7,613 415 2,563 10,591 69%
CCGT 1,579 45 162 1,787 20%
The modelling includes the construction cost of a 2GW transmission and interconnector upgrade between South Australia and
Victoria. This is primarily needed for the large nuclear option, but benefits all plants including the renewable generators in South
Australia. The base assumption is that this will be financed by the transmission network service provider as it could have wider
benefits through reductions in wholesale electricity prices in both South Australia and also the NEM.
Operating costs
A summary of the percentage split of operating costs applying for a plant commissioned in 2030 under the Moderate climate
change/action policy scenario (IS2) is shown in Table 3 (note: capital and infrastructure costs are added for completeness). Operating
costs will vary for each year, being aligned with the carbon price and gas price trajectories for 2030. The figures demonstrate the
importance of fuel and carbon costs for the CCGT options relative to the nuclear options, which is reflected in the changing operating
costs in the different climate change/action policy scenarios.
Table 3: Costs of generator plant options as a % of lifetime costs
Plant options Fixed
O&M
Variable
O&M
Fuel cost Carbon cost Other/De-
commissioning
cost
Capital &
Infrastructure
cost
CCGT with CCS 5% 9% 41% 12% 1% 32%
Small nuclear 15% 0% 6% 0% 4% 76%
Large nuclear 18% 0% 5% 0% 5% 72%
CCGT 3% 1% 43% 28% 1% 25%
Wholesale electricity price and generator operating assumptions
The wholesale electricity price trajectories were produced by Ernst & Young (EY)8 based on the different climate change/action policy
scenarios. In their modelling, the nuclear options were not initially selected for operation and an additional run was undertaken to
assess the impact on the wholesale electricity price if nuclear options were included in the generation mix. The same percentage
wholesale electricity price reduction was used in all the calculations when assessing the viability of the nuclear options under the
BIS and IS2 climate change/action policy scenarios9. The climate change/action policy scenario and the resulting carbon price has a
material impact on the wholesale price of electricity, shown in Table 4, with the difference being $31/MWh by 2050 between the
BIS/IS2 and IS3 climate change/action policy scenarios.
8 EY Electricity Market Modelling Draft 30th November 2015
9 If any of the CCGT plants were assumed to be running in baseload mode then the same price reduction would be applied as for the small nuclear option as the plants are a similar size. However, the base assumption is that they would run at mid-merit order (c.f. Section 5.2.2).
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Table 4: Wholesale electricity prices under different climate change/action policy scenarios
Climate change/action policy scenario Wholesale electricity price
($/MWh)
Financial year starting 2030 2040 2050
Baseline investment scenario (BIS) $124.0 $133.1 $154.2
Moderate climate change/action scenario (IS2) $125.1 $141.9 $161.7
Strong climate change/action scenario (IS3) $138.7 $155.0 $185.7
Strong climate change/action scenario (IS3) with large nuclear $105.6 $124.3 $148.0
Strong climate change/action scenario (IS3) with small nuclear $130.3 $146.1 $175.8
The CCGT plants were not assumed to operate in baseload mode, as during low price periods the marginal cost of operating these
plants would have exceeded the marginal revenue. To reflect this, the modelling applied the EY assumed capacity factor for the
CCGT option along with the average wholesale electricity price received for this level of operation for the 2030/31 and 2049/50 time
horizons. This data is shown in Table 5 and has been used to derive an annual increase in wholesale electricity prices for each year
from 2030/31.
Table 5: Capacity factors and wholesale electricity price adjustments
Climate change/action policy scenario / year BIS
2030/31
BIS
2049/50
IS3
2030/31
IS3
2049/500
Capacity factor CCGT (%) 68.2% 65.5% 66.9% 64.1%
% Increase in wholesale electricity price received 16.8% 20.4% 18.1% 23.0%
The NPV modelling results presented below apply these capacity factor adjustments for the CCGT options.
Viability assessment of generation options
The NPVs for the different generation options and climate change/action policy scenarios are shown in Table 6. These NPVs are
based on the most likely value of each of the key parameters that contribute to the NPV analysis such as the discount rate, capital
cost, operating cost, carbon price and so forth with a defined external source having been referenced for each of these parameters.
Whilst there is a range of results depending on commissioning timelines/climate change/action policy scenarios, the nuclear options
consistently deliver negative NPVs under the current set of assumptions.
Table 6: NPV of different generator plant options A$m.
Plant options Baseline climate change
policy scenario (BIS)
Moderate climate
change/action policy
scenario (IS2)
Strong climate
change/action policy
scenario (IS3)
2030 2050 2030 2050 2030 2050
CCGT with CCS -$ 479 -$ 66 -$ 334 $ 99 $ 9 $ 617
Small Nuclear -$ 2,326 -$ 2,068 -$ 2,182 -$ 1,901 -$ 1,820 -$ 1,365
Large Nuclear -$ 7,870 -$ 6,960 -$ 7,413 -$ 6,416 -$ 6,263 -$ 4,680
CCGT $ 80 $ 222 $ 222 $ 372 $ 319 $ 565
The modelling also considered the levelised cost of electricity (LCOE), which shows the cost (in real dollars) per MWh of constructing
and operating the generation options over their assumed lifetime. The LCOE is shown in the chart in Figure 4 for the four generator
options with the CCGT/CCGT with CCS operating as a mid-merit order plant. The results are consistent with the NPV analysis and
indicate that the nuclear options generally have a higher LCOE than the CCGT/CCGT with CCS options.
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Figure 4: LCOE for generator options under varying climate change/action policy scenarios
The LCOE results are not directly translated into the NPV results for the different generators. An additional consideration is that the
large nuclear option will have a material impact on the average wholesale electricity price as a result of its bidding patterns. There
is also an impact from the small nuclear option, or any other similar capacity plant when operating in baseload mode, but it is lower
at just above 5% of the wholesale electricity price. This is shown in the chart in Figure 5 for the levelised price of electricity (LPOE).
The combination of the LCOE and LPOE along with the capacity of the generators is reflected in the NPV calculations.
Figure 5: LPOE for generator options under varying climate change/action policy scenarios
Sensitivity of nuclear viability to key inputs
One of the challenges of the NPV analysis is the level of uncertainty associated with many of the key inputs. There are likely to be
significant differences in the projected value for a number of key inputs such as discount rates, life of plant, capital cost, fuel cost,
etc and these will have a material impact on the NPV of the different options being considered. Sensitivity testing considers some of
the changes in the key parameters that would be required to make the nuclear option viable for commissioning under the IS3 -
Strong climate change/action policy scenario, which was seen as the most likely scenario under which a nuclear generator might be
commissioned in the short term.
0.0 50.0 100.0 150.0 200.0 250.0
CCGT with CCS Mid-Merit
Small Nuclear
Large Nuclear
CCGT Mid Merit
LCOE $/MWh
IS3 2050 IS2 2050 BIS 2050 IS3 2030 IS2 2030 BIS 2030
0.0 50.0 100.0 150.0 200.0 250.0
CCGT with CCS Mid-Merit
Small Nuclear
Large Nuclear
CCGT Mid Merit
LPOE $/MWh
IS3 2050 IS2 2050 BIS 2050 IS3 2030 IS2 2030 BIS 2030
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It is possible that a fully funded nuclear generator might secure a commercial discount rate below 10% and the Commission has
received evidence that this rate may be influenced by the existence of a secure waste storage and disposal facility. To assess the
importance of the required return to the viability of the plant an evaluation was undertaken of the internal rate of return (IRR) of
the generators. The large nuclear option has an IRR of 5.6% under the IS3 - Strong climate change/action policy scenario in 2030
with the small nuclear generator having an IRR of 5.9%. This is still significantly lower than the commercial discount rates used in
the model, but is almost 2% more than the 4% ‘social’ discount rate identified by the NFCRC as reflective of the rates that public
projects typically receive for finance.
The capital costs allocated to the nuclear option are relatively high compared to other baseload generation technologies and have
increased from the figures quoted in studies from 2012/2013. If the capital costs were to decrease by 25% it would bring down the
LCOE values of the large nuclear option to 10% below the LCOE of the CCGT plant operating in mid-merit order. However, the impact
of the large nuclear generator on the South Australian regional reference price, leads to a differential of over $60/MWh in the LPOE
secured by the nuclear generator over its operating life as compared with the CCGT option. This leads to the NPV of a large nuclear
generator remaining negative. While nuclear power may have a lower LCOE under a variety of cost and finance scenarios, the low
variable cost of operation leads to market prices in the South Australian region being suppressed to a significant degree when it is
introduced as a new source of generation. There may be alternative wholesale market designs that would be beneficial to nuclear
generators, but this is a complex assessment that was outside the scope of this Study.
One of the elements restraining the level of wholesale electricity prices was the introduction of CCGTs into the market model as the
lowest cost form of baseload generation. The development of more aggressive climate change/action policy scenarios is expected
to see continued reduction in the average lifecycle carbon emission intensity of electricity generation across the NEM to below
0.25t CO2-e/MWe for emissions under the aggressive climate action scenarios. This means that a CCGT plant will have significantly
higher carbon emissions than the average intensity of the NEM and that situation increases over time as average carbon emissions
continue to fall to 2050. While a CCGT plant has the benefit of supporting the development of additional renewable power
generation, there is the risk that it may become a stranded asset owing to the implementation of aggressive climate change/action
policy goals before the end of the CCGT’s economic life. Any further increase in carbon prices, or concerns on the medium term
viability for the development of alternative baseload solutions like CCGTs would benefit the business case for the nuclear option as
it is likely to feed through into higher wholesale electricity prices.
The modelling did consider the required increase in carbon prices to make the nuclear plant viable in 2030. The level required was
a very significant 146% increase for the small nuclear plant from the IS3 carbon prices that were already projected as between $123
per tonne in 2030 and $254 per tonne in 2050. The large nuclear plant required an even more substantial increase of 245%. This
increase assumes that the modelled relationship predicted between carbon prices and wholesale prices continues to hold, which is
highly likely to be an invalid assumption with this level of change.
Conclusions
This Report has examined the commercial viability of generating electricity from a nuclear option against alternative generation
options across a number of climate change/action policy scenarios. On the basis of the assumptions provided for the input to the
NPV model, the nuclear options generally have a lower NPV than other generation options across all climate change/action policy
scenarios. Whilst the position of the nuclear options can improve with some parameter changes, the NPV for the nuclear options
still remains lower than the alternatives when assessed within the current key parameter ranges.
The results in the analysis presented in this Report are dependent on the input assumptions of the key parameters, particularly those
pertaining to discount rates, wholesale electricity prices, carbon prices and capital costs of the nuclear options. The current set of
parameter assumptions is based on estimates from well-respected sources that have been documented in this Report and that
include ranges around the central values that have been use for the sensitivity testing. However, if in the future, these key
parameters were to move outside of the expected range around the central value, then this could change the relative viability of the
nuclear generator options that have been considered.
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1 INTRODUCTION
1.1 Study objective
The key objective of this Study was to quantify the relative economic viability of integrating nuclear power generation technologies
as one of a suite of renewable and fossil fuel power generation technologies within the National Electricity Market (NEM) in the
years 2030 and 2050. In addition, the project looked at the potential greenhouse gas ameliorating effect of the integration of nuclear
power generation.
The modelling needed to undertake a comparative assessment of market entry for four generator options sited in South Australia,
which comprised:
Small nuclear plant - 285MWe (consisting of 6 x 47.5MWe small modular reactors)
Large nuclear plant - 1,125MWe AP 1000 reactor
Combined cycle gas turbines (CCGT) with carbon capture and storage (CCS) - 327MWe
CCGT - 374MWe
The analysis required an assessment of the costs and benefits of these generator options under a variety of electricity demand and
renewable generation scenarios in South Australia. The results needed to be presented as a net present value (NPV) calculation that
included sensitivity testing with a resulting range of possible outcomes
1.2 Study approach
The focus of the Study was the calculation of the costs and benefits from new generators being commissioned in South Australia in
either 2030 or 2050. The modelling was broken down into a number of defined activities that are combined to produce the economic
assessments for each of the generator options as shown in Figure 6.
Figure 6: Overview of the Study approach
A summary of each of these activities is provided below with the following Sections providing a more detailed description of the
approach and results from each area of the NPV analysis.
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1.2.1 Calculation and projection of electricity demand for South Australia
The starting point for the NPV model is the projection of demand in South Australia for 2030 and 2050. To produce these projections
the model relies on comprehensive data sets10 of load measured at half hourly (HH) intervals collected by SA Power Networks over
a period of thirteen years and not available from any other sources for the following consumer categories:
Business consumer category
Residential consumer category
Major customer category
Hot water load
These historic data sets were extrapolated forward to predict the shape of the demand in South Australia in 2030 and 2050 with
additional demand projections also included for the development of a new category of demand being that of electric vehicles (EV).
1.2.2 Projections of renewable generation in South Australia
The NPV model considers a number of renewable generation technologies that may expand/emerge to meet the South Australian
demand. This includes a combination of; photovoltaics (PV), PV paired with storage, wind, wind paired with grid storage, centralised
solar thermal plant (STP) and vehicle to grid (V2G) release of some of the energy from EV storage. Fossil fuels or nuclear then make
up the population of centralised and decentralised generation technologies (using the Australian Energy Market Operator’s (AEMO)
projections where appropriate11) to supply the South Australian system demand.
The modelling builds up the likely generation from this range of technologies based on assumed penetration rates and a prioritisation
of the dispatch schedule for different generation technologies. The modelling incorporates disaggregated renewable generation
characteristics built into HH generation shapes that mirror the demand projection and includes storage release rules that have been
devised to come close to mimicking real time demand. This provides for a comprehensive comparison of how the suite of generation
technologies is able to meet demand in each HH in South Australia in the 2030 and 2050 time horizons.
1.2.3 Generation dispatch for new South Australian generators
The main objective of the first part of the NPV model was to calculate the generation required, or that could be exported from South
Australia to the NEM from either a new CCGT or nuclear options. To do this, a load dispatch model was created to satisfy the demand
with the predicted mix of generation in South Australia at HH intervals.
The load dispatch model considered the generation of CCGT/nuclear in both a dispatch mode after all renewables (i.e. load following
or last dispatch mode) and a mode after the dispatch of some non-storable renewables (i.e. baseload or third dispatch mode). To
perform the dispatch, a hierarchy of plant dispatch was formulated and mathematical operations research techniques were
employed to constrain dispatch of plant within the boundaries of the South Australian system demand and of the interconnector
constraint for excess generation available for export into the NEM.
1.2.4 Generator cost and benefit assumptions
The NPV model allows the user to make a number of assumptions as to the demand and renewables scenarios and the economic
situations that might apply for the generator in 2030 and 2050. These assumptions include:
Dispatch options.
Carbon reduction targets and associated carbon, wholesale electricity prices and gas market prices.
10 Data provided courtesy of SA Power Networks
11 AEMO, ElectraNet 2014. Renewable energy integration in South Australia. AEMO. Available at http://goo.gl/VSLCOh (last accessed 19 June 2015).
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Plant availability.
Infrastructure requirements.
This Report details a number of assumptions that have been made in the Base scenario modelling and the alternative assumptions
that have been tested in the sensitivity and scenario analysis.
1.2.5 Viability assessment of generation options
The NPV model calculates the NPV of the costs and benefits for each option based on the selected demand/generation and
renewable parameters as well as the most likely value of each key parameter. The key parameters cover areas like capital cost,
discount rate, operating cost and efficiency levels with a clear reference to the source data for each of these parameters.
The output from the model is four NPVs for the different generation options that are broken down into key costs and the value of
electricity sales for each option.
1.2.6 Sensitivity and Monte Carlo analysis
The initial NPV calculations are based on the most likely (central) value of each of the key parameters. Given the length of the
timeframe for the NPV analysis, a number of the key inputs have a level of uncertainty associated with them and a range was
therefore applied with a low, high and most likely value included.
The NPV model assesses the impact of the change in each key parameter from the most likely value to the extremes selected within
the range. This clearly highlights the relative impact of each of the key parameters. The assessment includes a ‘Monte Carlo’ analysis,
which allows a large number of simulations to be undertaken with all the key parameters varying according to a defined probability
distribution. As a result, the refined NPV model produces a statistically more plausible NPV that includes a spread of possible NPV
outcomes.
1.2.7 Impact of alternative system scenarios
The modelling results are presented against a base case for demand, renewable generation and interconnection and a set of dispatch
options for the generator. This part of the NPV analysis examines the impact on the modelling from changing these assumptions
including an assessment of:
Medium demand growth in South Australia
High demand with low renewables in South Australia
High demand and high renewables in South Australia
Load following dispatch mode.
Social discount rate.
1.2.8 Game changing events
Whilst the sensitivity analysis and scenario selection tests the impact of changes around the most likely views on key parameters it
has not really considered the impact to the modelling of fundamental shifts or ‘game changers’ that may materially impact the
viability of the generator plants.
The Section on game changing events considers some of these events and provides examples as how the ‘game changers’ could
impact the LCOE of the generator options. The LCOE has been chosen as often the event will have a fundamental impact on the
wholesale electricity market and therefore any assessment of the impact on the NPV may not be robust.
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2 PROJECTING SOUTH AUSTRALIAN DEMAND
2.1 Historic electricity demand in South Australia
The approach applied to projecting electricity demand in South Australia builds on historic load category demand profiles along with
forecast growths provided by a computational general equilibrium (CGE) model12 to 2030 and 2050. These projections are done at
HH granularity, which allows for:
More accurate categorisation of load characteristics of:
o Peak demand values and their durations; and
o Temporal (i.e. week days or weekends) and seasonal variability.
Assessment of disaggregated consumer and renewable generation characteristics.
Load shapes to more closely mimic real time load shapes.
Greater confidence in projecting demand shapes.
‘Statistically’ more credible forecasts.
SA Power Networks has provided HH data for the South Australian grid for the following demand categories:
Business consumers from 2002/03
Residential consumers from 2002/03
Major customers from 1999/00
Hot water load from 2002/03
An example of a portion of a HH data set is shown in Figure 7.
Figure 7: HH data set for the business consumer category for 2012/1313
12 A body of work carried out for the NFCRC by Ernst & Young for the Commission.
13 HH data supplied courtesy of SA Power Networks.
SETTLEMENT
DAY
SETTLEMENT
PERIODJul-12 Aug-12 Sep-12 Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13
1 0:00:00 507082 578654 524685 465071 571891 583575 499259 574170 572810 467666 570179 553491
1 0:30:00 496440 566402 505719 460541 559030 573235 492648 564146 563246 459399 553314 537788
1 1:00:00 489695 559181 494427 457628 549925 561510 481977 557341 556914 452459 548100 522420
1 1:30:00 480606 544134 484389 447330 546619 546904 473357 549322 551643 445206 534263 510946
1 2:00:00 475167 537159 478806 441106 540922 530539 464779 538243 543261 439085 528751 500751
1 2:30:00 462583 531623 470411 437980 535984 521377 460776 535650 540686 436649 527863 492770
1 3:00:00 454213 524770 466051 433732 536863 517812 453477 536936 540353 436977 518042 486969
1 3:30:00 455391 523095 464629 432411 540299 516999 452100 542572 540232 437005 514297 485230
1 4:00:00 452867 528992 467470 436312 550543 516919 455822 552739 548939 442678 517786 480005
1 4:30:00 450683 535909 469357 436534 572372 527896 456916 573281 571621 450799 525521 481412
1 5:00:00 452535 542214 468835 442049 607709 535297 456129 603187 603235 457231 534028 481833
1 5:30:00 459366 566121 480612 452311 644801 529268 437724 663376 664978 476638 562895 494358
1 6:00:00 464939 607098 497522 454144 683427 547203 434046 688732 718997 485854 603432 507606
1 6:30:00 479257 683664 529447 453882 745953 575117 448299 743211 746891 504232 665180 537413
1 7:00:00 487486 756787 519000 469009 821885 605200 466124 800873 799238 486732 716740 561926
1 7:30:00 490673 825129 534619 499321 880889 639228 489015 860887 860637 495157 765764 574440
1 8:00:00 472987 903425 553369 509804 933716 673315 506314 893016 898397 505357 832204 574836
1 8:30:00 481935 1005868 577027 521405 963737 701609 525823 923657 927000 515781 894295 593576
1 9:00:00 489626 1066498 598456 523369 962343 716294 542829 924838 936058 525005 931372 610557
1 9:30:00 512330 1092114 612558 529623 967458 726097 562693 932636 949795 530297 947160 630920
1 10:00:00 523878 1077315 617125 530836 975759 732064 576610 945169 963996 536516 954461 635699
1 10:30:00 538215 1058719 618677 534846 970829 728182 595846 945821 970833 546716 954952 642677
1 11:00:00 557204 1045126 613910 545086 969228 727151 606394 947388 981928 545390 959151 644361
1 11:30:00 562186 1034526 608176 550989 963698 711294 612470 944628 987891 544842 962967 643671
1 12:00:00 561248 1014847 598519 550192 963002 698086 620978 947845 997304 552430 969904 635944
1 12:30:00 553519 993504 587914 552106 964556 690603 623571 940958 1005854 557686 965432 629090
1 13:00:00 555154 983315 581786 547936 961584 686354 627230 939092 1019039 555388 966033 622762
1 13:30:00 553154 968006 576419 543274 956411 680173 623325 928193 1027273 551350 964206 620539
1 14:00:00 548560 956090 568683 537917 947085 678314 625351 915077 1023827 545764 967742 614579
1 14:30:00 548420 946522 563206 538362 935491 675289 625257 893826 1010923 539620 967033 605452
1 15:00:00 544874 933832 557846 536994 909947 670179 626391 872773 991477 537704 952232 599789
1 15:30:00 543945 911346 548609 533820 879498 657431 625738 845751 970353 537979 933185 592001
1 16:00:00 541662 889198 544836 530906 849541 647759 622058 818470 941397 533487 909367 591812
BUSINESS DEMAND (kW) 2012/13
Note: This data is sourced from SA Power Networks
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A plot of the individual peak demands for these load categories is shown in the traces in Figure 8. Business peak demand (1,278MW
on 18-Feb-13) and residential peak demand (1,452MW on 18-Feb-13) each comprised about half the system peak demand. However
business customers consumed 6,277GWh of energy in the year compared to the residential customers who consumed only
3,603GWh in the year or just over half that of the business consumption. Major customer peak demand remained relatively flat for
the entire period from 1999/00 (158MW on 17-Jan-00) to 2012/13 (141MW on 17-Feb-13). Hot water load, although substantial,
did not contribute to the peak demand as it occurs during the trough in the South Australian system demand.
Figure 8: System peak demand at 16:30 EST categorised by consumer categories14
2.1.1 Characteristics of the South Australian demand categories
The HH granularity of the data sets allows for load characteristics of the different consumer categories to be compared and
contrasted. In Figure 9 a plot of the residential demand for October (spring) and February (summer) against the backdrop of the
total South Australian system demand highlights significant seasonal variability with relative consistency of demand in spring, but
significant peaks in summer, particularly on a succession of a sequence of hot days when consumers all turn on their air conditioners
at the same time.
Figure 9: Residential consumer demand profile in October (spring) and February (summer)
Business consumers, on the other hand, do not exhibit the same seasonal characteristics as the residential sector, with their load
profile being relatively uniform throughout the year. However temporal variability is pronounced with week days exhibiting peaks
and weekends virtually none as shown in the plots in Figure 10.
14 SA Power Networks, ESCOSA Demand Management Final Report, February 2015, pp12.
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Figure 10: Business consumer demand profile in October (spring) and February (summer)
The traces in Figure 11 compare the profiles of all consumer categories for a week in summer (January). It can be seen that business
load is relatively flat on weekends but assumes a cyclical pattern with peaks at about 12:30 and troughs at 01:30 during weekdays.
Residential load, on the other hand, has a consistent cyclical pattern on both weekdays and weekends but with peaks advanced
about 6 hours from the business peak. The troughs of the business and residential loads occur at about the same time. Major
customer load is consistently flat. Hot water load is strongly cyclical ramping up quickly to peaks coinciding with the troughs in the
business and residential loads and coasting down just as quickly to practically zero for the remainder of the day.
Figure 11: HH traces of consumer category loads (kW) for a week in summer (January)15
The historic consumer category proportional makeup of the system peak demand indicates that residential peak load has been
trending upwards at approximately 2.3% per annum, while business peak load has been almost flat at 0.4% per annum. Major
customer peak load and long term hot water peak load have been trending downwards at 5.3% and 16.0% per annum respectively.
These plots are shown in Figure 12.
Figure 12: Load category peak demand as a % of peak demand from 2002/03 to 2012/1316
15 ibid, pp13.
16ibid, pp13, Note: The plots are not coincident peaks (e.g. the hot water peak occurs at the system trough and the business peak occurs 3 to 5 hours prior to the residential peak).
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The residential sector contributed as much as 56% (2009/10) of the peak demand on days of extreme heat or after a succession of
hot days in a heat wave and averaged 53% in the period 2002/03 to 2012/13. The minimum of 47% occurred in 2002/03 and 2003/04.
A particular characteristic of the South Australian grid is the significant impact of the residential sector to the system’s peak demand
driven primarily by the impact of air conditioning load on a succession of hot days. Figure 13 highlights the strong correlation of the
South Australian system demand with ambient temperature.
Figure 13: Residential peak demand vs temperature index for summer periods from 2009 to 201317
The end result of these consumers’ individual characteristics is that the South Australian grid has a particularly challenging load
duration curve with almost a third of its generating plant mix being required for less than 200 hours per year. The load duration
curve in Figure 14 illustrates this vividly.
Figure 14: System load duration curve for calendar year 201318
2.1.2 Co-generation impacts
The modelling assumed that increasing levels of energy efficient cogeneration, tri-generation and district heating/cooling
technologies would lead to a commensurate decline in commercial energy consumption and residential hot water load. The
projections assumed that cogeneration and tri-generation technologies will continue to evolve and, subject to market forces, will be
deployed in the grid. Transitioning to the smarter grid facilitates this deployment of co-generation and tri-generation and it was
assumed that the current evolutionary trajectory of the smarter grid will continue unabated.
An example is shown in Figure 15 for a medium level of penetration of co-generation. It shows a reduction during a portion of the
load curve brought about by the residential demand being decreased by the equivalent of the hot water load phase shifted by
18.5 hours. However, the traces suggest that the impact of cogeneration on a day of high demand is relatively insignificant.
17 ibid, pp29.
18 ibid, pp11.
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Figure 15: Projections of residential demand profiles for a day of maximum demand without cogeneration (left) and with cogeneration (right)19
2.1.3 EV load
Historic HH load data indicates that demand from centralised sources (i.e. major customers) remains relatively unchanged, both
temporally and seasonally. However, the one major load change currently anticipated is the electrification of transport.
EV load will be dependent on the time users choose to charge their vehicles and the uptake of EVs. Key considerations to determining
the impact of EV charging are therefore (i) the rate of uptake of EVs in the community and (ii) the number of EVs being charged at
the same time; and (iii) the charge required for each individual EV.
To assess the impact, HH data of daily EV loads on the South Australian grid was sourced from a study commissioned by SA Power
Networks of the impact of EV load on low voltage (LV) transformers carried out by ISD Analytics20. In preparing their report,
ISD Analytics estimated the rate of EV uptake and the load that this would present on LV transformers21. The weekly variation of the
EV load is evident in the HH trace shown in Figure 1622.
Figure 16: HH daily and weekly trace of the EV load on the grid23
Projections of bulk demand and EV load were subject to considerable uncertainty in terms of rate of uptake as they are dependent
on so many variables (i.e. in particular technological, societal and political). The range of most likely, high and low projections are
therefore crucial to forming a view of the impact of these loads on the total demand in 2030 and 2050.
19 Legend in this Section: For the time horizon 2050: T50ev = electric vehicle demand; T50res = residential consumer category demand; T50bus = business consumer category demand; T50hw = Hot water load; T50mjc = major customer category demand.
20 SA Power Networks, ESCOSA Demand Management Final Report, February 2015, pp91-94.
21 See Appendix C for more detail.
22 EV market penetration of the passenger vehicle fleet in South Australia for varying parameter values that can be selected in the demand/generation model is presented in Appendix B.
23 This is the load profile used for scaling purposes and has been derived from the work of IDS Analytics. See Appendix C for a more detailed discussion.
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2.1.4 Demand management
The role of demand management techniques and technologies is to deal with issues of peak demand in a more cost effective manner
than just augmenting assets under constraint. Techniques such as direct load control (DLC) to remotely control load such as air
conditioning, and Time of Use (ToU) tariffs, capacity tariffs and Critical Peak Pricing (CPP) send price signals to consumers, some of
whom will modify their consumption as result of these signals. DLC has been studied extensively by SA Power Networks24. The
resulting findings were that DLC of air conditioners can reduce peak residential demand by a factor of up to 15%. With the
deployment of smart technology in the distribution network and vehicle to grid (V2G) installation in the premises it is highly probable
that EV demand will be similarly managed.
However, with the transitioning of legacy grids to smarter grids, distributed generation and storage (DGS) is becoming a feature of
the energy mix and modelling demonstrates that this can solve the issue of peak demand, the proviso being that distributed storage
(DS) is able to deal with all the exigencies placed on it by the grid (e.g. power spikes due to the intermittency of renewables and load
spikes caused by appliances such as air conditioners coming on simultaneously).
Demand management methodologies and techniques reshape the load curves and are aimed at improving the load factor. However
they have relatively little impact on the annual quantum of energy consumed in grids with South Australian characteristics. For the
purpose of the current modelling therefore, load shaping (with the exception of cogeneration) has not been addressed for the time
horizons of 2030 and 2050 as this would have an insignificant impact on the NPV of nuclear/CCGT options.
2.2 Projecting methodology
Projecting the load profiles of the consumer categories to the time horizons of 2030 and 2050 takes account of the historic
characteristics discussed above and applies a scaling factor derived from the CGE model and other published projections25 to create
HH data sets for 2030 and 2050. The summation of each of the categories at HH intervals then provides the overall South Australian
system demand. In the model, each consumer load profile can be projected individually, using the model’s input sheet shown in an
example in Figure 17.
Figure 17: Example variable inputs to the model
The variable inputs are the following for each consumer category:
Business category: (high, medium, low).
Residential category: (high, medium, low).
Major customers: (high, medium, low).
Hot water load: (high, medium, low).
Cogeneration: (yes/no).
Electric vehicle load: (percentage penetration of the light vehicle EV population).
The individual consumer category demand profiles are projected in accordance with the mathematical procedure discussed in
Appendix A and summed at each HH interval. The resulting individual load profiles and the total South Australian system demand
24 ETSA Utilities, Demand Management Program Final Report, August 2012, Case Studies 5 to 10 and 12 to 15.
25 AEMO (Australian Energy Market Operator) Detailed Summary of 2015 Electricity Forecasts, 2015 National Electricity Forecasting Report, Published: June 2015.
Business category (% pa) medium 0.73%
Residential category (% pa) medium 0.73%
Major customers (% pa) high 0.50%
Hotwater load (% pa) low -0.20%
Cogeneration (yes/no) yes -1
Electric Vehicles (EV) (scaling factor) 28% 1344
Demand
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profile are shown in Figure 18 for days of minimum demand and days of maximum demand in 2030 for the parameters chosen in
the input example above.
Figure 18: Projections of customer category demand profiles and the resulting system demand profile for days of minimum demand and maximum demand in South Australia
2.3 Projection parameters in the 2030 and 2050 models
The following sets of growth factors shown in Table 7 have been applied in the modelling for projecting demand in different scenarios
for the time horizon of 2030.
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Table 7: Source and derivation of input variables for 2030
2030 time horizon
Category Variable Value Comment
Business category
High 1.00% pa Estimate by DGA Consulting/Carisway. Projected from
2013 to 2030.
Low -0.48% pa EY’s IS3 - Strong carbon/climate action scenario26.
Projected from 2016 to 2030.
Medium 0.24% pa EY's IS2 - Moderate carbon/climate action scenario27.
Projected from 2016 to 2030.
Residential category
High 1.00% pa Estimate by DGA Consulting/Carisway.
Low -0.48% pa EY’s IS3 - Strong carbon/climate action scenario28.
Projected from 2016 to 2030.
Medium 0.24% pa EY's IS2 - Moderate carbon/climate action scenario29.
Projected from 2016 to 2030.
Major customer category
High 0.54% pa
Assumes a dry fluoride conversion and laser enrichment
facility of a total of 33MWe additional to the existing
major customer demand as informed by the NFCRC.
Projected from 2013 to 2030.
Low 0.10% pa Informed by SA Power Networks historic data discussed
in Section 2.1.1. Projected from 2013 to 2030.
Medium 0.20% pa Informed by SA Power Networks historic data discussed
in Section 2.1.1. Projected from 2013 to 2030.
Hot water load
High 0.10% pa Estimate by DGA Consulting/Carisway. Projected from
2013 to 2030.
Low -0.20% pa Value has been informed from the trend line exhibited in
SA Power Networks’ historic data sets30.
Medium -0.10% pa Value has been sourced from SA Power Networks historic
data sets31.
Co generation Yes
Assumes co generation has taken hold in the market and
is equivalent to the hot water load phase shifted by
18:30 hours (i.e. 37 HH intervals) and deducted from the
residential demand.
No No impact on the demand from co generation.
26 Figures derived from the updated IS3 - Strong climate/action policy scenario. Ernst & Young, Computational General Equilibrium (CGE) Modelling of Investment in the Nuclear Fuel Cycle.
27 Figures derived from Ernst & Young, Computational General Equilibrium (CGE) Modelling of Investment in the Nuclear Fuel Cycle.
28 ibid.
29 ibid.
30 Note: Long term downward trend of the peak in hot water load has been significantly greater than the assumed value (as much as 4% per annum between 2002/03 and 2012/13). However the hot water load does not materially impact the calculations of the NPV so that the assumed decline is considered reasonable.
31 ibid.
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2030 time horizon
Category Variable Value Comment
Electric vehicle market
penetration
5%
Value has been sourced from SA Power Networks,
ESCOSA Demand Management Final Report, February
2015, pp 9332.
9% CSIRO, Future Grid Forum, Leaving the grid scenario and
EY’s IS2 - Moderate carbon/climate policy scenario33,34. 20% EY's IS3 – Strong carbon/climate policy scenario35,36.
28% CSIRO, ClimateWorks37,38.
Similarly growth factors have been applied in the modelling for projecting demand for the time horizon of 2050 and can be viewed
at Appendix B.
2.4 System demand by 2030 and 2050
The charts in Figure 19 show the projected system load for the BIS climate change policy scenario by 2030 and 2050 for a normal
demand day, illustrating the very significant increase in EV load on the system between the two time horizons.
Figure 19: Projections of customer category profiles for a day of minimum demand in South Australia in 2030 and 2050
Similarly the charts in Figure 20 show the projected system load for the BIS scenario by 2030 and 2050 for a maximum demand day.
The charts highlight how EV load exacerbates the peak on days of high demand.
32 60,000 light electric vehicles in 2028 representing 5% of the market in South Australia. One transformer services 50 EV's with 5 transformers in the sample (c.f. IDS Analytics). Scaling factor of 240 is applied to the IDS Analytics sample data (c.f. Appendix C) yielding 555GWh of demand from EVs by 2030. The scaling factor for the 5% market penetration is used to scale up the other variable percentage penetrations.
33 This Report assumes a light electric vehicle fleet.
34 IS2 - Moderate climate/carbon policy scenario estimates EV annual demand of 976GWh by 2030. Hence market penetration of (976/555)*5% = 9%.
35 This Report assumes a light electric vehicle fleet.
36 IS3 - Strong climate/carbon policy scenario estimates EV annual demand of 2,194GWh by 2030. Hence market penetration of (2,194/555)*5% = 20%.
37 This Report assumes a light electric vehicle fleet.
38 CSIRO/ClimateWorks estimate EV annual demand of 3,108GWh by 2030. Hence market penetration of (3,108/555)*5% = 28%.
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Figure 20: Projections of customer category profiles for a day of maximum demand in South Australia in 2030 and 2050
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3 RENEWABLE GENERATION IN SOUTH AUSTRALIA
3.1 General methodology
3.1.1 Current electricity generating mix in South Australia
The process of understanding the future electricity generation mix supplying the South Australian grid began with a review of the
historic HH data sets for the total South Australian system demand, PV generation over the entire South Australian grid and wind
generation at two locations in South Australia.
The plots in Figure 21 show PV generation against the background of the South Australian system demand for a spring and a summer
month in 2012/13. As a proportion of the total system demand, PV contributed relatively little, although the total amount of
generation was significant and is growing rapidly.
Figure 21: HH monthly trace of the PV output against the background of the South Australian system demand
In contrast to PV generation, wind generation contributed significantly to the total South Australian system demand in 2012/13. In
certain instances in a low demand month it met, and even exceeded, the entire system demand as shown in Figures 22 for the same
spring and summer month as the PV output.
Figure 22: HH monthly trace of wind generation against the background of the South Australian system demand
Deducting the PV generation and the wind generation from the total South Australian system demand in the same spring and
summer months results in the plots shown in Figure 23. These plots are particularly illuminating in illustrating the characteristics of
the generation mix in South Australia for they highlight that in a low demand month (i.e. spring) baseload fossil fuel plant (i.e. coal
fired) cannot operate in baseload mode when just supplying South Australia, albeit there being some improvement in a high demand
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month (i.e. summer). As South Australia is connected to the NEM via interconnectors some of the generation from coal fired plants,
subject to market conditions, had the capacity of being exported to the NEM39.
Figure 23: HH monthly trace of fossil fuel generation against the background of the systems requirement
3.1.2 Future generation mix
The generation mix available by 2030/2050 will change materially from the current fleet. A significant increase in wind and PV is
anticipated particularly with advancements in storage creating increased flexibility for these options. To assess the future generation
mix, a number of separate combinations of renewable/storage technologies were created that can be combined in different ways
to meet the South Australian system demand. The technologies considered were:
Generation from native (i.e. without storage) PV.
PV paired with storage.
Native (i.e. without storage) wind generation.
Wind paired with storage.
V2G release from EV storage.
Centralised STP.
Nuclear power.
CCGT or CCGT with CCS as alternatives to nuclear power.
Fossil fuels40.
An overview of the renewable technologies is provided below.
3.2 Renewable generation output
3.2.1 Generation from photovoltaics
Analysis of the PV data sets shows that maximum day PV generation in the South Australian grid doubled from 01-Mar-10 to 05-Mar-
11; quadrupled from 06-Mar-11 to 06-Mar-12; and doubled again from 07-Mar-12 to 28-Feb-13 indicating an almost exponential
increase in installed PV capacity. A HH plot of the maximum day gross PV output profile shown in Figure 24 confirms the observation
of the growth in PV penetration and the seasonal variability of PV output. A more detailed discussion of the daily and seasonal
variability of PV output is provided at Appendix D.
39 This will cease after about March 2016 - “Alinta Energy has today informed its workers that it will close the Leigh Creek coal mine on November 17, and the Port Augusta power stations around March 31 next year”, Cooper Pedy Regional Times, Leigh Creek Coal Mine & Port Augusta Power Station Closure Dates Confirmed, Posted October 7 2015.
40 ibid, excluding coal fired plants.
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Figure 24: HH maximum day profiles of gross PV output from 2010 to 2013 and the average monthly gross PV output profiles in 2009/10
Currently this gross PV output represents only a small proportion of the residential peak demand. However the recent rapid
proliferation of PVs in South Australia is expected to continue to possible saturation by 202841,42,43. This scaled up44 gross PV output
relative to the residential demand in 2012/13 is shown in the HH traces in Figure 25 for one week in winter (July) and in summer
(February).
Figure 25: HH traces of residential demand and scaled up gross PV output for one week in winter (July) and in summer (February)
The traces clearly highlight that although the peak of the scaled up gross PV output is not coincident with the peak of the residential
demand, it comes close to meeting, or even exceeding, the residential peak demand in summer but not in winter.
3.2.2 Wind generation
Analysis of the wind HH data sets for a twelve month period from Jun-13 to May-14 at two locations in the State informs on the
temporal characteristics of wind generation45 with:
August likely to have the greatest wind intensity but with the lowest probability of occurrence.
June, March, April and May having the least wind intensity with the highest probability of occurrence.
41 ibid, SA Power Networks “Future Operating Model, 2013 -2028”.
42 AEMO, Rooftop PV Information Paper, National Electricity Forecasting, 2012, pp21. “The average system size per dwelling is 3.5 kW…and
the uptake even at saturation is 75%”
43 SA Power Networks, Submission to the AEMC - Integration of Energy Storage, Regulatory Implications Discussion Paper, 5 November 2015, pp2.
44 The scaling factor is related to the annual peak demand of the residential consumer category for 2012 and to the annual peak demand of the residential consumer category plus the annual peak demand of the business consumer category for the time horizons of 2030 and 2050. The demand/generation model automatically generates the appropriate scaling factor based on the parameters chosen for residential demand growth, business demand growth, PV penetration and PV storage and the time horizon under investigation.
45 See Appendix D for a more detailed discussion of the seasonal and temporal characteristics of wind generation.
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Other months having significant wind intensity but with variable probability of occurrence.
According to AEMO, South Australia has the largest installed level of wind generation46 in Australia with a total installed wind
generation capacity of 1,473MW47 in 2013/14. In their report, AEMO stated that in 2012/13, maximum 5 minute wind generation in
South Australia was 1,073MW and the maximum ratio of wind to local demand was 88% with wind generation being relatively
consistent on a typical hourly basis when averaged across the whole of the State. The rate of increase in wind generation in South
Australia reported by AEMO is shown in Table 8.
Table 8: Installed capacity of wind generation in South Australia
Year Installed capacity
(MW)
Maximum 5 minute
generation (MW)
2005/06 389 263
2006/07 548 327
2007/08 742 554
2008/09 870 736
2009/10 870 769
2010/11 1,152 1,060
2011/12 1,203 1,081
2012/13 1,203 1,073
2013/14 1,473 1,31448
To analyse the impact of wind generation in the South Australian grid, HH data from two wind farms for the period from Jun-13 to
May-14 was used. Analysis of the data from both locations highlighted a seasonal variation in wind intensity, but not frequency, as
shown below in the HH traces in Figure 26 of wind generation for the months of October and February.
Figure 26: HH traces of wind generation output for one week in Oct-13 and Feb-14 summed at location A and location B
3.2.3 Pairing PV with battery packs
The proliferation of PV installations in South Australia in recent years has come about as a consequence of many factors, but
principally they relate to: the rise in unit electricity costs; the rapid fall in the cost of PV installations; and the generous feed in tariffs
46 AEMO (Australian Energy Market Operator), South Australian Wind Study Report, 2013, http://www.google.com.au/url?url=http://www.aemo.com.au/Electricity/Planning/South-Australian-Advisory-Functions/~/media/Files/Other/planning/South_Australian_Wind_Study_Report_2013_2.ashx&rct=j&frm=1&q=&esrc=s&sa=U&ei=P-qwU5_uKYbxPMWugeAD&ved=0CCAQFjAC&usg=AFQjCNGatm9tRaI5QxlmwI54fW21E78aPA
47 1,203MW to the end of 2013 as reported by AEMO plus 270MW commissioned at Snowtown Stage 2 in 2014/15.
48 Estimated figure using published AEMO data.
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that had prevailed. Paradoxically this has had little impact on peak demand and thus creates a so called ‘death spiral’ of unit cost
rises and energy throughput reductions. PV installations continue to make up a greater share of the generation mix, and their
intermittency can create significant grid instability causing any excess power that is generated to be spilt in instances where it cannot
be exported. However, using battery storage to store and release power can mitigate this instability49. The smarter grid is able to
readily accommodate both storage of and release of power in line with predicted demand profiles thus smoothing the swings in the
load being supplied by the generation fleet.
With the transition of the legacy grid to a smart grid, consumers are now able to interact with their appliances and employ
opportunistic or pre planned cost reduction strategies and then observe how these strategies impact on their bill. This may be a
temporary phenomenon as with the rapidly emerging internet of things (IoT) human interaction may become a thing of the past.
Data generated by the IoT will include the house load profile, as well as the electronic signatures of all connected appliances. A load
management system would manage the household load in accordance with targeted cost savings measures, and the electricity
production profile of PVs and other sources of distributed generation available to the house as well as the state of charge of
distributed storage. The load management system would then apply algorithms that signal dumb demand response enabling devices
(DRED) to either store power or release power in accordance with optimal load shaping criteria.
Under a scenario with the en masse adoption of this IoT technology, as has been the case with personal computers and smart
phones, the electricity distribution system is transformed into a neural net that reacts in real time to consumer behaviour, both
actual and anticipated. A tentative first step in this direction was announced by SA Power Networks, which is trialling PVs paired
with batteries in association with Enphase Energy, a NASDAC listed company, to see how home storage can help the electricity
system. Paul Roberts, a spokesman for SA Power Networks told the media “We agreed to be involved in the trial as part of our efforts
to validate potential benefits of battery storage and energy management systems.”50
Clearly, it is not possible to simulate this environment perfectly. However, for the purpose of generation dispatch, a very close
approximation of optimal power release from storage for PV paired with batteries is discussed in detail in Appendix E.
3.2.4 Pairing wind generation with grid storage
A significant benefit of distributed storage is that it creates firm capacity in the grid, which allows other plant to operate more
efficiently. Advances in grid energy storage technology are continuing51 and it may soon be economically feasible to store sufficient
energy and meet the ramp up and coast down rates of intermittent renewable energy so that energy storage and release can be
controlled to remain within acceptable tolerances. Without this technology, if wind output drops suddenly, a conventional plant is
not able to ramp up its output quickly enough to avoid an outage. In such an event the amount of renewable energy output that can
be relied on may be greatly diminished. Distributed storage allows renewable energy to taper off rather than falling sharply allowing
other sources of generation to come on stream at a safe rate.
To model the power released from grid storage, it was assumed that native wind generated output feeding into South Australian
grid would be stored for a period of time in grid storage and released in such a way that it followed the total system demand including
EV load. This is discussed more fully in Appendix E.
49 Anderson, Roger N, Boulanger, Albert, Powel, Warren B, Scott, Warren, Adaptive Stochastic Control for the Smart Grid, Proceedings of the IEEE, Vol. 99, No. 6, June 2011, http://alliance.columbia.edu/files/newalliance/content/ASC%20for%20the%20Smart%20Grid.pdf
50 Christopher Russell, The Advertiser, ‘US entity to trial solar storage with SA Power’ October 7 2015, http://www.adelaidenow.com.au/business/us-entity-to-trial-solar-storage-with-sa-power/news-story/8be62d7fb043dbced1e1df5f64cccdc9.
51 Chris Griffith, ‘Moore power to Monash battery’, The Australian Business Review, Tuesday, July 28, 2015, pp 23.
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3.2.5 Centralised solar thermal plants
Solar thermal plants (STP) are installations of large arrays of solar panels that collect heat from the sun's rays to heat a fluid. The
steam produced from the heated fluid uses a conventional generator to generate electricity similar to the way fossil fuel plants work.
Such installations can then be considered either as load following or baseload generators.
These plants need not be paired with grid storage and can be considered as conventional centralised plants for dispatch into the
grid.
3.3 Interconnector capacity
The South Australian electricity network is connected to the NEM via a regulated interconnector. “The NEM facilitates wholesale
power exchange between electricity producers and consumers through a pooled system, where output from all generators is
combined and scheduled in real-time to meet consumer demand52.” Load flows between South Australia and the NEM are a
continuous occurrence and this is administered by the Australian Energy Market Operator (AEMO). The current capacity of the
Heywood interconnector connecting South Australia and Victoria is to be increased to 650MW by 2016 and for the purpose of the
modelling being conducted herein is considered to be a high constraint. Relaxing this constraint (i.e. increasing the interconnector
capacity) is important in terms of exporting both surplus renewable energy and surplus energy derived from nuclear options and
displacing fossil fuels in the NEM.
3.4 Scenario selection for renewable generation
The previous sets of analyses have provided the demand and renewable generation profiles and volumes for South Australia along
with the interconnection capacity constraints to the NEM. A key element of these consumer profiles is their dependence on a number
of assumptions that users may wish to modify such as the level of cogeneration, increase in energy demand, level of EV penetration
and so forth.
Input parameters53 for the demand/generation model component have been informed by published and unpublished reports and
documents referred to in this Report, discussions with subject matter experts, inputs from other advisers to the Commission and
agreement with the Commission itself. The list of available parameter options and sources is provided in Table 9 for the 2030 time
horizon. For the 2050 time horizon, the parameters can be viewed at Appendix B.
52 http://www.electranet.com.au/network/national-electricity-market/
53 The words ‘parameter’ and ‘variable’ are frequently used interchangeably.
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Table 9: Derivation and source of key parameters for 2030
2030 time horizon
Category Variable Value Comment
Photovoltaics
Business category
High 80%
Estimate by DGA Consulting/Carisway assumes saturation
of PV installation in the commercial sector in 2030 as per
the residential sector.
Low 11% AEMO Detailed Summary of Electricity Forecasts, June
2015, pp53, Table 2454.
Medium 16% AEMO Detailed Summary of Electricity Forecasts, June
2015, pp53, Table 2455.
Photovoltaics paired with storage
PV paired with battery
storage
High 80%
Estimate based on breakthrough battery technologies
such as those being worked on by Tesla56 for EVs that can
be transposed to in the home battery packs57.
Low 20%
Estimate based on little progress in the R&D of battery
storage technology and informed by AEMO, Emerging
Technologies Information Paper, June 2015.
Medium 30% Khalilpour R and Vassallo A 2015. Leaving the grid: An
ambition or real choice. Energy Policy, 82 p207-2158.
Wind generation
Wind paired with grid
storage
High 60%
Estimate by DGA Consulting/Carisway assumes rapid
uptake of grid storage technologies to deal with issues of
grid instability caused by intermittent wind generation.
Low 0%
EY’ IS3 - Strong climate action policy scenario assumes no
grid storage to be present in the network electricity
system.
Medium 40%
Low-cost and highly scalable grid storage systems are
currently being trialed that can be scaled up from 500kW
to large scale applications in the hundreds of
megawatts59,60.
54 Estimate based on small commercial PV installation in 2034/35 at 866MW (730MW when scaled back to 2030) and residential PV installation at 1,738MW. Hence PV installed capacity of commercial approximates to 50% of residential. Assuming 25% penetration of PV in the small commercial sector as agreed with the Commission = (730/1,738)*.25 = 11% penetration.
55 Estimate based on small commercial PV energy consumption in 2034/35 at 1,177GWh. Medium energy consumption of the commercial sector in 2030 is 7,536GWh. Hence assume penetration is = (1,177/7,536) = 16%.
56 https://www.teslamotors.com/en_AU/powerwall
57 Also informed by CSIRO, Future energy storage trends, An assessment of the economic viability, potential uptake and impacts of electrical energy storage on the NEM 2015-2035, September 2015.
58 http://www.sciencedirect.com/science/article/pii/S0301421515001111
59 Hughes Public Relations, News Release, Sand May Provide Energy Storage Solution, Adelaide firm bolstered by grant to commercialise concept, October 13, 2015.
60 Luke Griffiths, The Advertiser, ‘Hot on the trail to make a mark’, October 13 2015, pp23 & 25 – “Another business to have received AC funding is Latent Heat Storage (LHS), which has patented a low-cost and highly scalable thermal energy storage system (TESS) based on the latent heat properties of silicon derived from sand. It differentiates from competing technologies because of its scalability, from small scale 500kW applications through to large scale applications in the hundreds of megawatts.
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2030 time horizon
Category Variable Value Comment
Wind installed capacity
Wind installed capacity
High 4,421MW AEMO, SA Fuel and Technology Report61.
Low 1,314MW
Estimated figure derived from 1,203MW to the end of
2013 as reported by AEMO plus 270MW commissioned at
Snowtown Stage 2 in 2014/1562.
Medium 3,000MW
Sourced from Dr Mark Diesendorf, Institute of
Environmental Studies, UNSW Australia, Response to
questions posed in the Nuclear Fuel Cycle Royal
Commission Issues Paper 3: Electricity Generation from
Nuclear Fuels.
Solar Thermal Plant (STP)
Installed capacity
High 450MW
Estimate based on Repowering Port Augusta, A blueprint
to replace Northern and Playford B coal power stations
with renewable energy63.
Low 0MW No STP plant.
Medium 280MW AEMO, SA Fuel and Technology Report (2015)64.
Nuclear plant
Switch Yes 1 Nuclear installation.
No 0 CCGT or CCGT with CCS installation.
Nuclear installation Low 285MWe
WSP | PB cost estimate based on nuScale 6 x 47.5MWe
reactors.
High 1,125MWe WSP | PB cost estimate based on AP1000 reactor.
Interconnector constraint
Installed capacity
High 650MW Heywood Capacity upgrade by 2016.
Low 2,000MW NFCRC relaxed constraint.
Medium 1,180MW
Sourced from Dr Mark Diesendorf, Institute of
Environmental Studies, UNSW Australia, Response to
questions posed in the Nuclear Fuel Cycle Royal
Commission Issues Paper 3: Electricity Generation from
Nuclear Fuels.
61 The report proposes the potential for an additional 3,107MWe of wind generation projects across South Australia, http://bit.ly/1LqrPpv pp9.
62 1,314MW is an estimate of the maximum 5 minute generation (c.f. AEMO web site for conversion data) for an installed capacity of 1,473MW.
63 Phase 1 envisages 220MW and Phase 2 envisages 540MW. http://media.bze.org.au/Repowering_PortAugusta.pdf
64 The report notes that Arizona’s largest public utility recently commissioned a solar array with a maximum output of 280MW with 6 hours of molten salt storage.
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2030 time horizon
Category Variable Value Comment
Vehicle to Grid (V2G)
Percentage of electric
vehicles with V2G
installations
High 70% Discussions with NFCRC.
Low 0% Current state in 2015.
Medium 25% Estimate by DGA Consulting/Carisway.
3.5 System demand compared with projected renewable generation
The charts in the Figures below show examples of the central estimate of the South Australian system demand against a projection
of the renewable generation in 2030 and 2050 for selected days. These charts are done without nuclear and conventional generation
to provide an indication of the generation required from non-renewable sources in 2030 and 2050.
The graphs in Figures 27, 28 and 29 demonstrate that for some HH periods on a day of minimum demand the level of renewable
generation will be greater than the projected demand so that surplus renewable generation will need to be exported. In other HH
periods there is a small amount of energy that needs to be supplied by non-renewable sources in South Australia or imported from
the NEM via the interconnector.
Figure 27: Projections of renewable generation meeting demand in SA for a day of minimum demand in South Australia in 2030 and 205065
Figure 28: Projections of renewable generation exported to the NEM for a day of minimum demand in South Australia in 2030 and 2050
65 Legend in this and the following Section: foss = fossil fuel plant; stp = solar thermal plant; evs = storage release from electric vehicles with V2G (vehicle to grid) installations; winds = wind generation paired with grid storage; pvs = photovoltaics paired with battery storage; nuc = nuclear plant; windo = wind generation without storage; pvo = photovoltaics without storage.
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Figure 29: Projections of non-renewable generation meeting demand in SA for a day of minimum demand in South Australia in 2030 and 2050
On a day of maximum demand the graphs in Figures 30 and 31 show that the level of renewable generation will not be sufficient to
meet the projected demand so that power will have to be sourced from non-renewable sources or imported. In other HH periods
there is a small amount of power that could be exported. However, these days are relatively rare occurrences.
Figure 30: Projections of renewable generation meeting demand in SA for a day of maximum demand in South Australia in 2030 and 2050
Figure 31: Projections of renewable generation exported to the NEM for a day of maximum demand in South Australia in 2030 and 2050
The graphs in Figure 32 vividly highlight that on a maximum demand day in South Australia, such as that following a sequence of
days of extreme heat as occurred between 14-Dec-15 to 19-Dec-15, there is likely to be a significant requirement for power to be
imported from the NEM or to be provided by non-renewable generation. These days occur rarely throughout the year thus further
exacerbating the problems associated with peak demand in the South Australian system.
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Figure 32: Projections of non-renewable generation meeting demand in SA for a day of maximum demand in South Australia in 2030 and 2050
Inspection of the above graphs shows that there is relatively little change in the non-renewable generation profile, both in terms of
power and energy to be supplied to meet the South Australian demand over the 2030 to 2050 time trajectory for a typical minimum
and maximum demand day.
The graphs, however, highlight the need for enhanced interconnector capacity as the peaks can even be in excess of 2.0GW assumed
in this Report as the low interconnector constraint.
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4 GENERATION AND DISPATCH IN SOUTH AUSTRALIA
4.1 Generating capacity of new plant
For the modelling the generator units selected were those that represented the likely possible size of alternative options that could
be commissioned in South Australia. The net capacity and derivation for each plant is summarised in Table 10. Within the modelling
the majority of costs and benefits are derived using inputs that are provided as $/KW or $/KWh. This means that the capacity
selected should not impact whether the NPV is positive or negative with the one exception being the pre-construction costs, which
are included as a set amount per option.
Table 10: Net capacity of generation plant
Plant Capacity
(MWe sent out)
Source
CCGT with CCS 327 Table 3.2.1 in AETA 2012 Report (based on gross capacity of 361MWe).
Small modular reactors 285 WSP|PB (6 x 47.5 MWe SMR example plants).
Large nuclear reactor 1,125 WSP|PB cost estimates (Westinghouse AP1000 model).
CCGT 374 Table 3.2.1 in AETA 2012 Report (based on gross capacity of 386MWe).
The generation output of the plant is calculated for each year allowing for the availability of the plant. All output calculations are
made net of the auxiliary load.
4.2 Hierarchy of plant dispatch
The period up until 2030 is likely to see continued development of renewable technology, storage and smart grid applications that
optimise how storage is released, which provides a more optimal power dispatch system. These developments will impact on how
large dispatchable generators can operate. Nuclear power with low variable costs will prefer to be fully dispatched when available,
but the ability to achieve this will be dependent on the level of renewable generation, storage levels (and how this is optimised),
demand in South Australia and the level of interconnector capacity. Two scenarios of plant dispatch of the nuclear options are
therefore considered; as follows:
(i) nuclear dispatched after all other renewables (last dispatch or load following mode); and
(ii) nuclear dispatched after native renewables (third dispatch or baseload mode and once the nuclear plant has been
built, the ‘third’ dispatch mode should be in line with the relative variable costs of the nuclear plant).
These options are detailed in Table 11. The same level of prioritisation for dispatch has also been applied to the CCGT options to
allow a fair comparison between the technologies.
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Table 11: Ordering of generator plant dispatch
Rank Power dispatch (i)
(nuclear dispatched last - load following)
Power dispatch (ii)
(nuclear dispatched third - baseload)
1 PV only PV only
2 PV paired with DS that follows the system load profile Wind only
3 Wind only Nuclear or the CCGT alternative
4 Wind paired with grid storage that follows the system
load profile
PV paired with DS that follows the system load profile
5 V2G EV release that follows the system load profile Wind paired with grid storage that follows the system
load profile
6 Solar Thermal Plant V2G EV release that follows the system load profile.
7 Nuclear or the CCGT alternative STP
8 Fossil fuels that are required to meet any generation
shortfall in the South Australian grid
Fossil fuels that are required to meet any generation
shortfall in the South Australian grid
The objective function66, whether the nuclear plant is operating in last dispatch mode or third dispatch mode, is to maximise the
power output of the nuclear plant at each HH interval subject to relevant constraints. These constraints are that the cumulative
power generated at each HH interval cannot exceed the system demand for power supplied in the South Australian grid and cannot
exceed the interconnector capacity for power supplied into the NEM. Sub constraints have to do with the maximum power able to
be generated by each of the generators in the generation mix, which are determined by the technology inputs chosen for the
scenario being examined. Further constraints are that each of the generators in the generation mix for each HH interval must not
violate the system demand and interconnector capacity boundary conditions.
An assumption of the modelling is that energy storage technology in the time horizons of 2030 and 2050 is assumed to be a mature
technology capable of storing and releasing power in accordance with a proportion of the installed distributed generation and
storage embedded in the grid67,68. This assumption is particularly significant as the modelling indicates that if PV and wind generation
expand unabated, without any form of storage, then the grid is likely to experience periods of instability when there is no alternative
but to spill generation. This aspect is discussed in more detail when considering the feasibility of an upgraded interconnector in
Section 4.7.
A final limitation of the dispatch schedule is that the model does not optimise plant dispatch on the basis of the individual generating
source’s marginal cost or position in the contract market. The model groups renewable generators and allocates the generation
output from the plant type according to the hierarchy in Table 11. The only individual generators that have a dispatch schedule are
the four generators options being assessed as part of this Study.
4.3 Hierarchy of generation supply for South Australian generators
The modelling has assumed the following hierarchy of markets for South Australian generators with further details below:
Supplying electricity to meet local demand in South Australia.
66 In linear programming, the problem to be solved is defined in terms of maximising or minimising a linear function that is subject to a set of linear constraints that can take the form of equalities or inequalities. The linear function to be maximised or minimised is the objective function. See Appendix F for a detailed mathematical formulation.
67 CSIRO Energy Flagship, Electrical Energy Storage, Technology Overview and Applications, Prepared for the Australian Energy Market Commission, 8 July 2015.
68 CSIRO Energy, Future energy storage trends, An assessment of the economic viability, potential uptake and impacts of electrical energy storage on the NEM 2015–2035, Report prepared for the Australian Energy Market Commission, Report No. EP155039, September 2015.
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Exporting energy via the interconnectors.
Utilisation of excess energy, not used to meet the South Australian demand or exported to the NEM, for other purposes.
4.3.1 Generation supplying South Australian demand
The first element of revenue from a South Australian generator is meeting the demand for generation in South Australia.
The analysis calculates the HH power provided by each generator under consideration to meet the South Australian demand. The
best case scenario is that sufficient demand exists in the South Australian grid to allow the generator to operate in baseload mode.
However, particularly for the larger nuclear generator, this is not always possible and surplus generation needs to be fed into the
NEM, sold for a different purpose, or the plant will need to go into cycling mode.
4.3.2 Exporting energy via the Interconnectors
Where the generation capacity exceeds the South Australian demand there is the option to sell the excess capacity beyond that
required for South Australia into the NEM through the interconnectors. This depends on the interconnector capacity available and
is one of the variables tested in the sensitivity analysis of the NPV modelling. In each HH the modelling calculates the level of power
that the generator exports via the interconnector in both last dispatch and third dispatch modes.
At times, particularly on low demand days, the modelling shows that renewables generation is sufficient to meet almost the entire
South Australian system demand as well as supplying surplus generation via the interconnector. This restricts the available
interconnector capacity that could be used by the new CGGT/nuclear generation when they are operating in last dispatch mode. An
export scenario is illustrated in Figures 33 (without interconnector constraint) and Figure 34 (with interconnector constraint). The
impact of the interconnector constraint on the amount of power that could be exported to the NEM is explored in Section 4.7 of this
Report.
Figure 33: Power available for export on a minimum demand and maximum demand day unrestricted by the interconnector with nuclear operating in last dispatch mode
Figure 34: Power available for export on a minimum demand and maximum demand day restricted by the interconnector capacity with nuclear operating in last dispatch mode
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4.3.3 Utilisation of excess energy for other purposes
There are a number of potential uses for excess energy that can be explored if a nuclear generator has an insufficient market for its
planned capacity. This includes:
Storing energy in the form of heat – This is early stage technology (prototype plant to become operational in early 2016)
that stores excess generation from sources such as renewables in the form of heat and can thereafter release this stored
energy as not only electricity but also as a form of low grade district heating and cooling or high grade water desalination
heat. This is in essence a co-generator that mops up surplus wind energy, but could equally be applied to other forms of
generation with a low variable cost such as nuclear generation.
Producing fuel from power – This could be in the form of large scale electrolysis to produce hydrogen fuel. It would have
a number of potential uses including the production of methane for sales in the gas network or LNG.
These additional uses for excess energy are still speculative and may not develop in the medium term. They are also likely to require
a constant output. Given this uncertainty these options have not been assessed within this NPV analysis.
4.4 Example of generation dispatch
The charts in the Figures below provide typical HH traces of generation dispatch with a large nuclear option in last dispatch mode
for the time horizon of 2030 for a minimum demand day and a maximum demand day. Areas of the charts highlighted in dark blue
represent the selected technologies being dispatched to meet the system demand in South Australia. Figure 35 illustrates the
selection of technologies in the demand/generation model for the generation dispatch that this example illustrates.
Figure 35: Technology variables for a scenario in 2030
The HH traces in Figure 36 highlight the generation dispatch of PV generation without storage for the time horizon of 2030 for a
minimum demand day (i.e. spring) and a maximum demand day (i.e. summer). For the technology parameters chosen in this
example, this form of generation (dark blue area) represents only a small proportion of the South Australian system demand in both
minimum and maximum demand days.
Business category penetration (%) medium 16%
PV paired with storage (%) high 80%
Wind paired with storage (%) medium 40%
Wind installed capacity (MW) medium 3000
Installed capacity (MW) medium 280
Nuclear Plant yes 1
CCGT Plant no 0
Installed capacity (MW) high 1125
Interconnector Constraint (MW) low 2000
Percentage of EV's with V2G medium 40%
Nuclear Plant
Wind
Solar Thermal Plant (STP)
Technology
Photovoltaics (PV)
Interconnector Constraint
Vehicle to Grid (V2G)
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>
Figure 36: HH traces of PV generation to meet the South Australian system demand on a minimum demand day and maximum demand day with nuclear in last dispatch mode
PV paired with storage generation (dark blue area) for the time horizon of 2030 for a minimum demand day (i.e. spring) and a
maximum demand day (i.e. summer) is shown in the plots in Figure 37. This form of generation is significant in meeting the South
Australian system demand in both minimum and maximum demand days. Note that the impact of storage makes PV power available
for the entire day as opposed to just during the sunlight hours of the day that is the case for native (i.e. without battery storage) PV
generation.
Figure 37: HH traces of PV paired with storage to meet the South Australian system demand on a minimum demand day and maximum demand day with nuclear in last dispatch mode
Wind generation alone (dark blue area) for the time horizon of 2030 for a minimum demand day (i.e. spring) and a maximum demand
day (i.e. summer) is dispatched next as shown in Figure 38. This form of generation could meet 32% of the total annual South
Australian system demand. However its availability is variable and is dependent on the intermittency of the wind.
Figure 38: HH traces of wind generation alone to meet the South Australian system demand on a minimum demand day and maximum demand day with nuclear in last dispatch mode
Wind generation paired with grid storage (dark blue area - also termed ‘bulk storage’ to differentiate it from battery storage, which
is also referred to as ‘behind the meter’ storage) for the time horizon of 2030 for a minimum demand day (i.e. spring) and a maximum
demand day (i.e. summer) is dispatched after native wind generation (i.e. wind generation without bulk storage). The impact of grid
storage, as with PV paired with battery storage, is to smooth out the intermittency of renewables generation as shown in Figure 39
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and could supply of the order of 14% of the total annual South Australian system demand. Therefore wind in native form and paired
with grid storage can potentially supply 56% of South Australia’s total annual system demand in this example. Nonetheless there are
still periods wherein this form of energy falls short of meeting the South Australian system demand. In the example presented herein
this is the case for both a minimum and maximum demand day shown in the plots in Figure 39 meaning that a certain level of open
cycle gas, combined cycle gas, electricity imports from Victoria or nuclear capacity will be required.
Figure 39: HH traces of wind generation paired with storage to meet the South Australian system demand on a minimum demand day and maximum demand day with nuclear in last
dispatch mode
V2G storage release from EVs (dark blue area) for the time horizon of 2030 for a minimum demand day (i.e. spring) and a maximum
demand day (i.e. summer) contributes only marginally to the South Australian system demand. However this situation could change
materially as the penetration of EVs in the market increases and V2G installations become ubiquitous in the grid. The impact of V2G
storage, as with PV paired with battery storage, is to smooth out the intermittency of renewables generation and is shown in Figure
40.
Figure 40: HH traces of V2G release from EV storage to meet the South Australian system demand on a minimum demand day and maximum demand day with nuclear in last dispatch
mode
In a minimum demand day (i.e. spring) centralised STP generation (dark blue area) is in excess of the South Australian system demand
but is almost fully utilised on a maximum demand day as shown in the plots in Figure 41. Surplus energy from the centralised STP in
a minimum demand day is thus available for export via the interconnectors.
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:
Figure 41: HH traces of STP to meet the South Australian system demand on a minimum demand day and maximum demand day with nuclear in last dispatch mode
Nuclear for the time horizon of 2030 for a minimum demand day (i.e. spring) and a maximum demand day (i.e. summer) is in excess
(dark blue area) of the South Australian system demand in a low demand day and on a maximum demand day as shown in the plots
in Figure 42. Surplus energy from nuclear is thus available for export via the interconnectors.
Figure 42: HH traces of nuclear in last dispatch mode to meet the South Australian system demand on a minimum demand day and maximum demand day
Surplus power generation by the nuclear generator (dark blue area) is then exported to the NEM subject to the interconnector
constraining capacity and is illustrated in the HH traces in Figure 43 for a minimum demand and maximum demand day.
Figure 43: HH traces of nuclear exported to the NEM on a minimum demand day and maximum demand day when operating in last dispatch mode
The plots in Figures 44 and 45 contrast the nuclear generator that is dispatched in third dispatch mode (dark blue area) with dispatch
in last dispatch mode (i.e. Figures 42 and 43) for a minimum demand day (i.e. spring) and a maximum demand day (i.e. summer)
that show nuclear being fully utilised in satisfying the South Australian system demand for both a minimum demand day and a
maximum demand day.
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Figure 44: HH traces nuclear in third dispatch mode to meet the South Australian system demand on a minimum demand day and maximum demand day
Figure 45: HH traces of nuclear exported to the NEM on a minimum demand day and maximum demand day when operating in third dispatch mode
The annual energy outputs for the nuclear and CCGT options are produced by the demand/generation model for the example being
illustrated in this Section for input to the NPV component of the model. These outputs populate the Table shown in Figure 46 and
are generated every time a variable is altered.
Figure 46: Energy sent out (GWh/a) for input to the NPV component of the model
4.5 Output for the operation of nuclear/CCGT option
Operation of the nuclear option for a low demand month (i.e. spring) is shown in the plots in Figure 47 for the plant operating in last
dispatch mode (left Figure) and third dispatch mode (right Figure). It is clearly evident that in a last dispatch mode the majority of
the energy generated by the nuclear generator is exported, but subjected to the interconnector constraint, whilst in third dispatch
mode the energy mostly satisfies the South Australian system demand.
SA NEM SA NEM
CCGT with CCS (327MW) 1438 1329 2585 189
Small Nuclear (285MW) 1278 1135 2265 153
Large Nuclear (1125MW) 3039 5558 7578 1966
CCGT (374MW) 1608 1555 2939 234
Energy Sent Out (GWh) in 2030
Last Dispatch Mode Third Dispatch Mode
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Figure 47: Nuclear plant operation in a low demand month in last dispatch (i.e. left) and third dispatch (i.e. right) modes
The percentage makeup of energy sent out by a 1,125MWe nuclear plant into the South Australian grid and the NEM operating in
last dispatch and third dispatch modes is illustrated in the doughnut charts in Figure 48. It is clear that the hierarchy of dispatch has
important implications for the nuclear plant operation in terms of the annual energy it can supply to the South Australian grid and
the amount of annual energy it exports to the eastern states of the NEM subject to the interconnector constraints.
Figure 48: Percentage of nuclear energy supplied to the South Australian grid and exported in the NEM in a low demand month in last dispatch and third dispatch modes
The utilisation of a 1,125MWe nuclear plant operating in last dispatch and third dispatch modes is illustrated in the doughnut charts
in Figure 49. Operating in last dispatch mode in the scenario being investigated, the plant would experience a total downtime slightly
greater than when operating in third dispatch mode. In the first year of operation with no refuelling required the large nuclear
generator’s operation and maintenance only accounts for approximately 3% of the plant’s downtime measured on an annual basis.
Any downtime in excess of this figure means that there is no market for the generator’s output during that period. This is a
consequence of the interconnector constraint, which limits the market for energy exports. Operating in third dispatch mode, the
nuclear plant is fully utilised.
Figure 49: Utilisation of a 1,125MWe nuclear plant operating in last dispatch and third dispatch modes
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The pie charts in Figure 50 highlight the generation mix supplying the South Australian grid with the inclusion of a 1,125MWe nuclear
plant operating in last dispatch and third dispatch modes. In both modes of operation fossil fuels have been almost entirely displaced
and replaced by renewables and nuclear and a small amount of peaking capacity necessary to meet reliability standards, which could
be of the form of embedded diesel generators or wind paired with diesel generators. This approach is common in some countries
but not in Australia, where it is mostly deployed in remote communities69. In last dispatch mode nuclear contributes 20% of the
annual energy demand in South Australia and in third dispatch mode 49%, for the 2030 time horizon.
Figure 50: Annual energy sent out to the SA grid of a 1,125MWe nuclear plant operating in last dispatch and third dispatch modes
4.6 Summary of demand and technology inputs to economic modelling
The charts in the Figures below highlight some of the scenario inputs in terms of the demand projections and technology forecasts
in 2030 in the South Australian grid and exported to the NEM via the interconnector, with the scenarios being:
Base scenario: Demand aligned with EY’s IS3 - Strong climate change/action policy scenario with steady growth in
renewables, no grid storage and low interconnector constraint with 285MWe or 1,125MWe nuclear installed capacity
assessed in load following and baseload modes.
Scenario 1: Medium growth in demand, high EV penetration, medium renewables penetration and low interconnector
constraint with 285MWe or 1,125MWe installed capacity assessed in load following and baseload modes.
Scenario 2: High demand growth, low renewables penetration, high EV penetration and low interconnector constraint
with 285MWe or 1,125MWe installed capacity assessed in load following and baseload modes.
Scenario 3: High demand growth with high renewables penetration, high EV penetration and low interconnector constraint
with 285MWe or 1,125MWe installed capacity assessed in load following and baseload modes.
Scenario 4: Base scenario with no more wind installed capacity from the 2016 base of 1,314MW.
The variables inputs for the Base scenario and Scenarios 1 to 4 are shown in Figures 51 and 52.
Figure 51: Demand variables inputs for all generator options for all scenarios in 2030
69 See the following web site for a live data feed of a hybrid power system: http://www.kingislandrenewableenergy.com.au/
Unit
Variable Factor Variable Factor Variable Factor Variable Factor Variable Factor
Business % pa low -0.48 medium 0.24 high 1.00 high 1.00 low -0.48
Residential % pa low -0.48 medium 0.24 high 1.00 high 1.00 low -0.48
Major customers % pa medium 0.20 medium 0.24 high 0.54 high 0.54 medium 0.20
Hot water load % pa medium -0.10 medium 0.24 high 0.10 high 0.10 medium -0.10
Co generation Switch no 0 no 0 no 0.00 yes 1 no 0
Electric vehicle market share % 20 28 28 28 20
Base scenario Scenario 1 Scenario 2 Scenario 3 Scenario 4
Growth in Demand
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Figure 52: Technology variables inputs for all generator options for all scenarios in 2030
Tables 12 and 13 summarise the total annual energy from the small (285MWe SMR) and large nuclear (1,125MWe AP1000) options
supplying the South Australian grid and exported to the NEM for all five scenarios for the 2030 time horizon. The derivation of the
annual energy figures uses the average availability factor for the large nuclear plant, rather than the first year availability factor,
which was higher than the average availability factor70.
70 The capacity factor used in the load modelling was set to just under 97%. Within the economic model the outputs of each of the generator options are scaled back to reflect the projected availability from WSP-PB for the nuclear plant and from AETA for the CCGT plants. The numbers presented in Tables 12 and 13 reflect these adjusted availability levels.
Variable Factor Variable Factor Variable Factor Variable Factor Variable Factor
Penetration of business category medium 16% medium 16% low 11% high 80% medium 16%
Penetration of residential category 100% 100% 100% 100% 100%
Photovoltaics paired with storage medium 30% medium 30% low 20% high 80% medium 30%
Wind paired with storage low 0% medium 40% low 0% high 60% low 0%
Wind installed capacity (MW) medium 3000 medium 3000 low 1314 high 4421 low 1314
STP installed capacity (MW) low 0 medium 280 low 0 high 450 low 0
Interconnector constraint (MW) low 2000 low 2000 low 2000 low 2000 low 2000
V2G penetration high 70% low 0% low 0% low 0% high 70%
Photovoltaics
Wind generation
Solar Thermal Plant (STP)
Interconnector constraint
Vehicle to grid (V2G) penetration
Technology Projections
Base scenario Scenario 1 Scenario 2 Scenario 3 Scenario 4
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Table 12: Total annual energy (GWh) from nuclear options in load following and baseload modes of operation sent out to the SA grid and exported to the NEM via the interconnector for
the four Scenarios in 2030
Plant installed capacity (MWe)
Mode of operation Total annual energy sent out (GWh)
SA grid Exported via the interconnector to
the NEM
Base scenario
285 Load following 1,303 989
285 Baseload 1,478 834
1,125 Load following 3,359 5,144
1,125 Baseload 4,168 4,529
Scenario 1
285 Load following 1,300 1,006
285 Baseload 2,041 281
1,125 Load following 3,214 5,467
1,125 Baseload 6,397 2,635
Scenario 2
285 Load following 2,186 135
285 Baseload 2,220 102
1,125 Load following 7,215 1,818
1,125 Baseload 7,459 1,546
Scenario 3
285 Load following 643 1,363
285 Baseload 2,190 132
1,125 Load following 1,306 5,775
1,125 Baseload 7,282 1,752
Scenario 4
285 Load following 1,978 344
285 Baseload 2,134 188
1,125 Load following 5,410 3,622
1,125 Baseload 6.455 2,579
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Table 13: Total annual energy (%) from nuclear options in load following and baseload modes of operation sent out to the SA grid and exported to the NEM via the interconnector for the four
Scenarios in 2030
Plant installed capacity (MWe)
Mode of operation Total annual energy sent out as a percentage of maximum capacity
(%)
SA grid NEM Total
Base scenario
285 Load following 52% 40% 92%
285 Baseload 59% 33% 93%
1,125 Load following 34% 52% 86%
1,125 Baseload 42% 46% 88%
Scenario 1
285 Load following 52% 40% 92%
285 Baseload 82% 11% 93%
1,125 Load following 33% 55% 88%
1,125 Baseload 65% 27% 92%
Scenario 2
285 Load following 88% 5% 93%
285 Baseload 89% 4% 93%
1,125 Load following 73% 18% 92%
1,125 Baseload 76% 16% 91%
Scenario 3
285 Load following 26% 55% 80%
285 Baseload 88% 5% 93%
1,125 Load following 13% 59% 72%
1,125 Baseload 74% 18% 92%
Scenario 4
285 Load following 79% 14% 93%
285 Baseload 85% 8% 93%
1,125 Load following 55% 37% 92%
1,125 Baseload 65% 26% 92%
Key points to note from the Tables are:
The large nuclear option is most underutilised at a 72% capacity factor when operating in load following mode in Scenario
3 - high demand growth, high EV penetration with high renewables penetration and low interconnector constraint.
The small nuclear option is most underutilised at an 80% capacity factor when operating in load following mode in Scenario
3 - high demand growth, high EV penetration with high renewables penetration and low interconnector constraint.
The large nuclear option supplies most of its total annual energy sent out (76%) to the South Australian grid when operating
in baseload mode in Scenario 2 - high demand growth, high EV penetration, low renewables penetration and low
interconnector constraint.
The small nuclear option supplies most of its total annual energy sent out (89%) to the South Australian grid when
operating in baseload mode in Scenario 2 - high demand growth, high EV penetration, low renewables penetration and
low interconnector constraint and Scenario 3 (88%).
The large nuclear option supplies more than half (52% to 59%) of its total annual energy sent out to the NEM when
operating in load following mode in the Base Scenario - demand aligned with EY’s IS3 - Strong climate change/action policy
scenario with steady growth in renewables, no grid storage and low interconnector constraint, Scenario 1 and Scenario 3.
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The small nuclear option exports to the NEM more than half (55%) of its total annual energy sent out when operating in
load following mode in Scenario 3 - high demand growth, high EV penetration with high renewables penetration and low
interconnector constraint.
In summary, Scenario 3 - high demand growth, high EV penetration with high renewables penetration and low interconnector
constraint is the least conducive to the integration of the nuclear options if operating in load following mode in the South Australian
electricity system. This is due to the very high penetration and concomitant generation of renewables power that can not be
exported to the NEM even with low constraints on the interconnector capacity. Scenario 2 - high demand growth, high EV
penetration, low renewables penetration and low interconnector constraint is the most conducive to the integration of nuclear into
the electricity system and also sees most of the generation from the nuclear being used to meet local demand. Scenario 2 is closely
followed by Scenario 4, this being the Base scenario but with no further wind capacity installed from its 2016 base of 1,314MW.
4.7 Renewables and generation mix for selected scenarios
The scenarios considered in Section 4.6 were used to calculate the total annual energy demand and maximum HH power
requirement in South Australia and the level of annual generation (both peaking power and energy throughput) needed to provide
South Australia with its electricity needs from renewables and nuclear. The net deficit in the satisfaction of the energy demand in
South Australia would have to be met by alternative plant (i.e. fossil fuels) or imported from the NEM via the interconnector. These
results are shown in Table 1471.
71 These figures are based on the availability calculations from the load modelling. These are scaled back to reflect the availability projections from WSP-PB.
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Table 14: Generation for all scenarios supplying the South Australian demand in 2030
Generation supplying the South Australian demand
Nuclear installed capacity (MWe)
Mode of nuclear
operation
Total demand
Demand not met by renewables and nuclear
Renewables generation
Nuclear generation
(GWh) (GWh) Peak HH load (GW)
(GWh) Max HH generation
(GW)
(GWh) Max plate rating (GW)
Base scenario
285 LF 13,380 2,481 2.1 9,543 4.7 1,303 0.285
285 Base 13,380 2,481 2.1 9,361 4.7 1,478 0.285
1,125 LF 13,380 288 1.3 9,543 4.7 3,359 1.125
1,125 Base 13,380 288 1.3 8,688 4.7 4,186 1.125
Scenario 1
285 LF 15,381 2,364 2.3 11,664 4.9 1,300 0.285
285 Base 15,381 2,364 2.3 10,892 4.9 2,041 0.285
1,125 LF 15,381 322 1.5 11,664 4.9 3,214 1.125
1,125 Base 15,381 322 1.5 8,301 4.9 6,397 1.125
Scenario 2
285 LF 16,794 7,737 3.2 6,779 3.1 2,186 0.285
285 Base 16,794 7,737 3.2 6,744 3.1 2,220 0.285
1,125 LF 16,794 2,391 2.3 6,779 3.1 7,215 1.125
1,125 Base 16,794 2,391 2.3 6,522 3.1 7,459 1.125
Scenario 3
285 LF 16,16172 749 1.5 14,741 6.7 643 0.285
285 Base 16,161 749 1.5 13,130 6.7 2,190 0.285
1,125 LF 16,161 39 0.7 14,741 6.7 1,306 1.125
1,125 Base 16,161 39 0.7 8,427 6.7 7,282 1.125
Scenario 4
285 LF 13,380 4,201 2.2 7,119 3.0 1,978 0.285
285 Base 13,380 4,201 2.2 6,957 3.0 2,134 0.285
1,125 LF 13,380 545 1.4 7,119 3.0 5,410 1.125
1,125 Base 13,380 545 1.4 6,015 3.0 6,455 1.125
The results of the scenario runs highlight that with 3.2GW of wind and solar generation capacity in the South Australian grid
(i.e. Scenario 2) peak plant capacity73 of at least 3.2GW would be required to meet the demand in the South Australian grid not met
by renewables and the small nuclear plant. This level of generation, both in terms of peaking power and energy throughput would
need to be met from fossil fuels or imports from the eastern NEM and would account for about 46% of the total annual South
Australian energy demand if an SMR was installed in the grid and about 14% of the total South Australian energy demand if an
AP1000 was installed in the grid (c.f. Table 15) in 2030. The least amount of demand not met by renewables and nuclear is evidenced
in Scenario 3 - high demand growth, high EV penetration with high renewables penetration and low interconnector constraint, where
the demand not supplied by generators in South Australia is reduced to about 5% of the total annual energy demand in South
72 The difference in total demand between Scenario 3 and Scenario 2 is due to the impact of co-generation, which is assumed to be present in Scenario 3 but not present in Scenario 2.
73 In excess of renewables and nuclear installed capacity.
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Australia with an SMR option and rounded to 0% with the AP1000 option. The peak power capacity of fossil fuels plants or imports
from the NEM is reduced from 3.2GW to 1.5GW with the small nuclear option.
Clearly, if no additional baseload capacity74 were to be installed in South Australia, the State would have to rely increasingly on
OCGTs to meet the balance of demand not met by renewables paired with storage. However, increasing reliance on storage,
intermittent renewables and peaking OCGT generation has been forecast to lead to significant increase in price volatility under all
climate change/action policy scenarios (i.e. BIS, IS2 & IS3) resulting in:
An electricity system that is comprised of renewables, electricity storage and load following plant characterised by
significant price volatility and the potential for shortfalls in supply. Price volatility and shortfalls in supply may be further
exacerbated by increased penetration of electric vehicles and major industry, for example the potential development of
the front end of the nuclear fuel cycle75 considered in Scenario 3.
The supply deficit net of renewables and storage presents an opportunity for low carbon power generation technologies
to operate in either baseload or load following mode. The set of technologies that could meet these requirements include;
nuclear, high efficiency gas power generation based on a CCGT and CCGT that integrates carbon capture and geological
sequestration (i.e. CCS).
At a high level of demand but low level renewables generation capacity and low levels of storage (i.e. Scenario 2), peak load following
capacity of up to 3.2GW would be required and would have to be sourced from a dispatchable generator such as an open cycle gas
turbine (OCGT). At this level of installed capacity an interconnector expansion to at least 2.0GW is needed to avoid constraining the
generation output from the renewables and nuclear76 in South Australia.
Table 15 summaries the outputs of Table 14 in percentage terms for the demand not met by renewables and nuclear (i.e. residual
demand), renewables generation and nuclear generation as a percentage of the total annual energy demand in South Australia.
74 Includes nuclear options or the CCGT alternatives.
75 Refer to price duration curves prepared by EY for climate change/action policy scenarios BIS, IS2 & IS3).
76 Includes all renewable generation and an AP1000 reactor operating in load following mode.
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Table 15: % generation for the four scenarios supplying the South Australian demand in 2030
Demand requirements in South Australia
Nuclear installed
capacity (MWe)
Mode of nuclear
operation
Residual demand as % of
total demand
Renewables as % of total
demand
Nuclear as % of total demand
Base Scenario
285 LF 19% 71% 10%
285 Base 19% 70% 11%
1,125 LF 4% 71% 25%
1,125 Base 4% 65% 31%
Scenario 1
285 LF 15% 76% 9%
285 Base 15% 71% 14%
1,125 LF 3% 76% 21%
1,125 Base 3% 54% 43%
Scenario 2
285 LF 46% 41% 13%
285 Base 46% 41% 13%
1,125 LF 14% 40% 46%
1,125 Base 14% 40% 46%
Scenario 3
285 LF 5% 91% 4%
285 Base 5% 81% 14%
1,125 LF 0% 91% 9%
1,125 Base 0% 52% 48%
Scenario 4
285 LF 31% 54% 15%
285 Base 31% 53% 16%
1,125 LF 4% 54% 40%
1,125 Base 5% 46% 49%
Key points to note from Table 15 are:
Under the Base scenario, the proportion of demand in South Australia that remains unmet by the combination of
renewable generation, a small nuclear option and storage technologies is 19%. This level of demand will have to be met
by dispatchable generation including combined or open cycle gas turbines, including some imports from the eastern
regions of the NEM. Excluding the small nuclear option operating in either load following or baseload mode, the proportion
of unmet demand would be approximately 28%.
Under the High renewables and storage penetration case (Scenario 3) that includes a small nuclear option, 5% of demand
would have to be met by a combination of dispatchable generation including gas fired generation or some imports from
the eastern regions of the NEM. Excluding the small nuclear option operating in load following mode, the proportion of
unmet demand for this Scenario would be on the order of 8%.
Demand not met by renewables and nuclear is greatest in Scenario 2 - high demand growth, high EV penetration, low
renewables penetration and low interconnector constraint, at 46% of the total annual South Australian energy demand in
2030 for a small nuclear generator operating in either load following or baseload mode. Scenario 4 follows closely on
Scenario 2 with 36% of the South Australian demand not being met.
With the exception of the above, for all scenarios, imported generation is reduced to between 0% and 19% of the total
annual South Australian energy demand, for all nuclear options operating in either load following or baseload modes.
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For both nuclear generator options operating in either load following or baseload modes, renewables generation is
greatest in Scenario 3 at between 52% to 91% of the total annual South Australian energy demand followed by Scenario 1
at between 54% and 76%.
4.8 The importance of an enhanced interconnector
Evidence presented to the NFCRC77 suggests that there are several locations in South Australia where a power generator of
600MWe capacity could be installed without requiring an upgrade to the transmission network. While generation capacity of up to
400MWe could take advantage of the existing capacity of the high voltage (HV) grid, installing more than 600MWe of new
generation capacity (or two SMRs) will require an upgrade to the 275kV backbone and an expansion of the interconnector capacity
between South Australia and the NEM.
Whilst some of the nuclear options could operate without an interconnector upgrade, there are benefits to both renewables and
nuclear generation from enhancing the size of the interconnector. This is shown in Table 16 which presents the export of electrical
energy generation derived from renewables and nuclear via the interconnector to the NEM for all four Scenarios and compares
these exports with the case of a high interconnector constraint (i.e. 650MWe78). This is particularly illuminating as it informs on the
amount of constraint on renewables and nuclear generation that would result without increasing the capacity of the interconnector.
77 Presentation to the NFCRC by representatives of ElectraNet on 18 September 2015.
78 ElectraNet have stated that the Heywood interconnector is being upgraded to 650MW. There is additional capacity from the Murraylink interconnector, which should allow the combined capacity to be 870MW (ElectraNet Network Vision Discussion Paper, The future of South Australia’s regulated transmission network, December 2015). The modelling in this Section tests a worst case scenario where the Murraylink interconnector is not operational and the capacity is limited to 650MW. Similar issues will arise with an 870MW interconnector, albeit with a slightly lower level of constraint on generation.
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Table 16: Annual energy generation exports to the NEM for all four scenarios with interconnector constraints set to low (2,000MWe) and high (650MWe) in 2030
Annual energy export to the NEM (GWh)
Nuclear installed capacity (MWe)
Nuclear mode
Energy exports (low interconnector constraint
2,000MWe)
Energy exports (high interconnector constraint
650MWe)
Renew-ables
Nuclear Total export
Renew-ables
Nuclear Total export
Base scenario
285 LF 2,552 989 3,541 1,630 483 2,113
285 Base 2,714 834 3,548 1,692 423 2,115
1,125 LF 2,552 5,144 7,696 1,630 2,399 4,029
1,125 Base 3,202 4,529 7,731 1,858 2,184 4,042
Scenario 1
285 LF 2,178 1,006 3,184 1,412 548 1,960
285 Base 2,933 281 3,214 1,821 252 2,073
1,125 LF 2,178 5,467 7,645 1,512 2,626 4,138
1,125 Base 5,170 2,635 7,808 2,292 1,887 4,179
Scenario 2
285 LF 74 135 209 71 126 197
285 Base 109 102 211 102 96 198
1,125 LF 74 1,818 1,892 71 1,454 1,525
1,125 Base 331 1,546 1,877 237 1,297 1,534
Scenario 3
285 LF 6,097 1,363 7,460 3,155 575 3,730
285 Base 7,379 132 7,511 3,619 130 3,749
1,125 LF 6,097 5,775 11,872 3,155 2,004 5,159
1,125 Base 10,349 1,752 12,101 3,788 1,405 5,193
Scenario 4
285 LF 212 344 556 205 316 521
285 Base 374 188 562 345 181 526
1,125 LF 212 3,622 3,834 205 2,719 2,924
1,125 Base 1,314 2,579 3,893 871 2,089 2,960
Key points to note from Table 16 are:
The impact of the interconnector constraint is most pronounced for Scenario 3 - high demand growth, high EV penetration
with high renewables penetration and low interconnector constraint, curtailing the amount of energy that could be
exported to the NEM by over 50% from 12,101GWh to about 5,193GWh for baseload nuclear operation. In this instance
the largest reduction in exports is in renewables generation which reduces from 10,349GWh being exported to 3,788GWh.
Scenario 2 - high demand growth, high EV penetration, low renewables penetration and low interconnector constraint,
with an installed SMR is the least affected by an interconnector capacity constraint with a reduction of only 12GWh, which
is insignificant compared to the total generation capacity.
The scenario having the most annual energy available for export to the NEM after Scenario 3 is the Base scenario - Demand
aligned with EY’s IS3 - Strong climate change/action policy scenario with steady growth in renewables, no grid storage and
low interconnector constraint, this being annual energy exports of more than 7,000GWh with the large nuclear generator.
This scenario would be adversely impacted from a high interconnector constraint with a reduction in annual energy exports
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of nearly 4,000GWh for an AP1000 reactor operating in either load following or baseload mode. As an alternative, this
situation could be much improved if gird storage paired with wind was deployed in the South Australian grid79.
Table 17 highlights the annual energy generation exports derived from nuclear generation and renewable generation with nuclear
in different modes of operation and the constraints on both renewables and nuclear if these generating sources are curtailed from
accessing the NEM because of the transmission and interconnector constraints. As the amount of annual energy consumed within
the South Australian grid does not change with the interconnector size, any reduction in the percentage being exported represents
a constraint on the generator’s operation.
79 See Section 3.2.4 Pairing wind generation with grid storage for a discussion of the benefits of pairing distributed generation with distributed storage.
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Table 17: Total annual nuclear and renewable energy exported to the NEM with a low (2,00MWe) and high interconnector constraint (650MWe) in 2030 for load following and baseload
operation of the nuclear options
Impact of the interconnector constraint on nuclear and renewables generating sources
Nuclear installed capacity (MWe)
Mode of nuclear
operation
Annual nuclear generation exported
(GWh)
Annual renewables generation exported
(GWh)
With low IC Constraint (2,000MW)
With high IC
Constraint (650MW)
% reduction due to IC
constraint
With low IC Constraint (2,000MW)
With high IC
Constraint (650MW)
% reduction due to IC
constraint
Base scenario
285 LF 989 483 51% 2,552 1,630 36%
285 Base 834 423 49% 2,714 1,692 38%
1,125 LF 5,144 2,399 53% 2,552 1,630 36%
1,125 Base 4,529 2,184 52% 3,202 1,858 42%
Scenario 1
285 LF 1,006 548 46% 2,178 1,412 35%
285 Base 281 252 11% 2,933 1,821 38%
1,125 LF 5,467 2,626 52% 2,178 1,512 31%
1,125 Base 2,635 1,887 28% 5,170 2,292 56%
Scenario 2
285 LF 135 126 6% 74 71 4%
285 Base 102 96 4% 109 102 6%
1,125 LF 1,818 1,454 20% 74 71 4%
1,125 Base 1,546 1,297 17% 331 237 28%
Scenario 3
285 LF 1,363 575 58% 6,097 3,155 48%
285 Base 132 130 1% 7,379 3,619 51%
1,125 LF 5,775 2,004 65% 6,097 3,155 48%
1,125 Base 1,752 1,405 20% 10,349 3,788 63%
Scenario 4
285 LF 344 316 8% 212 205 3%
285 Base 188 181 4% 374 345 8%
1,125 LF 3,622 2,179 25% 212 205 3%
1,125 Base 2,579 2,089 19% 1,314 871 34%
Key points to note from Table 17 are:
Scenario 3 - high demand growth, high EV penetration with high renewables penetration and low interconnector
constraint, accounts for the highest nuclear constraint with exports dropping from 5,775GWh to 2,004GWh, a 65%
reduction, for an AP1000 option and from 1,363GWh to 575GWh, a 58% reduction for an SMR, when operating in load
following mode.
Scenario 3 with a large nuclear plant running in baseload mode of operation also accounts for the highest constraint on
renewables being exported dropping from 10,349GWh to 3,788GWh, a 63% reduction, for an AP1000 option and
7,379GWh to 3,619GWh, a 51% reduction for an SMR, when operating in baseload mode.
Scenario 1 - medium growth in demand, high EV penetration, medium renewables penetration and low interconnector
constraint mimics Scenario 3, but with a smaller reduction in levels of generation exported.
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Scenario 2 - high demand growth, high EV penetration, low renewables penetration and low interconnector constraint has
the least wastage of both nuclear and renewables generation for all nuclear generator options and operating modes.
Therefore to fully utilise the level of renewables availability in South Australia and to the NEM under the Base scenario, Scenario 1
and Scenario 3 will require expansion of the interconnector capacity or the development of power to fuel technology as received in
evidence to the NFCRC80. In Scenario 2 and Scenario 4 with an SMR option an interconnector upgrade would be essential to ensure
that peak demand can be met in South Australia from imports from the NEM if sufficient other peaking plant is not available in South
Australia.
Any upgrades would benefit renewables located in South Australia as well as the large nuclear option and could reduce wholesale
electricity prices in South Australia and other regions of the NEM. However, the cost of the interconnector and transmission network
upgrade is material and a separate investigation would be needed to consider if it delivered net market benefits.
80 Dickinson RR. Evidence to the Nuclear Fuel Cycle Royal Commission. 4 September 2015.
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5 GENERATOR COST AND BENEFIT ASSUMPTIONS
5.1 Approach to the economic model
The approach to the NPV modelling focussed on flexibility and transparency with all data inputs able to be defined and configured
by the user. This allows users of the model to understand each of the calculations and to change the parameter inputs in order to
assess how varying the parameters may impact the relative viability of the generator options.
The model has two main user selection options that make material impacts to the NPV. These break down into:
Economic scenarios – This involves the selection of economic model options such as the climate change/action policy
scenarios that are to apply in the modelling run.
Key Parameters – Most likely (i.e. central), high and low values are included for all the key parameters. These inputs all
have a defined source and justification and are discussed below.
This Section explains the options that were applied in the modelling and, where applicable, reports the settings used for the NPV
analysis, discussed in Section 6.
5.2 Economic scenario assumptions
5.2.1 Climate change/action policy assumptions
A critical element in determining both the wholesale electricity price and the cost of operation of gas fired power stations is the
climate change/action policy and how this translates into a carbon price for the generator options. The climate change modelling
considered three different scenarios selected by the NFCRC and was undertaken by EY81. The three scenarios were:
Baseline Climate Change Policy Scenario (BIS) – This scenario assumes the current Government’s 2030 emissions
reduction target of 26% to 28% below 2005 emissions continues to be in place. This will be achieved by a significant
expansion of the currently implemented ‘Emissions Reduction Fund Reverse Auction’ schemes with the Large Scale
Renewable Energy Target (LRET), Small Scale Renewable Energy Target (SRET) and energy efficiency policies continuing.
After 2030 a carbon price mechanism would be implemented to achieve the deep level of decarbonisation by 2050 across
all sectors of the economy aimed at reducing carbon emissions by 80% compared to 2000.
Moderate Climate Change/Action Policy Scenario (IS2) – This scenario assumes a carbon price mechanism is implemented
in 2020 that achieves the 2030 and 2050 emissions reduction target. The 2050 target of 80% below 2000 levels is then
targeted as the minimum needed to keep emissions at 550ppm. This and the baseline climate change policy scenario
assume a moderate level of electrification of the transportation sector.
Strong Climate Change/Action Policy Scenario (IS3) – This scenario assumes a more dramatic reduction in emissions in
line with the recent recommendations of the Climate Change Authority. It requires a 40% to 60% reduction in CO2
emissions relative to 2005 levels by 2030 using a carbon price adopted in 2020. This carbon emissions reduction target for
2030 of 60% below 2005 levels is consistent with a 1.5 degree centigrade of average warming by 2100.
A forecast of the carbon price applying at different time horizons under the three climate change/action policy scenarios that were
modelled is shown in Table 18. The modelling results are presented for all climate change/action policy scenarios as this can make
a material impact to the viability of the generator option.
81 Ernst & Young, Computational General Equilibrium (CGE) Modelling of Investment in the Nuclear Fuel Cycle, December 2015.
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Table 18: Carbon prices under different climate change/action policy scenarios
Scenario Carbon price (A$ per tonne)
Financial year starting 2030 2040 2050
Baseline investment scenario (BIS) $86 $126 $179
IS2 - Moderate climate change/action policy scenario (IS2) $88 $130 $185
IS3 - Strong climate change/action policy scenario (IS3) $123 $179 $254
5.2.2 Method of economic generator operation
The model has three potential dispatch options for how the generators are operated as follows:
Load following – In this option the nuclear/CCGT generators are dispatched after all the renewable generators including
storage have been dispatched. This is likely to result in constraints on generation depending on levels of renewable
generation and interconnector capacity.
Baseload – The plants operate at full capacity (allowing for availability), with an assumption that they are dispatched after
wind/solar with no storage. They may be restricted by the ability of the South Australian demand and the export
interconnector capacity constraint to absorb all of their output, but this restriction is relatively low with a low
interconnector constraint. Under this operation option all plants receive the average wholesale electricity price for all of
the energy supplied into the South Australian market and exported to the NEM.
Baseload with mid merit – Once constructed the nuclear generators have very low variable costs and would therefore
prefer to operate in baseload mode. With high carbon prices and increased gas prices there is a substantial variable cost
for the CCGT plants. These plants would therefore only operate when the marginal price is above the marginal cost. To
assess this, the EY market modelling provides an assumed capacity factor for a CCGT along with average wholesale price
received for this level of operation for 2030/31 and 2049/50. This data is shown in Table 19 and has been used to derive
an increase in wholesale electricity prices for each year from 2030/31 onwards using linear interpolation. It is assumed
that the same capacity factor and wholesale price increase would apply between IS2 and BIS climate change/action policy
scenarios and as a simplifying assumption the same capacity factor/wholesale electricity price adjustment is applied to the
CCGT with CCS.
Table 19: Capacity factors and wholesale electricity price adjustments
Option and Financial Year Starting BIS/IS2
2030/31
BIS/IS2
2049/50
IS3
2030/31
IS3
2049/50
Capacity factor CCGT (%) 68.2% 65.5% 66.9% 64.1%
% Increase in wholesale electricity price received (%) 16.8% 20.4% 18.1% 23.0%
The NPV modelling results presented apply the ‘baseload with mid merit’ mode of operation. The LCOE calculations consider both
baseload and mid-merit modes of operation.
5.2.3 Discounting approach
The NPV model has all costs in real 2014/15 dollars with mid-year discounting applied that assumes costs and benefits appear evenly
over the year. Two options were available for the way in which discounting could be applied.
Option 1 - Discounting all costs/benefits from a common commissioning date - With this approach all costs and benefits
are expressed as at the beginning of the financial year of 2030 or 2050 (i.e. the year in which the proposed generator is
scheduled for completion). Within the NPV model there are a number of costs for plant construction and infrastructure
that will arise before the commissioning date. These costs are built up with a profile determined by when they arise and
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the applicable financing costs are then used to derive a total construction cost that includes interest that would be incurred
by the time the plant is delivered (e.g. 1st July 2030).
Option 2 - Discounting from the year of project commencement - The alternative option for the NPV analysis is to
undertake discounting from the time of commencement of plant expenditure. This would be 2020 for the large nuclear
option and from some later date for the other plants being commissioned in 2030. Under this option no interest needs to
be applied to any of the costs as it is reflected in the model discounting from the first year that costs are incurred for the
plant.
The first discounting option has the advantage of being easier to compare NPVs between the generator options as there is a common
year for commencement. The costs that are treated as arising in 2030 could be seen as equivalent to the cost that a new entrant
generator would need to pay for an installed generator in 2030 (if it reflected all costs to plan, build and finance the plant up to this
point). This discounting option was selected as the base option for the NPV modelling. The two discounting options produce the
same LCOEs so this only impacts the presentation of NPV results.
5.2.4 Wholesale electricity prices
A number of time series wholesale electricity prices for South Australia were calculated by EY82 based on the different carbon prices
produced from the climate change/action policy scenarios. In EY’s modelling, the nuclear plants were not initially selected for
operation and an additional run was undertaken to assess the impact on the wholesale electricity price if they were included in the
generation mix in South Australia under the IS3 climate change/action policy scenario. The inclusion of the nuclear generator would
reduce average wholesale electricity prices in the IS3 climate change/action policy scenario as shown in Table 20. The same
percentage difference on the wholesale electricity price was used in all the NPV calculations when assessing the viability of the
nuclear generator under the BIS and IS2 climate change/action policy scenarios83.
Table 20: Wholesale electricity prices under different climate change/action policy scenarios
Scenario Wholesale electricity price ($MWh)
Financial Year Starting 2030 2040 2050
BIS - Baseline climate change policy scenario $124.0 $133.1 $154.2
IS2 - Moderate climate change/action policy scenario $125.1 $141.9 $161.7
IS3 - Strong climate change/action policy scenario $138.7 $155.0 $185.7
IS3 - Strong climate change/action policy scenario with large nuclear $105.6 $124.3 $148.0
IS3 - Strong climate change/action policy scenario with small nuclear $130.3 $146.1 $175.8
5.2.5 Gas prices
The gas prices have been provided by EY84 and are aligned with the climate change/action policy scenarios being assessed and
AEMO’s gas price forecast85. These prices are fairly consistent between climate change/action policy scenarios and are all flat after
2039. The forecast prices are shown in Table 21.
82 Ibid $122.7, $135.1
83 If any of the CCGT plants were assumed to be running as baseload then the same price reduction was applied as the small nuclear option as they are of a similar size. However, the base assumption is that they run mid-merit order.
84 ibid.
85 Fuel and Technology Cost Review Data, Produced by ACIL Allen for Australian Energy Market Operator, 2014.
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Table 21: Gas prices under different climate change/action policy scenarios
Scenario Gas price ($/GJ)
2030 2040 2050
BIS - Baseline climate change policy scenario $9.23 $10.20 $10.20
IS2 - Moderate climate change/action policy scenario $9.20 $10.18 $10.18
IS3 - Strong climate change/action policy scenario $9.19 $10.16 $10.16
5.2.6 Transmission and interconnector upgrade
The NPV model provides for the option of expanding the capacity of the interconnector and transmission line, which will provide for
a 500kV link through South Australia into Victoria. This would benefit both the new plant and the growth in renewable generation,
which may be restricted without an upgrade in the interconnector capacity. Costings for this upgrade have been provided by
WSP-PB86 with a central estimate of around $2bn.
The base NPV model has the contribution from the generators set at 0%. This assumes the upgrade is implemented by the
transmission company, as it delivers net market benefits and could be recovered through standard transmission use of system (TUoS)
charging mechanisms. Alternative options considered have the large nuclear generator making a contribution of 50% or 100%
towards the cost of the upgrade. It was assumed that smaller generators could all operate close to baseload mode with the existing
infrastructure and no upgrade costs were therefore associated with these plants.
5.2.7 Additional economic assumptions
A number of additional economic assumptions were made in the NPV model including:
Plant availability – Set to medium from the estimates considered with a most likely level of 90% for the CCGT with CCS,
92% for the CCGT, 93% for the small nuclear reactor and between 89% and 97% for the large nuclear reactor.
Infrastructure – This covers the costs of transport, connection and transmission needed explicitly for the generator option.
The base costs assumed a greenfield implementation site for each of the generator options.
Cooling Water – The current assumption is that no cooling towers are required as plants will all be sea cooled.
5.3 Derivation of the key parameters
There are a number of key assumptions that have an uncertain value and have a material impact on the NPV of the different
generator options. The parameters with the highest materiality are shown in Tables 22 to 30 along with a description of how they
were derived.
5.3.1 Discount rate
The magnitude of the capital cost for the nuclear plant means that the choice of discount rate is the most critical single parameter
within the model. The NPV model has assumed a real discount rate of 10% for all generator options as requested by the NFCRC,
which was similar to the 10.47% weighted average cost of capital (WACC) estimated by WSP-PB for nuclear plants. The NPV model
tests a significant range of discount rates down to 7%, which is below the discount rate used by the Future Grid Forum (FGF) in 2013
with the higher rates of 13% above the 11% pre-tax real WACC estimates provided by Imperial College87 in their assessment of the
86 Parsons Brinkerhoff, Initial Business Case and Cost Estimates, Quantitative analyses and initial business case – establishing a nuclear power plant and system in South Australia, 16 September 2015.
87 Imperial College Centre for Energy Policy and Technology “Costs Estimates for Nuclear Power in the UK”, August 2012.
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WACC for nuclear plants. This is by far the most sensitive parameter in the modelling reflecting the high up front cost of nuclear
plants.
Table 22: Discount rates for generator options
Plant options Discount rate real
Most likely High Low
CCGT with CCS 10% 13% 7%
CCGT 10% 13% 7%
Small nuclear 10% 13% 7%
Large nuclear 10% 13% 7%
The NPV model includes the option for the application of a social discount rate of 4% to be used as an alternative to a commercial
rate. This has a very significant impact on the model outputs and is considered in more detail in Section 8 of this Report.
5.3.2 Life of the plant
The central value of the life of the plant has been set at 60 years for the nuclear options and 40 years for the gas fired options in line
with estimates from AETA and WSP-PB. Given the level of the real discount rates applied, the range for the life of the plant is not
that material to the NPV of the different options.
Table 23: Life of plant options
Plant options Life of plant (years)
Most likely High Low
CCGT with CCS 40 50 30
CCGT 40 50 35
Small nuclear 60 70 45
Large nuclear 60 70 45
5.3.3 Overnight capital cost
The impact of the overnight capital cost estimates shown in Table 24 is significant for all options, but is much more material for the
nuclear generators. The nuclear generator estimates have been produced by WSP-PB88 based on international evidence, which also
includes a range for the costs. The estimated costs for 2050 are retained at the same level as 2030.
The capital cost of the gas fired generators is based on the estimate for 2028 and 2048 (assuming 2 years to build the plant) from
EY/AETA inputs. EY data is derived from the latest 2015 cost data from EPRI with AETA learning curves applied to convert this into
2028/2048 data. Costs were not available for a CCS in South Australia so a scaled up version of Victorian costs was derived using the
ratio between CCGT costs that apply in Victoria/South Australia. The CCS costs have a larger potential for an increase as they are
dependent on a strong learning curve being achieved before 2028. The high cost includes an allowance for this learning curve not
being achieved in addition to the level of variability applied to all other options.
The costs and benefits in the NPV model include some pre-construction costs that are independent of the size for the nuclear
generator. These have a wide range and result in a material impact for the small nuclear option once interest during construction is
included. Pre-construction costs are included for the CCGT options, but these are relatively small and are provided as a single value
parameter that is listed in Appendix G.
88 ibid.
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Table 24: Plant cost of generator options
Parameter Cost
Most likely High Low
Capital cost of CCGT with CCS in 2030 ($/kW) 2,567 3,594 2,054
Capital cost of CCGT with CCS in 2050 ($/kW) 2,492 3,489 1,994
Capital cost of CCGT in 2030 ($/KW) 1,579 1,895 1,263
Capital cost of CCGT in 2050 ($/KW) 1,639 1,967 1,311
International capital cost of small nuclear plant (US$/kW) 4,008 4,797 3,393
Local capital cost of small nuclear plant (A$/kW) 3,588 4,295 3,044
International capital cost of large nuclear plant (US$/kW) 3,167 3,495 2,942
Local capital cost of large nuclear plant (A$/kW) 3,475 3,844 3,229
Nuclear project development costs (A$m) 316 631 158
Nuclear overseas project development costs (US$m) 65 129 32
Regulatory and licensing and public enquiry costs (A$m) 67 99 40
5.3.4 Nuclear fuel costs
The nuclear fuel costs have been provided as a $/MWh rate that includes costs of enrichment and fabrication. The cost shown in
Table 25 varies between small and large nuclear generators, which reflects efficiencies and economies of scale. Whilst this is
presented as a variable cost it will be largely fixed with the fuel replaced as part of a replacement cycle. A 20% range has been
applied to this cost in line with the recommendations of WSP-PB89.
Table 25: Fuel cost of nuclear generator options
Plant options Cost of fuel ($/MWh)
Most likely High Low
Small nuclear 9.3 11.2 7.5
Large nuclear 7.8 9.3 6.3
5.3.5 Operations and maintenance costs
Operation and maintenance costs have been provided for each of the generation options and are split between fixed and variable
costs. Almost all of the nuclear costs are fixed with the estimates having been provided by WSP-PB. These costs have been split into
overseas and local maintenance costs and include a separate cost for the insurance elements of the plant. The costs for the CCGT
options have been sourced from EY/AETA. The operations and maintenance costs are expected to grow at a rate of 1.05% per annum
above the inflation rate and this has been applied to all generator options from the date from which the variable is set.
Table 26: Operation and maintenance costs of generator options
Parameter Operation and maintenance cost
Most likely High Low
VOM CCS in 2030 ($/MWh sent out) 14.7 17.6 11.8
VOM CCGT in 2030 ($/MWh sent out) 1.8 2.2 1.5
VOM small nuclear in 2015 ($/MWh sent out) 0.1 0.1 0
VOM large nuclear in 2015 ($/MWh sent out) 0.1 0.1 0
89 ibid.
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Parameter Operation and maintenance cost
Most likely High Low
FOM CCS in 2030 ($/MW) 42,868 51,442 34,294
FOM local nuclear for a large plant in 2015 ($/MW) 98,503 118,183 78,720
FOM overseas nuclear for a large plant in 2015 (US$/MW) 57,400 68,880 45,920
FOM local nuclear for a small plant in 2015 ($/MW) 108,035 129,663 86,408
FOM overseas nuclear for a small plant in 2015 (US$/MW) 50,123 60,065 40,078
Insurance large nuclear in 2015 (US$/MW) 17,528 19,373 16,298
Insurance small nuclear in 2015 (US$/MW) 20,295 24,293 17,220
FOM CCGT in 2030 ($/MW) 24,492 29,395 19,597
Annual escalation factor for O&M (%) 1.05% 1.25% 0.5%
5.3.6 Loss factors
The marginal loss factors (MLF) applying for each of the generators will have an impact on the value of the electricity sales. All
generators in South Australia will be paid according to the regional reference price (RRP) in the applicable region and require the
MLF to be adjusted for the generator revenue for the number of electricity units sold. The MLFs shown in Table 27 were produced
by WSP-PB with separate factors for small and large nuclear generators and included a range of potential outcomes. The small
generator MLFs was adopted for the gas plants as the MLFs should be independent of technology.
Table 27: Marginal loss factors for generator options
Plant options Marginal loss factor (MLF)
Most likely High Low
Large nuclear 0.965 0.990 0.965
Small nuclear/CCGT with CCS/CCGT 0.975 0.975 0.950
5.3.7 Plant efficiencies
The gas consumption and carbon outputs associated with the CCGT and CCGT with CCS plants are a function of the expected
efficiencies of these plants and were derived using AEMO data produced by ACIL Allen. Plant efficiencies are expected to improve
over time and different variables are therefore applied for the 2030 and 2050 time horizons reflecting the expected efficiency of the
plant when construction commences in 2028 and 2048.
The ranges applied in the sensitivity analysis highlighted in Table 28 are based on the AETA data for the high estimates that have
steady learning curves, compared with the AEMO data set that declines over time. The low estimates assume that only half of the
projected efficiency increase emerges from 2015 to 2028 and that after 2028 the efficiencies stay constant. As a modelling
simplification, no adjustment has been made for the efficiency impacts from different modes of operation.
Table 28: Plant efficiencies of CCGT generator options
Plant options Plant efficiencies
Most likely High Low
CCGT with CCS in 2030 48.1% 49.5% 46.1%
CCGT in 2030 54.7% 55.1% 52.7%
CCGT with CCS in 2050 50.7% 57.5% 48.1%
CCGT in 2050 56.6% 62.1% 54.7%
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5.3.8 Carbon sequestration
The cost of carbon sequestration was based on the Australian Power Generation Technology Report90. This report had cost estimates
varying between $10 per tonne up to almost $80 per tonne for transporting low volumes of CO2 over long distances with a single
source to single sink case. The cost estimate in the Australian Power Generation Technology Report are built up using a lower cost
of capital than assumed in this NPV analysis and most of the lower end estimates are in areas that have better access to storage
sites than is the case in South Australia. The assessment carried out for this NPV modelling has therefore applied a most likely figure
that is towards the upper end of the numbers provided, but has tested a fairly wide range of numbers as illustrated in Table 29.
One observation from the numbers in the Australian Power Generation Technology Report is that the costs do not include costs of
storage site exploration and appraisal works, which can be significant adding 14% to 25% to the total cost91.
Table 29: Cost of carbon sequestration
Parameter Cost ($/tonne CO2)
Most likely High Low
Cost of carbon sequestration 45 80 30
5.3.9 De-commissioning and storage costs
The NPV model includes decommissioning costs estimated by WSP-PB. These are escalated each year, but due to the long life of the
nuclear generators have a relatively small impact on the NPV. The levy to cover storage costs was calculated by WSP-PB based on a
cost of between US$1m and US$2m per tonne of heavy metal (data provided by Jacobs). This was converted into $/MWh shown in
Table 30 using assumptions on efficiency for the different plant with a range provided by WSP|PB.
Table 30: Decommissioning costs
Plant options Decommissioning costs
Most likely High Low
De-commissioning costs for large nuclear (US$m) 513 615 410
De-commissioning costs for small nuclear (US$m) 256 308 205
Levy to cover dry storage costs for large nuclear (US$/MWh) 3.8 4.6 2.6
Levy to cover dry storage costs for small nuclear (US$/MWh) 4.6 6.2 3.1
5.4 Sensitivity based key parameters
There are a number of parameters in the model that are set to zero as a base value, but can be varied to reflect project risks (both
upside and downside) and this is assessed in the sensitivity analysis. These parameters are discussed below.
5.4.1 Change in the US$ exchange rate
Many of the nuclear generator costs are entered in US$ to reflect the overseas component of the cost. A long term exchange rate
has been entered as a time series in the model with an average between 2020 and 2050 of A$0.766 to US$1.000 with a relatively
narrow range within this period of A$0.755 to A$0.76992. Given recent large charges in exchange rates the model reviews a 15% and
90 Australian Power Generation Technology Report, November 2015.
91 W Hou, G Allinson, I MacGill, PR Neal, MT Ho (2014), ‘Cost comparison of major low-carbon electricity generation options: an Australian case study’, Sustainable Energy Technologies and Assessments, 8:131–148 referenced in Australian Power Generation Technology Report, November 2015.
92 Exchange rate was provided by EY as part of the CGE modelling.
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-10% move away from the central estimate in order to consider the impact on the nuclear option NPVs. The use of an asymmetric
rate is more reflective of the level of exchange rate movement experienced in the last 10 years.
Table 31: Change in US$ exchange rate
Parameter Exchange rate divergence
Most likely High Low
Divergence of US$ exchange rate from the central value 0% 15% -10%
5.4.2 Percentage change in carbon price from the most likely prediction
The NPV model set up requires the user to select climate change/action policy scenarios, which automatically result in a carbon price
track that should apply during the operation of the model. However, even within a selected climate change/action policy scenario
there is likely to be variation in the carbon price. This parameter enables users to vary the carbon price around the expected
trajectory and assess its impact on each generating plant option.
The carbon price is assumed to feed through into the wholesale electricity price and the quantification of this has been assessed by
comparing a ‘no action’ (no carbon price) wholesale electricity price with the wholesale electricity price that emerges from BIS, IS2
and IS3 climate change/action policy scenarios. This derives the elements of the wholesale price associated with the carbon price,
which can be adjusted upwards/downwards according to the range defined in Table 32.
Table 32: Percentage change in the carbon price
Parameter Change in the carbon price
Most likely High Low
Change in the carbon price 0% 10% -10%
5.4.3 Variation in wholesale electricity price without a carbon price
The selection of the climate change/action policy scenarios and therefore the carbon price tracks would have been a key input in EY
calculating a wholesale electricity price trajectory for 2030 to 2050. However, as with the carbon price, even within a set of climate
change/action policy scenarios there is likely to be a variation in the wholesale electricity price. Part of this variability will be
associated with changes in the carbon price, which will have been assessed separately. To avoid double counting, this variable
removed the carbon price impact and examined the impact of the variability on the remaining part of the wholesale electricity price.
Table 33: Percentage change in the wholesale electricity price
Parameter Change in the wholesale electricity price
Most likely High Low
Variation in wholesale electricity price without carbon 0% 10% -10%
5.4.4 Percentage change in gas price
Wholesale gas prices have been produced for South Australia by EY’s modelling93 for a specific carbon price scenario, which includes
a forecast gas price. The gas prices vary slightly between climate change/action policy scenarios, but generally rise from around
$9.2GJ in 2030 to around $10.2GJ by 2040. This is in line with the recent AEMO data94.
93 ibid.
94 ibid.
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All of these forecasts represent a material increase in current gas costs and reflect expectations of a growing LNG industry and
domestic prices reflecting international levels. There is the possibility that the LNG industry does not continue to develop with
weaker demand growth in China (and other areas) and a glut of supply resulting in gas continuing to be sold in Australia with prices
remaining closer to current levels rather than the increase to above $10/GJ that are predicted. There is also an alternative scenario
of strong growth in demand for gas emerging after the recent downturn in the market with increasing gas replacing coal as a fuel
source for generation. The NPV model therefore applies a relatively wide 20% range to the gas price projections.
As well as impacting the operating costs of the gas fired generators, the gas price will also impact the wholesale electricity price. It
is important to note that the wholesale electricity price will be adjusted to avoid the model overstating the impact of any
increase/decrease in gas prices from the expected level. Due to the complexity of the market modelling it is not possible to re-run
the NPV modelling for any small change to these variables and therefore a proxy was set up to assess the impact. This is based on
the percentage of time that the gas plant is assumed to be the marginal plant, the assumed efficiency of the gas fired plant setting
the price and the percentage of the changing costs that are passed through to the wholesale price95. These calculations only impact
the NPV sensitivity analysis.
5.4.5 Time and cost of delay
There is a risk of delay in delivery for any of the generator options, which increase with the construction time and complexity of the
generator option. A profile for construction costs has therefore been set up in the NPV model and can be varied according to the
length of delay/acceleration specified. Within the NPV modelling the delay has to be an integer and is capped at two years for the
large nuclear plant and one year for the other options. The delay/acceleration period for the nuclear option is consistent with the
range indicated in the WSP-PB analysis with the gas fired plant aligned with the small nuclear option. As the CCGT plants are assumed
to only have a two year construction period, no acceleration is considered feasible.
Associated with the delay period is a likely overrun of costs. These are considered together due to the high project burn rates
associated with these types of construction. The potential cost overrun has been set to 25% for the CCGT with CCS and nuclear plants
options with a lower 10% applied to CCGT as a technology that is more established in Australia. This overrun is lower than some
historical experience with nuclear plant, but this also reflects the higher forecast costs being applied within the modelling compared
to the levels previously considered. The modelling also assumes an established design and a next of a kind (NOAK) installation.
Table 34: Time delay of plant options
Plant options Time delay (years)
Most likely High Low
CCGT with CCS 0 1 0
CCGT 0 1 0
Small nuclear 0 1 -1
Large nuclear 0 2 -1
95 An explanation for how these variable have been set is provided in Appendix G
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6 VIABILITY ASSESSMENT OF GENERATOR OPTIONS
6.1 Review of NPV results
The results presented in Tables 35 and 36 derive from the key parameters discussed in Section 5 for all three of the proposed climate
change/action policy scenarios. The climate change/action policy scenarios have a material impact on the value of the wholesale
electricity price and therefore on the viability of the generator option being considered. It is therefore sensible to consider the
possible outcomes together. Due to the difference in the capacity of the generator option, a benefit cost (B/C) ratio has also been
presented in order to highlight a more meaningful comparison between the options.
Table 35: 2030 NPV and benefit cost ratios for 2030
Plant options Baseline climate change
policy scenario (BIS)
Moderate climate
change/action policy
scenario (IS2)
Strong climate
change/action policy
scenario (IS3)
NPV B/C NPV B/C NPV B/C
CCGT with CCS -$ 479 0.86 -$ 334 0.90 $ 9 1.00
Small nuclear -$ 2,326 0.56 -$ 2,182 0.59 -$ 1,820 0.66
Large nuclear -$ 7,870 0.55 -$ 7,413 0.57 -$ 6,263 0.64
CCGT $ 80 1.02 $ 222 1.07 $ 319 1.09
Table 36: 2050 NPV and benefit cost ratios for 2050
Plant options Baseline climate change
policy scenario (BIS)
Moderate climate
change/action policy
scenario (IS2)
Strong climate
change/action policy
scenario (IS3)
NPV B/C NPV B/C NPV B/C
CCGT with CCS -$ 66 0.98 $ 99 1.03 $ 617 1.17
Small nuclear -$ 2,068 0.63 -$ 1,901 0.66 -$ 1,365 0.75
Large nuclear -$ 6,960 0.62 -$ 6,416 0.65 -$ 4,680 0.74
CCGT $ 222 1.06 $ 372 1.10 $ 565 1.13
Key points to note are:
The modelling assumption resulted in all of the nuclear plant options having negative NPVs for both the 2030 and 2050
time horizons with a highest benefit/cost ratio of 0.75 achieved under the IS3 climate change/action policy scenario in
2050. This reflects the higher carbon price and therefore the higher wholesale electricity prices that apply by this point.
The combination of wholesale electricity price tracks, carbon prices and capital costs result in only the CCGT having positive
NPV for the BIS/IS2 climate change/action policy scenarios in 2030. The CCGT with CCS has a slightly positive NPV for the
IS3 climate change/action policy scenario in 2030 although the sensitivity analysis indicates a reasonable probability that
the NPV could still be negative.
In 2050 under all three climate change/action policy scenarios, the CCGT has a strongly positive NPV with the CCGT with
CCS being marginally positive under the IS2 climate change/action policy scenarios and strongly positive under the
IS3 climate change/action policy scenario with the highest benefit/cost ratio of any option.
The CCGT has a relatively tight band for the benefit/cost ratios of between 1.02 and 1.13. This is a much smaller range
than the other generation options.
6.2 Review of LCOE results
An alternative way of reviewing the viability of generator options is to consider the LCOE for each of the options. A summary of the
LCOE for the four plant options under each of the climate change/action policy scenarios is shown in Figure 53. In the case of the
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CCGTs, it is instructive to consider the LCOE baseload and for non-baseload operation as this will reflect how the plant could operate.
Due to the impact that the large nuclear option has on the wholesale electricity price, and therefore the revenue for the generator,
a direct comparison between the LCOE and the NPV cannot be made.
Figure 53: LCOE of generation options
Key messages from this chart are:
The LCOE of a small nuclear plant is always higher than the other generation options under any of the climate
change/action policy scenarios.
The large nuclear plant always has a higher LCOE than the baseload CCGT plant. However under the IS3 climate
change/action policy scenario in 2050. it is marginally lower than the LCOE for mid-merit operation.
The costs of the nuclear plant are not materially impacted by the climate change/action policy scenario and are therefore
almost identical for all options96.
The CCGT always has the lowest baseload LCOE except for the IS3 climate change/action policy scenario in 2050, which
starts with a relatively high carbon price and results in the CCGT with CCS being the lowest cost option.
6.3 Breakdown of component costs
Table 37 provides a breakdown of the key costs for each generator option being considered with a description of how the costs are
built up. The figures presented in Table 37 are for the IS2 climate change/action policy scenario and are included as a percentage of
total cost for the 2030 and 2050 time horizons and based on mid-merit operation for the CCGTs.
Table 37: Percentage breakdown of component costs for generating plant options
Cost category 2030 time horizon 2050 time horizon
CCGT
with CCS
Small
nuclear
Large
nuclear
CCGT CCGT
with CCS
Small
nuclear
Large
nuclear
CCGT
Capital cost 27% 69% 69% 20% 26% 66% 65% 18%
Connection/infrastructure 5% 7% 4% 5% 5% 7% 3% 4%
FOM cost 5% 15% 18% 3% 6% 17% 21% 3%
96 The plant is assumed to purchase a small amount of power at the wholesale price when it is non-operational. This results in very minor differences between the nuclear options.
0.0 50.0 100.0 150.0 200.0 250.0
CCGT with CCS Baseload
CCGT with CCS Mid Merit
Small Nuclear
Large Nuclear
CCGT Baseload
CCGT Mid Merit
LCOE $/MWh
IS3 2050 IS2 2050 BIS 2050 IS3 2030 IS2 2030 BIS 2030
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Cost category 2030 time horizon 2050 time horizon
CCGT
with CCS
Small
nuclear
Large
nuclear
CCGT CCGT
with CCS
Small
nuclear
Large
nuclear
CCGT
VOM cost 9% 0% 0% 1% 11% 0% 0% 1%
Decommissioning 0% 4% 4% 0% 0% 4% 4% 0%
Fuel 41% 6% 5% 43% 39% 5% 5% 37%
Carbon cost 12% 0% 0% 28% 13% 0% 0% 35%
Other 0.59% 0.35% 0.96% 0.70% 1% 0% 1% 1%
As a visual comparison the LCOE broken down by component is also provided for both the 2030 and 2050 time horizons in Figure 54
for the IS2 climate change/action policy scenario.
Figure 54: LCOE components ($/MWh) for mid merit operation of CCGT plants
Key points to note include:
Both of the nuclear options are dominated by the capital costs, which are assumed to remain constant over time. The
reduction in the $/MWh cost for the large nuclear generator reflect the lower levels of constraint by 2050 and therefore
an increased number of MWh over which the fixed costs are apportioned.
The CCGT’s LCOE shows a material increase due to the carbon prices, which reflects these rising carbon prices that are only
partly offset by efficiency gains. There is a small increase for the CCGT with CCS plant as the carbon price increases are
largely offset by the efficiency gains
A more detailed review of each component part is provided below.
6.3.1 Capital cost/connection and infrastructure
The combination of capital costs and connection and infrastructure costs is the key cost element for the nuclear plant and makes up
68% to 76% of the total lifetime cost. This combination of capital costs has a far lower materiality effect for the CCGT/CCS options
and is dependent on a number of key factors including:
Overnight capital cost per kW.
Discount rate.
Interest during construction.
Size of the plant.
$- $50.00 $100.00 $150.00 $200.00 $250.00
CCS 2030
CCS 2050
S Nuclear 2030
S Nuclear 2050
L Nuclear 2030
L Nuclear 2050
CCGT 2030
CCGT 2050
LCOE Components $/MWh
Capital Cost Connection and Infrastructure cost Fixed Ops and Maintenance Cost
Variable Ops and Maintenance Cost Decommissioning Cost Fuel Cost
Operating Carbon Cost Other Costs
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Exchange rate (for the nuclear option).
A number of these factors have moved adversely since previous studies were undertaken, two to three years ago, that had indicated
lower LCOE levels for nuclear97. This includes movement in the exchange rate and assumptions of a lower real discount rate that
applied in the modelling conducted at that time.
6.3.2 Fixed/variable operation and maintenance (VOM) cost
The NPV model assumes that operation and maintenance costs continue to increase in real terms each year by 1.05% per year. This
is assumed to impact both local and Australian components of costs and justifies the growth in the percentage of these costs
between the 2030 and 2050 models.
The nuclear costs are almost entirely in the fixed category. These costs range from 15% to 18% of the total costs for the 2030 model
to 17% to 21% for the 2050 model. This cost split is different to some previous studies such as that undertaken by AETA98, which had
more of a variable/fixed cost split. However, the revised allocation is believed to better reflect how costs are incurred and the very
low level of cost savings available should the plant not be operating close to baseload mode.
The VOM costs for the CCGTs are based on the Electric Power Research Institute (EPRI) data and are significant for the CCGT with
CCS at 9% to 11% of the lifetime costs, but are only 1% of the lifetime costs for the CGCT. This is based on a much lower level of VOM
per MWh than in other studies. Whilst the fixed operation and maintenance (FOM) costs are higher for the CCGT, this only partly
offsets this difference with an estimate of the PV of the combined lifetime costs for FOM and VOM of only 4% for the CCGT compared
to 14% to 17% for the CCGT with CCS in 2030 and 2050.
6.3.3 Decommissioning cost
The decommissioning cost category includes the levy to cover the dry storage costs, which is the major element of this component
of cost making up over 98% of the total. The levy for the dry storage costs is worked out on an MWh basis and is assumed to start
at $16m per year for the small nuclear plant and $55m per year for the large nuclear plant and is expected to increase over time.
The decommissioning costs of the plant, whilst a substantial sum of $300m to $600m, is assumed to occur after 60 years of
discounting and therefore has little impact on the PV cost. Plant rehabilitation costs are included for the CCGT plant but these are
not material and this is rounded to zero.
6.3.4 Fuel cost
The fuel costs are the most significant lifetime cost element for both the CCGT with CCS and the CCGT. The costs are 41% and 39%
of the lifetime costs of the CCGT with CCS in 2030 and 2050, compared to 43% and 37% for the CCGT. Both plants benefit from the
increase in efficiency between 2030 and 2050 commissioning dates with a relatively small increase in gas prices between 2030 and
2050. The CCGT percentage cost reduction is also more significant due to the increase in carbon prices between 2030 and 2050,
which make up a larger proportion of the total.
The nuclear fuel costs are fixed in $/MWh and do not escalate. They result in a figure of 6% of the costs for the small plant and 5%
of the costs for the large plant when commissioned in 2030 and 5% for both options when commissioned in 2050. The small
percentage cost reduction reflects the growth in operating costs rather than any change in the fuel costs for the nuclear plant.
97 See for example Australian Energy Technology Assessment 2013 Model Update – December 2013 for the NOAK Comparison.
98 Australian Energy Technology Assessment 2012, Bureau of Resources and Energy Economics.
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6.3.5 Carbon price
The carbon cost varies materially between the different climate change/action policy scenarios. The IS2 climate change/action policy
scenario shows the carbon cost for the CCGT increasing as a percentage of total costs from 28% in 2030 to 35% in 2050. Under the
IS3 climate change/action policy scenario the carbon cost would be 35% of the lifetime cost in 2030 and 42% of the total costs in
2050. This level of costs would be more significant without the efficiency improvements that are assumed to arise between 2030
and 2050.
The operating carbon costs for the CCGT with CCS include both costs for carbon sequestration and transportation as well as the costs
for carbon permits for the percentage of carbon that isn’t captured. The NPV model made an assumption that 85% of the carbon
can be economically captured by the CCS plant. The majority of the cost in both 2030 and 2050 is associated with the sequestration
and storage of carbon.
6.3.6 Other costs
Other costs are made up of three elements as follows:
TUoS charges (based on load when not generating).
Electricity costs when not generating based on the wholesale electricity price.
Additional spinning reserve costs for the large nuclear generator, based on the need for the system to hold incremental
spinning reserve to cope with the potential loss of a 1,125MWe unit.
These costs are small for all generators with an estimate between 0% and 1% of the PV of costs for all options under the IS2 climate
change/action policy scenario.
6.4 Breakdown of revenue and LPOE
Figure 55 shows the LPOE for each unit of generation at the station gate. It reflects the MLFs being applied for each generator and
the assumed average wholesale price of generation. This is lower for the large nuclear plant as it is assumed to negatively impact
the wholesale electricity price by much more than the other plants even if all are operating in baseload mode. If the CCGTs operate
mid merit, the spread of differences in the price received for each MWh is more dramatic.
Figure 55: LPOE $/MWh
Key points to note are:
$- $50.0 $100.0 $150.0 $200.0 $250.0
CCGT with CCS Baseload
CCGT with CCS Mid Merit
Small Nuclear
Large Nuclear
CCGT Baseload
CCGT Mid Merit
LPOE $/MWh
IS3 2050 IS2 2050 BIS 2050 IS3 2030 IS2 2030 BIS 2030
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Baseload operation results in a similar price of electricity being received for all three of the smaller generators. There are
some differences relating to the varying life of the plants, but these are minor.
The large nuclear LPOE is an average of 17% per MWh lower than for the other plant operating as baseload in both 2030
and 2050.
The LPOE differential between the large nuclear plant and CCGTs running at mid merit order is most significant at
$63/MWh under the IS3 climate change/action policy scenario for a plant commissioned in 2030 and $65/MWh under the
IS3 climate change/action policy scenario for a plant commissioned 2050. The price differential is lower when comparing
BIS/IS2 climate change/action policy scenarios, but remains above $50/MWh.
6.5 Internal rates of return
The internal rate of return (IRR) is the discount rate at which the NPV equals zero. The higher the IRR the more attractive a project
looks to an investor. Table 38 shows the IRR for the four generator options under the different climate change/action policy
scenarios.
Table 38: Internal rates of return for generator options
Plant 2030 time horizon 2050 time horizon
BIS IS2 IS3 BIS IS2 IS3
CCGT with CCS 5.8% 7.2% 10.1% 9.4% 11.0% 15.6%
Small nuclear 4.3% 4.8% 5.9% 4.6% 5.1% 6.6%
Large nuclear 4.0% 4.5% 5.6% 4.2% 4.8% 6.4%
CCGT 11.0% 12.6% 13.6% 12.6% 14.3% 16.4%
Key points to note include:
The IRR of the CCGT is always the highest.
The large nuclear option always has the lowest IRR.
The CCGT with CCS is always the second place option.
6.6 Carbon amelioration benefits of the technologies
Table 39 shows the carbon amelioration benefits for each of the generation options for 2030 and 2050 under the different climate
change/action policy scenarios. This is the undiscounted carbon saving based on the net saving of the average amount of carbon
emitted by the fuel at the power station compared to the average carbon intensity of generation in the NEM under the various
climate change/action policy scenarios. It assumes that the CCGT plants are operating as mid-merit order not baseload mode and is
based on a continued improvement in the average carbon intensity of generation over time reflecting the introduction of climate
change/action policy measures.
The calculations include both Scope 1 and Scope 3 emissions for nuclear and the CCGT plants and are based on the emission
intensities published by ACIL Allen99 that have been applied on a state by state basis to the EY100 forecasts of the generation mix.
The Scope 3 emissions include the carbon that would have been produced from the mining, production and processing of the fuels
used for electricity generation, whilst the Scope 1 emissions are associated with the combustion of the fuel. The Scope 3 emissions
are not impacted by CCS technology and are responsible for more than half of the emissions associated with the CCGT with CCS
plant.
99 Emissions Intensity Values, ACIL Allen Consulting (prepared for AEMO), 11 April 2014.
100 Electricity market modelling NEM Generation Mix, EY (for NFCRC), 30th November 2015.
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Table 39: Lifetime carbon savings in million tonnes of CO2
Plant 2030 time horizon 2050 time horizon
BIS IS2 IS3 BIS IS2 IS3
CCGT with CCS (106 tonnes) 3.1 2.5 0.7 2.1 2.0 0.7
Small nuclear (106 tonnes) 21.4 20.6 17.6 9.7 19.4 17.1
Large nuclear (106 tonnes) 83.8 80.7 69.1 77.5 76.3 67.6
CCGT (106 tonnes) -19.9 -20.5 -22.2 -20.2 -20.4 -21.3
Due to the growth in renewables and therefore reduced average intensity of generation, the carbon amelioration benefits of the
nuclear technologies decrease over time. However, the use of an undiscounted figure means that the savings from the 2030 and
2050 models cover much of the same time period.
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7 SENSITIVITY AND MONTE CARLO ANALYSIS
7.1 Overview
One of the challenges with this type of NPV analysis is the level of uncertainty associated with many of the key inputs to the NPV
model. There are likely to be significant differences in the projected value for a number of items such as discount rates, operating
cost, overnight capital cost, fuel cost and so forth and these will have a material impact on the NPV of the different options being
considered.
To mitigate this uncertainty, all of the key parameters have been set up in the NPV model with a central (most likely) value and a
high and low value. The sensitivity of these parameters has then been tested in two ways. First an individual assessment of the
influence of each parameter with the application of a ‘Tornado’ diagram and second using ‘Monte Carlo’ analysis to allow simulations
with all key parameters randomly changing within the prescribed range.
The charts presented in this Section are for the IS2 climate change/action policy scenario in 2030. The full range of sensitivity
(i.e. ‘Tornado’ and ‘Monte Carlo’) analysis charts for each of the generating options is provided in Appendix H.
7.2 CCGT with CCS
The ‘Tornado’ chart for the CCGT with CCS option under the IS2 climate change/action policy scenario in Figure 56 shows that a
change in the discount rate within the specified range would be sufficient to make the NPV positive. The key variables were:
Discount rate – A reduction in the discount rate to 7% improves the NPV by around $365m, whilst an increase to 13%
would decrease the NPV by a further $225m.
Capital cost of the CCGT with CCS – This variable has a higher potential for an increased cost than a further cost reduction
as the forecast cost in 2028 already includes a significant learning impact. The model considers a 40% cost increase and a
20% cost reduction which could reduce the NPV by $365m or improve it by $180m.
Percentage change in gas prices – The gas price will have a material impact on the cost of operation of the gas plants and
the level of uncertainty resulted in a 20% range being applied. This symmetric variable can move the NPV by $240m in
either direction. The impact is lessened by the assumption of mid-merit order operation and therefore a lower capacity
factor than if the plant was operating in baseload mode.
Figure 56: ‘Tornado’ diagram for CCGT with CCS plant in 2030 for the IS2 climate change/action policy scenario
While a number of the significant individual parameters are symmetric, the uncertainty and downside risk on the capital cost of the
plant is heavily reflected in the Monte Carlo simulations. This results in a decrease in the mean NPV to -$440m from the most likely
13.0%
3594
20.0%
-10%
80
1.00
-10%
46.1%
17.6
7.0%
2054
-20.0%
10%
20
-
10%
49.5%
11.8
-$800 -$700 -$600 -$500 -$400 -$300 -$200 -$100 $0 $100
Discount Rate Real CCGT CCS (10%)
Capital Cost of CCGT with CCS in 2030 ($2567/kW)
Percentage change in Gas Prices (0%)
Variation in Wholesale Price without Carbon (0%)
Cost of Carbon Sequestration ($45/tonne)
Time & Cost for Delay CCGT with CCS (0 years)
% Change in Carbon Price from Most Likely Predictions (0%)
Efficiency of CCGT with CCS in 2030 (48.14%)
VOM CCS in 2030 ($14.7/MWh sent out)
NPV of CCGT with CCS in 2030 (M$AUD)Showing Values >= 100.0 M$AUD
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value of -$334m. The model has a minimum NPV of -$1,142 m and a maximum of $498m. The model has 3% of results with a positive
NPV shown in Figure 57, which compares with the IS3 climate change/action policy scenario for 2030 where 33% of trials were NPV
positive.
Figure 57: ‘Monte Carlo’ simulation for CCGT with CCS plant in 2030 for the IS2 climate change/action policy scenario
7.3 Small nuclear
The ‘Tornado’ chart for the small nuclear option under the IS2 climate change/action policy scenario shown in Figure 58 is dominated
by the discount rate, reflecting the high capital cost of the plant. Key factors impacting the NPV all relate to the initial capital cost
and include:
Discount rate – This could improve the NPV by around $1bn or reduce the NPV by around $675m. This impact is over 70%
higher than the impact of any of the other parameters.
Time and cost for delay – A reduction in the time for construction would improve the NPV by around $250m reflecting
reduced interest costs. A delay of 1 year will reduce the NPV by over $700m reflecting the 25% increased costs associated
with the delay and the lost revenue from not operating in the planned commissioning year.
Nuclear project development costs – The project development costs have a wide range with a 100% increase and 50%
costs reduction considered. These were fixed at the same level for both nuclear plant options and the wide range has a
material impact on the smaller plant. The lower level could improve the NPV by nearly $250m, whilst the higher level could
deteriorate the NPV by close to $500m.
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Figure 58: Small nuclear ‘Tornado’ diagram in 2030 for the IS2 climate change/action policy scenario
The ‘Monte Carlo’ analysis shown in Figure 59 has a close to $90m deterioration from the most likely value of -$2,182m to a mean
of -$2,269m, reflecting the more negative impact of several parameters. There were no trial results with a positive NPV. The least
negative NPV value was -$786m with a standard deviation of -$440m. Only in the IS3 climate change/action policy scenario for 2050
were positive NPVs observed with 0.3% of the outcomes being NPV positive.
Figure 59: Small nuclear ‘Monte Carlo’ simulation in 2030 for the IS2 climate change/action policy scenario
7.4 Large nuclear
The ‘Tornado’ diagram for the large nuclear option under the IS2 climate change/action policy scenario shown in Figure 60 is again
dominated by the discount rate. The impact of key parameters includes:
Discount rate – This could improve the NPV by over $3.1bn or decrease the NPV by close to $2.3bn. The size of the impact
is reflective of the level of investment and construction time for this type of plant. This parameter has double the impact
of any other parameter in the NPV model.
Time and cost for delay – This has a maximum delay of 2 years and a 25% increase in cost resulting in a reduction in the
NPV of over $1.8bn. The reduced time to build could improve the NPV by $850m.
Exchange rate change from expected level – The high capital cost means that a potential change in the exchange rate of
15% could improve the NPV by over $1.2bn with a 10% reduction lowering the NPV by over $1bn.
13.0%
1.00
631
-10%
4797
-10%
4295
7.0%
- 1.00
158
15%
3393
10%
3044
-$3,500 -$3,000 -$2,500 -$2,000 -$1,500 -$1,000 -$500 $0
Discount Rate Real Small Nuclear (10%)
Time & Cost for Delay Small Nuclear (0 years)
Nuclear Project Development Costs (315.7 AUD $M)
Exchange Rate Change from Expected Level (0%)
International Capital Cost of Small Nuclear Plant (USD$4007.75/kW)
Variation in Wholesale Price without Carbon (0%)
Local Capital Cost of Small Nuclear Plant (AUD$3587.5/kW)
NPV of Small Nuclear in 2030 (M$AUD)Showing Values >= 200.0 M$AUD
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Figure 60: ‘Tornado’ diagram of large nuclear in 2030 for the IS2 climate change/action policy scenario
The ‘Monte Carlo’ analysis shown in Figure 61 indicates a deterioration in the NPV from a most likely value of -$7,412 to -$7,607m.
The NPV modelling does have a very large standard deviation of close to $1.5bn. There are no trial results with a positive NPV and
the maximum value is still a negative $2bn. Only in the IS3 climate change/action policy scenario for 2050 does the model show any
positive NPV results with 0.3% of positive trials for this simulation.
Figure 61: ‘Monte Carlo’ simulation for large nuclear for the IS2 climate change/action policy scenario
7.5 Combined cycle gas turbine
The ‘Tornado’ chart for the CCGT under the IS2 climate change/action policy scenario in Figure 62 shows that a number of single
parameters could turn the positive NPV to negative. The most influential of these parameters are:
Discount rate – A reduction in the discount rate to 7% improves the NPV by almost $400m, whilst the higher discount rate
results in a NPV deterioration of -$200m.
Variation in wholesale electricity price without carbon – A variation in the non-carbon wholesale electricity price has a
symmetrical impact of just under $250m from a 10% increase/decrease.
Percentage change in gas price – The gas price will have a material impact on the cost of operation of the gas plants and
a 20% range is applied. This symmetric variable that can move the NPV by $230m in either direction. This is slightly smaller
13.0%
2.00
-10%
-10%
3495
631
3844
1.25%
118183
7.0%
- 1.00
15%
10%
2942
158
3229
0.50%
78720
-$12,000 -$10,000 -$8,000 -$6,000 -$4,000 -$2,000 $0
Discount Rate Real Large Nuclear (10%)
Time & Cost for Delay Large Nuclear (0 years)
Exchange Rate Change from Expected Level (0%)
Variation in Wholesale Price without Carbon (0%)
International Capital Cost of Large Nuclear Plant (USD$3167.25/kW)
Nuclear Project Development Costs (315.7 AUD $M)
Local Capital Cost of Large Nuclear Plant (AUD$3474.75/kW)
Annual Escalation Factor for O&M (1.05%)
FOM Local Nuclear for large Plant in 2015 ($98502.5/MW)
NPV of Large Nuclear in 2030 (M$AUD)Showing Values >= 500.0 M$AUD
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than the CCGT with CCS impact ($240m in each direction) reflecting the lower efficiency of the CCS plant, which offsets
the increased capacity of the CCGT.
Figure 62: ‘Tornado’ diagram for CCGT in 2030 for the IS2 climate change/action policy scenario
The ‘Monte Carlo’ analysis presented in Figure 63 shows a small deterioration in the mean NPV to $182m compared to a most likely
value of $222m, but it remains strongly positive. The simulation has almost 81% of the outcomes with NPV positive results with a
range of -$374m to $987m. The standard deviation was the smallest of the options considered at $197m. The ‘Monte Carlo’ analysis
for the IS3 climate change/action policy scenario in 2030 improves the position further due to the higher wholesale electricity prices,
with close to 92% of simulations having a positive NPV.
Figure 63: ‘Monte Carlo’ simulation for CCGT in 2030 for the IS2 climate change/action policy scenario
13.0%
-10%
20.0%
1894.8
1.00
52.7%
7.0%
10%
-20.0%
1263.2
-
55.1%
-$100 $0 $100 $200 $300 $400 $500 $600 $700
Discount Rate Real CCGT (10%)
Variation in Wholesale Price without Carbon (0%)
Percentage change in Gas Prices (0%)
Capital Cost of CCGT in 2030 ($1579/KW)
Time & Cost for Delay CCGT (0 years)
Efficiency of CCGT in 2030 (54.68%)
NPV of CCGT in 2030 (M$AUD)Showing Values >= 100.0 M$AUD
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8 IMPACT OF ALTERNATIVE SYSTEM SCENARIOS
8.1 Approach
The results below summarise the impact to the NPV model of different systems scenarios or modes of operation from the model
compared to the base case presented in Sections 6 and 7. The selection of scenarios aligns with those in Section 4.6.
8.2 Scenario 1 - Medium growth demand and renewable penetration
The first scenario reviewed was Scenario 1, which was a medium growth in demand, high EV penetration, medium renewables
penetration and low interconnector constraint. The results are summarised in Tables 40 and 41 for the 2030 and 2050 time horizons.
Table 40: 2030 impact of Scenario 1
Plant options Baseline climate change
policy scenario (BIS)
Moderate climate
change/action policy
scenario (IS2)
Strong climate
change/action policy
scenario (IS3)
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
CCGT with CCS -$476 2 -$332 3 $13 3
Small nuclear -$2,320 6 -$2,176 6 -$1,813 7
Large nuclear -$7698 172 -$7232 181 -$6,059 204
CCGT $84 4 $226 4 $324 4
Table 41: 2050 impact of Scenario 1
Plant options Baseline climate change
policy scenario (BIS)
Moderate climate
change/action policy
scenario (IS2)
Strong climate
change/action policy
scenario (IS3)
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
CCGT with CCS -$66 0 $99 0 $617 0
Small nuclear -$2,068 0 -$1,901 0 -$1,365 0
Large nuclear -$6,934 26 -$6,389 28 -$4,648 32
CCGT $222 0 $372 0 $565 0
Key points to note are:
The changes result in nuclear and CCGT capacity running almost without constraints on capacity. This is due to:
o The increased level of demand from both residential and business customers as well as EVs: and
o Increased levels of storage for wind generation.
The introduction of the STP does not impact the nuclear plant under the baseload option as it is assumed to be dispatched
after nuclear generation.
The 2050 modelling only impacts the nuclear options as the other plants were not constrained by 2050.
8.3 Scenario 2 - High demand growth with low renewables penetration
This modelling scenario had high demand growth combined with low renewables penetration, high EV penetration and low
interconnector constraint. The results are presented in Tables 42 and 43.
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Table 42: 2030 impact of Scenario 2
Plant options Baseline climate change
policy scenario (BIS)
Moderate climate
change/action policy
scenario (IS2)
Strong climate
change/action policy
scenario (IS3)
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
CCGT with CCS -$476 2 -$332 3 $13 3
Small nuclear -$2,320 6 -$2,176 6 -$1,813 7
Large nuclear -$7,698 172 -$7,231 182 -$6,058 205
CCGT $84 4 $226 4 $324 4
Table 43: 2050 impact of Scenario 2
Plant options Baseline climate change
policy scenario (BIS)
Moderate climate
change/action policy
scenario (IS2)
Strong climate
change/action policy
scenario (IS3)
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
CCGT with CCS -$66 0 $99 0 $617 0
Small Nuclear -$2,068 0 -$1,901 0 -$1,365 0
Large Nuclear -$6,934 26 -$6,389 28 -$4,648 32
CCGT $222 0 $372 0 $565 0
Key points to note are:
The combination of high demand including EVs and low renewable generation results in all plant running at full capacity
with almost no constraints for generation that can’t be sold in South Australian or over the NEM.
The revised NPV results are very similar to Scenario 1.
The 2050 results only impact on the large nuclear model.
8.4 Scenario 3 - High demand growth and high renewables penetration
The third scenario combines high demand with high renewables penetration, high EV penetration and a low interconnector
constraint. The parameter set up shown in Figure 64 was therefore chosen for the 2030 and 2050 model runs.
Figure 64: Renewable parameter set up for Scenario 3
The impact of these changes is shown in Tables 44 and 45.
2030 2050
Business category penetration (%) high 80% 80%
PV paired with storage (%) high 80% 80%
Wind paired with storage (%) high 60% 80%
Wind installed capacity (MW) high 4421 4421
Photovoltaics (PV)
Wind
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Table 44: 2030 impact of Scenario 3
Plant options Baseline climate change
policy scenario (BIS)
Moderate climate
change/action policy
scenario (IS2)
Strong climate
change/action policy
scenario (IS3)
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
CCGT with CCS -$476 2 -$332 3 $13 3
Small nuclear -$2,320 6 -$2,176 6 -$1,813 7
Large nuclear -$7,698 172 -$7,231 182 -$6,058 205
CCGT $84 4 $226 4 $324 4
Table 45: 2050 impact of Scenario 3
Plant options Baseline climate change
policy scenario (BIS)
Moderate climate
change/action policy
scenario (IS2)
Strong climate
change/action policy
scenario (IS3)
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
CCGT with CCS -$66 0 $99 0 $617 0
Small nuclear -$2,068 0 -$1,901 0 -$1,365 0
Large nuclear -$6,934 26 -$6,389 28 -$4,648 32
CCGT $222 0 $372 0 $565 0
Key Points to note are:
Despite the high renewable generation this option still improves the NPV compared to the Base scenario. This is due to
the increase in demand/storage outweighing any impact of increased renewable generation on the nuclear dispatch when
in baseload mode.
Only the large nuclear plant is impacted in the 2050 model as the other plants were not constrained in the Base scenario.
8.5 Scenario 4 – Base Scenario with Low Wind Growth
This scenario has the same levels of demand and renewable generation as the base scenario, but with the level of wind
generation restriction to the current level of operation. The results are presented in Table 46 and 47.
Table 46: 2030 impact of Scenario 4
Plant options Baseline climate change
policy scenario (BIS)
Moderate climate
change/action policy
scenario (IS2)
Strong climate
change/action policy
scenario (IS3)
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
CCGT with CCS -$476 $2 -$332 $3 $13 $3
Small nuclear -$2,320 $6 -$2,176 $6 -$1,813 $7
Large nuclear -$7,698 $172 -$7,231 $182 -$6,058 $205
CCGT $84 $4 $226 $4 $324 $4
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Table 47: 2050 impact of Scenario 4
Plant options Baseline climate change
policy scenario (BIS)
Moderate climate
change/action policy
scenario (IS2)
Strong climate
change/action policy
scenario (IS3)
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
CCGT with CCS -$66 0 $99 0 $617 0
Small nuclear -$2,068 0 -$1,901 0 -$1,365 0
Large nuclear -$6,934 26 -$6,389 28 -$4,648 32
CCGT $222 0 $372 0 565 0
Key points to note are:
The impact is similar to Scenarios 1 and 3 as the reduced wind generation creates an increased requirement for
other forms of generation
Despite the reductions in business and residential demand all plant run very close to baseload with almost no
constraints in 2030
There are no constraints on any of the plant options running at full capacity in either baseload or load following
mode in 2050.
8.6 Load following mode
The base version of the model assumes that all the generators are dispatched after only PV and wind without storage is dispatched.
However, an alternative is that they could be dispatched after all renewable generation including storage and STP. This scenario
assumes that the CCGTs still operate as mid-merit plant and is projected to have the impact on the model results outlined in Tables
48 and 49.
Table 48: 2030 load following mode
Plant options Baseline climate change
policy scenario (BIS)
Moderate climate
change/action policy
scenario (IS2)
Strong climate
change/action policy
scenario (IS3)
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
CCGT with CCS -$485 -$6 -$342 -8 -$1 -10
Small nuclear -$2,344 -$18 -$2,201 -19 -$1,841 -21
Large nuclear -$8,140 -$269 -$7,698 -286 -$6,590 -327
CCGT $71 -$9 $212 -10 $308 -11
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Table 49: 2050 load following mode
Plant options Baseline climate change
policy scenario (BIS)
Moderate climate
change/action policy
scenario (IS2)
Strong climate
change/action policy
scenario (IS3)
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
CCGT with CCS -$74 -8 $90 -9 $605 -13
Small nuclear -$2,085 -16 -$1,918 -17 -$1,385 -20
Large nuclear -$7,413 -453 -$6,896 -480 -$5,243 -563
CCGT $212 -9 $361 -10 $553 -12
Key points to note are:
The assumption of the 2GW interconnector reduces the impact for all options as most of the output not supplied in South
Australia can be sold via the interconnector.
The impact of selecting load following rather than base load mode is reduced by the assumption of low levels of storage
for wind generation. In both dispatch approaches wind without storage is dispatched ahead of nuclear/CCGT plants and
therefore materialises as a constraint.
The impact would be larger if the modelling included any Solar Thermal Plant, which pushes the nuclear/CCGT options
further down the dispatch schedule.
The restrictions on generation are larger for the CCGT options than the small nuclear option due to their larger capacity.
However, as they only lose the marginal cost associated with the electricity, the impact on the NPV is lower for the CCGT
options.
8.7 Social discount rate
Within the model an alternative scenario was a social discount rate of 4% rather than a commercial discount rate. This rate was
specified by the NFCRC and could be applied to account for the inter-generational nature of the cash flows that accrue from the
options under consideration101. This is most relevant for the nuclear options if they were believed to have additional societal
benefits, particularly around climate change, that may not be fully valued by the markets. The results are shown in Tables 50 and 51.
Table 50: 2030 social discount rate
Plant options Baseline climate change
policy scenario (BIS)
Moderate climate
change/action policy
scenario (IS2)
Strong climate
change/action policy
scenario (IS3)
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
Small nuclear 256 2582 603 2785 1543 3363
Large nuclear 81 7951 1193 8606 4203 10467
101 Financial Modelling Methodology for NPP Business Case Analyses, Nuclear Fuel Cycle Royal Commission, 5th November 2015.
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Table 51: 2050 social discount rate
Plant options Baseline climate change
policy scenario (BIS)
Moderate climate
change/action policy
scenario (IS2)
Strong climate
change/action policy
scenario (IS3)
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
NPV
$m
Difference
to Base Case
Small nuclear 357 2425 727 2628 1910 3275
Large nuclear 371 7330 1568 7984 5393 10073
The figures in the Tables demonstrate that if a social discount rate was applied then a large/small nuclear option would be viable
under all scenarios. The results of the IRR analysis in Section 6 indicated that the commercial viability may be possible at a discount
rate above 4%.
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9 GAME CHANGING EVENTS
9.1 Introduction
The results presented in this Report demonstrate some variability on the viability of the generating plant options between the
climate change/action policy scenarios, but on the whole deliver an outcome that the CCGT plant performs relatively well on an
NPV/LCOE basis when compared to the nuclear options. These results are dependent on the input assumptions chosen, particularly
those around discount rates, wholesale electricity prices, carbon cost and the capital costs of the nuclear reactors.
The NPV sensitivity analysis results presented in Section 7 demonstrates the impact of changes around the most likely values for
each key parameter. However, it does not consider the impact to the modelling of fundamental shifts or ‘game changers’ that may
materially impact the viability of the generator options. This Section considers a number of ‘game changing’ events and where
applicable their impact on the LCOE. The LCOE has been chosen, as often the game changing event will have a fundamental impact
on the wholesale electricity market and therefore any assessment of the impact on the NPV may not be as illuminating as it is for
the LCOE.
9.2 Game changers
In their Paper102, Marvel and May stress the importance of planning for game changing events saying “Planning for game changing
events is not simply a matter of preventing unpleasant surprises or capitalising on unanticipated opportunities; rather, it requires
flexibility and adaptability. Events become game changers, and game changers become catastrophes, in part because of the inability
of forecasters to anticipate and plan for them.”
The model is able to throw light on some potential game changers, which include:
Game changers from nuclear technology
o Significant changes in reactor technology costs
o Global market penetration of small scale nuclear technologies
Game changers from outside the nuclear field
o Increased importance of climate change and a concomitant step change in the price of carbon
o Significant movement in the cost of capital
o Oil/gas price shock
o Stepwise reductions in the capital cost of storage or competing technologies
o Political desire for nuclear generation
o Electricity market re-design
It should be stressed that these are not proposed as likely events that are predicted to occur, but instead represent a range of
potential developments that if they were to emerge could change the viability of competing generating options.
9.2.1 Reactor technology costs
The model applies commissioned research undertaken by WSP-PB103 for the costs of nuclear generation and this is based on recent
projects across the globe. There has been a relatively slow level of growth in nuclear deployment in the last decade compared to
other technologies and unlike emerging technologies, such as PV, costs have not fallen. Should there be a wider deployment of
nuclear technology then there is the possibility that these costs could be reduced significantly with global economies of scale or if
102 Examples in this Section of the Report draw on the Paper by Kate Marvel and Michael May, American Academy of Arts and Sciences, Game Changers for Nuclear Energy, 2011.
103 ibid.
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multiple sites were commissioned in Australia. The international cost elements may also be reduced due to exchange rate
movements. If there was a combination of a reduction of both local and international capital cost by 25% it would have the impacts
on the LCOE between the generating options illustrated in Figure 65.
Figure 65: LCOE with reducing reactor costs
The reduction in cost would bring the LCOE comparisons more into alignment. However, in 2030, both the large nuclear and small
nuclear options remain at a consistently higher LCOE than the CCGT alternatives. In addition, the larger nuclear reactor has a material
impact on the wholesale price of electricity, so even with the same LCOE the NPV for them would be lower. If the reduced capital
costs due to economies of scale also resulted in lower operating costs, then the impact of both factors could make the nuclear plant
options more viable.
9.2.2 Development of smaller scale technologies
The significant capital expenditure associated with traditional nuclear plants is beyond the reach of many entities including
governments. However, with advancing technology and ongoing research and development, SMRs could step into the breach with
much smaller capacities ranging from 45MWe to 1,125MWe. These plants cost less, adjust more readily to the characteristics of grids
with relatively little base load requirements, are factory built and shipped to site and require less down time during their lifetimes.
They can potentially compete with smaller capacity renewables such as STPs with a number of utilities in the US now having
committed to getting units approved for commercial use104.
The customisation of these small plants may assist in reducing costs, although development and siting costs could be large. It would
have the benefit of avoiding the risk with a large plant that cannot operate in baseload mode due to capacity constraints, or that its
impacts on the market is so significant that the wholesale electricity price and therefore the plant’s revenue drops by a material
amount.
9.2.3 Climate change and the price of carbon
Marvel and May state that “The threat of climate change has the potential to reshape the entire electricity sector.” But at the same
time they conclude that “… aggressively moving forward on climate change can be seen as a necessary but not a sufficient condition
for a large increase in nuclear power’s share of the worldwide electricity market.” They form this view on the basis that whereas the
104 Kate Marvel and Michael May, American Academy of Arts and Sciences, Game Changers for Nuclear Energy, 2011, https://www.amacad.org/pdfs/book_game_changers.pdf
0.0 50.0 100.0 150.0 200.0 250.0
CCGT with CCS Baseload
Small Nuclear
Large Nuclear
CCGT Baseload
LCOE $/MWh
IS3 2050 IS2 2050 BIS 2050 IS3 2030 IS2 2030 BIS 2030
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private sector is exhibiting some interest in low-carbon technologies, such as nuclear power, only governments can put in place the
framework to make these technologies competitive with fossil fuels on a timescale foreseen in this model.
The modelling undertaken in this project does recognise the importance of climate change through the carbon price that was
included in all of the modelling scenarios. However, a question could be raised on whether this is materially understating the
importance of climate change and that achieving the expected climate change targets may require a much higher carbon price. As
an illustrative example only, the charts in Figure 66 consider the impact on the LCOEs of the different generation options if the
carbon price ended up at a level that was 50% higher than predicted in the analysis.
Figure 66: LCOE with increased carbon prices
Whilst the increased carbon price makes the LCOE’s more comparable, the 2030 scenarios still shows the CCGT having a generally
lower LCOE in 2030. With the impact of the large nuclear plant on the wholesale electricity price, the NPV of the nuclear option is
likely to be significantly lower than the CCGT alternatives.
9.2.4 Significant change in the cost of capital
There is a risk of a lack of market opportunities for investors driving down the cost of capital, which would materially change the
viability of plant. The LCOE calculations shown in Figure 67 have the discount rate for all the plant at a real 7%, which was the lower
end of the range considered in the sensitivity analysis.
0.0 50.0 100.0 150.0 200.0 250.0
CCGT with CCS Baseload
Small Nuclear
Large Nuclear
CCGT Baseload
LCOE $/MWh
IS3 2050 IS2 2050 BIS 2050 IS3 2030 IS2 2030 BIS 2030
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Figure 67: LCOE with 7% cost of capital
This shows that the LCOE for large nuclear option is only slightly higher than the baseload LCOE for the CCGT in 2030 under the IS2
and BIS climate change policy scenarios and has a lower LCOE under IS3. In the 2050 assessment the large nuclear option consistently
has the lowest LCOE.
9.2.5 Oil/gas price shock
Currently the global economy is experiencing a period of relatively low oil prices and this is reflected in the low gas prices being
experienced. The modelling assumes that gas prices by 2030 have recovered from the existing low levels. However, in the timescales
to 2050 there is a risk of an oil/gas price shock occurring similar to that experienced in 1973 and 1979 leading to a large increase in
gas prices. To test this ‘game changer’ event, the LCOE of the different generation options was compared with a 100% increase in
the gas price. This would take time to materialise as the LNG industry would need to develop in order to provide the alternative
market for gas. The impact on the LCOE’s between options is fairly dramatic as seen in Figure 68.
Figure 68: LCOE with oil/gas prick shock increasing fuel prices by 100%
The likelihood of a 100% increase in gas prices may be seen as extreme, but there is historical evidence for such large gas price
movements and scenarios could be envisioned where key countries decided to limit supply. Even a smaller shock of 50% would be
sufficient to make the LCOE values more comparable between generation options.
0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0 200.0
CCGT with CCS Baseload
Small Nuclear
Large Nuclear
CCGT Baseload
LCOE $/MWh
IS3 2050 IS2 2050 BIS 2050 IS3 2030 IS2 2030 BIS 2030
0.0 50.0 100.0 150.0 200.0 250.0 300.0
CCGT with CCS Baseload
Small Nuclear
Large Nuclear
CCGT Baseload
LCOE $/MWh
IS3 2050 IS2 2050 BIS 2050 IS3 2030 IS2 2030 BIS 2030
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9.2.6 Change in capital cost of storage or competing technologies
An alternative way of providing baseload power to the network would be to rely on renewables with sufficient storage that would
enable them to operate like a baseload plant. This is likely to require high levels of storage to deal with their intermittency. However,
depending on cost trajectories and economies of scale there is the potential for renewables/storage combinations to become a
competitive alternative for baseload generation at some point in the future.
An alternative to electricity storage is that technologies like STP or wave power may develop and see costs fall dramatically so that
they could become competitive. These new technologies could therefore become more attractive investments options than nuclear
power.
9.2.7 Electricity market re-design
The analysis within the model is based on electricity prices that reflect the current operation of the NEM. In the timescales
envisioned, it is possible that there could be a re-design of the market in a way that may reward firm capacity. The impact of this on
the viability of the different options is hard to predict as it will impact wholesale electricity prices and the revenue derived from the
generation plants. However, if the CCGTs were viewed as providing a similar level of firm capacity with a lower LCOE then they may
still be a more commercially viable option than nuclear power.
9.2.8 Political desire for nuclear power
The focus of the analysis has been on the commercial viability of nuclear generation. However, there are a number of non-
commercial reasons why a government may decide to support the development of nuclear generators within a country. This could
be due to:
Reduced reliance on imported fuels (unlikely for Australia).
Creation of jobs from the nuclear industry.
Spin off of related nuclear industries.
Additional assistance in meeting climate change targets.
Security of supply for the electricity system.
This Report does not test the validity of these benefits or whether nuclear technology would be the best mechanism to achieve
them, but highlights them as potential justifications for support of a nuclear option.
9.2.9 Combining the game changers
A number of the ‘game changers’ reviewed only partly strengthened the case for nuclear investment and the impact will depend on
the level of the change that is expected. However, it is likely that several of the ‘game changers’ could occur together. As an example
an oil/gas price shock could result in increased construction and expertise of nuclear generation, which reduces the reactor costs in
the medium term. It may also impact global growth and opportunities for investment, which reduces the cost of capital. These
factors combined could lead to a stronger incentive to invest in nuclear technology.
In assessing the potential for nuclear, it is worth considering a number of scenarios that combine these game changers in a way that
may present a strong business case for the nuclear options.
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Dr Mark Diesendorf, Institute of Environmental Studies, UNSW Australia, Response to questions posed in the Nuclear Fuel
Cycle Royal Commission Issues Paper 3: Electricity Generation from Nuclear Fuels.
Dr Shu Fan, Professor Rob J Hyndman, Monash University, Business & Economic Forecasting Unit, Forecasting long-term
minimum half-hourly electricity demand for South Australia, Report for the Australian Energy Market Operator (AEMO), 3 June
2015.
ETSA Utilities, Demand Management Program Final Report, August 2012.
Energy Networks Association (ENA), CSIRO, Electricity Network Transformation Roadmap, Workshop Draft: Refresh of Future
Grid Forum assumptions and scenarios, 2015-25,
http://www.ena.asn.au/sites/default/files/roadmap_interim_program_report.pdf
Ernst & Young, Computational General Equilibrium (CGE) Modelling of Investment in the Nuclear Fuel Cycle, December 2015.
Ethan S. Warner and Gavin A. Heath, Life Cycle Greenhouse Gas Emissions of Nuclear Electricity Generation, Systematic Review
and Harmonisation, © 2012 by Yale University.
Fuel and Technology Cost Review Data, Produced by ACIL Allen for Australian Energy Market Operator.
Frontier Economics, Electricity market forecasts: 2015, A report prepared for the Australian Energy Market Operator (AEMO):
Final, April 2015, http://www.aemo.com.au/Electricity/Planning/Forecasting/National-Electricity-Forecasting-Report/NEFR-
Supplementary-Information
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References
Imperial College Centre for Energy Policy and Technology “Costs Estimates for Nuclear Power in the UK”, August 2012,
https://workspace.imperial.ac.uk/icept/Public/Cost%20estimates%20for%20nuclear%20power%20in%20the%20UK.pdf
Jacobs, Consultation Paper: Modelling illustrative electricity sector emissions reduction policies, Climate Change Authority, 29
May 2015
Jenny Riesz, Claire Sotiriadis, Peerapat Vithayasrichareon, Joel Gilmore, “Quantifying Key Uncertainties in the Costs of Nuclear
Power”.
Kate Marvel and Michael May, American Academy of Arts and Sciences, Game Changers for Nuclear Energy, 2011,
https://www.amacad.org/pdfs/book_game_changers.pdf
Luke Griffiths, The Advertiser, ‘Hot on the trail to make a mark’, October 13, 2015, http://www.pressreader.com/australia/the-
advertiser/20151013/282209419688907/TextView
OECD Nuclear energy Agency, Technical and Economic Aspects of Load Following with Nuclear Power Plants, June 2011.
OECD Nuclear Energy Agency, Alexey Lokhov, Ron Cameron, and Vladislav Sozoniuk, OECD/NEA Study on the Economics and
Market of Small Reactors, September 30, 2013, http://www.kns.org/jknsfile/v45/1-13-58.pdf
Parsons Brinkerhoff, Initial Business Case and Cost Estimates, Quantitative analyses and initial business case – establishing a
nuclear power plant and system in South Australia, 16 September 2015.
Prof John Foster, Dr Liam Wagner, Alexandra Bratanova, Project 3: Economic and Investment Models for Future Grids
“A comparison of the theoretical frameworks and key assumptions”, funded by CSIRO Future Grid Flagship Cluster.
Robert Rowland Dickinson, A submission to the Nuclear Fuel Cycle Royal Commission (NFCRC), Modelling the market value
impact of nuclear power in SA with and without power to fuel, 16 July 2015.
Rocky Mountain Institute, The Economics of Demand Flexibility, How “Flexiwatts” create quantifiable value for customers and
the grid, Published August 2015.
Rocky Mountain Institute, The Economics of Battery Storage, How multi-use, customer-sited batteries deliver the most
services and value to customers and the grid, Published October 2015.
SA Power Networks, Future Operating Model, 2013 -2028.
SA Power Networks, ESCOSA Demand Management Final Report, February 2015.
W Hou, G Allinson, I MacGill, PR Neal, MT Ho (2014), ‘Cost comparison of major low-carbon electricity generation options: an
Australian case study’, Sustainable Energy Technologies and Assessments, 8:131–148 referenced in Australian Power
Generation Technology Report, November 2015.
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References
http://energyrealityproject.com/lets-run-the-numbers-nuclear-energy-vs-wind-and-solar/
http://nuclearrc.sa.gov.au/app/uploads/2015/11/Topic-5-Presentation-Session-4.pdf
http://www.aemc.gov.au/getattachment/42a1dfd9-bf32-4bf1-bcc4-81dd8095dfc7/Final-Report-Appendix-A-An-introduction-
to-congest.aspx
http://www.electranet.com.au/network/national-electricity-market/
APPENDICES
FOR THE
QUANTITATIVE VIABILITY ANALYSIS OF
ELECTRICITY GENERATION FROM NUCLEAR
FUELS
Nuclear Fuel Cycle Royal Commission
Authored by: DGA Consulting Carisway
Date of issue: 05/02/2016
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Nuclear Fuel Cycle Royal Commission – Appendix Report
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Document Control
Customer Details
Customer Name: Nuclear Fuel Cycle Royal Commission
Customer Address: Level 5, 50 Grenfell Street,
Adelaide, SA, 5000
Contact Person: Ashok Kaniyal
About this Document
Title: Appendix Report
Date of Issue: 5/02/2016
Prepared by: Dave Lenton (DL)/Robert Riebolge (RR)
Approved by: Dave Lenton (DL)/Robert Riebolge (RR)
Rev No. Date Reason for Issue Updated by Verified by
0.1 21/12/15 Draft for Review DL/RR DL/RR
0.2 21/01/16 Recast Draft for Review DL/RR DL/RR
1.0 27/01/16 Final Report DL/RR
2.0 05/02/16 Final Report with Scenario 4 DL/RR
Confidentiality
This Report may contain information that is business sensitive to DGA Consulting/Carisway or the Nuclear Fuel Cycle
Royal Commission (NFCRC). No part of this Report may be used, duplicated or disclosed for any purpose unless by
express consent of the Nuclear Fuel Cycle Royal Commission. As such the use of the information in this Report is
regarded as an infringement of DGA Consulting/Carisway’s intellectual property rights.
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CONTENTS
Appendix A – Demand Projecting ........................................................................................................................... 3
Appendix B – Model Projecting Variables ............................................................................................................... 5
Appendix C – Electric Vehicles (EV) Load .............................................................................................................. 9
Appendix D – Renewables .................................................................................................................................... 11
Appendix E – Power Release Rules ..................................................................................................................... 13
Appendix F – Generation Dispatch ....................................................................................................................... 19
Appendix G – Key Data Inputs .............................................................................................................................. 24
Appendix H – Sensitivity Charts For All Options ................................................................................................... 41
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APPENDIX A – DEMAND PROJECTING
All scenarios
For historic trending of the individual categories of demand to inform on the likely composition of the respective individual
categories of demand making up the overall South Australian system demand in 2030 and 2050 the scaling function is of the form:
𝐹𝑉 = 𝑃𝑉(1 + 𝑔𝑟)𝑛𝑦
where:
𝐹𝑉 = the future value of the HH data point in year 𝑛𝑦
𝑃𝑉 = the selected historic value of the HH data point in year 0
𝑔𝑟 = annual growth rate in (%)
𝑛𝑦 = time period (yrs)
HH demand points for the total demand on the system in 2030 and 2050 can then be expressed as:
𝑑𝑖,𝑇 = 𝑏𝑢𝑠𝑖,𝑇 + 𝑟𝑒𝑠𝑖,𝑇 + 𝑚𝑗𝑐𝑖,𝑇 + ℎ𝑤𝑖,𝑇 + 𝑒𝑣𝑖,𝑇
where:
𝑇 = scenario time horizon (i.e. 2030 and 2050)
𝑑𝑖,𝑇 = total system demand at the 𝑖𝑡ℎ HH in horizon T
𝑏𝑢𝑠𝑖,𝑇 = business demand at the 𝑖𝑡ℎ HH in horizon T
𝑟𝑒𝑠𝑖,𝑇 = residential demand at the 𝑖𝑡ℎ HH in horizon T
𝑚𝑗𝑐𝑖,𝑇 = major customer demand at the 𝑖𝑡ℎ HH in horizon T
ℎ𝑤𝑖,𝑇 = hot water load at the 𝑖𝑡ℎ HH in horizon T
𝑒𝑣𝑖,𝑇 = electric vehicle load at the 𝑖𝑡ℎ HH in horizon T
Analysis of historic data for the South Australian network reveals that the issue of peak demand is a factor for only a small
proportion of the year, typically occurring in summer months and lasting less than 100 hours of the year1. Therefore the matter of
peak demand has very little impact on the energy consumption, which is the summation of HH demand, including the peak, for an
entire year and is expressed as:
𝑆𝑌𝑆𝑇 = ( ∑ 𝑑𝑖,𝑇
17,520
𝑖=1
)/2
where:
𝑆𝑌𝑆𝑇 = Annual system energy consumed at time horizon T
1 For the purpose of analysis it is assumed that a year consists of 8,760 hours.
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𝑑𝑖,𝑇 = HH demand at the 𝑖𝑡ℎ HH at time horizon T
𝑖 = 𝑖𝑡ℎ HH interval
Technology scenario TS1 (ii) of the SoW2 calls for medium level of growth in demand (see DS3 of the SoW for sensitivity case) that
reflects penetration of energy efficient co/tri generation electricity and district heating/cooling technologies leading to
commensurate decline in commercial energy consumption. To do this, the residential demand is amended by phase shifting the
hot water load parameter3 by 18:30 hours (i.e. 37 HH intervals) and deducting it from the residential demand as follows:
𝑐𝑜𝑔𝑖,𝑇 = 𝑟𝑒𝑠𝑖,𝑇 − ℎ𝑤𝑖+37,𝑇
where:
𝑐𝑜𝑔𝑖,𝑇 = cogeneration/tri generation substitution at the 𝑖𝑡ℎ HH in horizon T
𝑟𝑒𝑠𝑖,𝑇 = residential demand at the 𝑖𝑡ℎ HH in horizon T
ℎ𝑤𝑖+37,𝑇 = co/tri generation at the 𝑖𝑡ℎ HH in horizon T
2 Statement of Work for the Contract to Provide Services - Quantitative Viability Assessment of Generating Electricity from Nuclear Fuels.
3 Phase shifted hot water load is a good proxy for heat in a co-generation system.
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APPENDIX B – MODEL PROJECTING VARIABLES
2050 time horizon
Demand variables for the 2050 time horizon have been sourced from various entities, published materials and the work of other
advisers to the Commission, as per Table B1.
Table B1: Source and derivation of demand variables for 2050
2050 time horizon
Category Variable Value Comment
Business category
High 1.00% pa Estimate by DGA Consulting/Carisway. Projected from
2013 to 2050.
Low -0.96% pa EY’s IS3 - Strong carbon/climate action scenario4.
Projected from 2016 to 2050.
Medium 0.02% pa EY's IS2 - Moderate carbon/climate action scenario5.
Projected from 2016 to 2050.
Residential category
High 1.00% pa Estimate by DGA Consulting/Carisway. Projected from
2013 to 2050.
Low -0.96% pa EY’s IS3 - Strong carbon/climate action scenario6.
Projected from 2016 to 2050.
Medium 0.02% pa EY's IS2 - Moderate carbon/climate action scenario7.
Projected from 2016 to 2050.
Major customer category
High 0.50% pa
Assumes a dry fluoride conversion and laser enrichment
facility of a total of 33MWe additional to the existing
major customer demand as informed by Jacobs.
Projected from 2013 to 2050.
Low 0.10% pa Estimate by DGA Consulting/Carisway. Projected from
2013 to 2050.
Medium 0.20% pa Estimate by DGA Consulting/Carisway. Projected from
2013 to 2050.
4 Figures derived from the updated IS3 – strong climate change/action policy scenario. Ernst & Young, Computational General Equilibrium (CGE) Modelling of Investment in the Nuclear Fuel Cycle.
5 Figures derived from Ernst & Young, Computational General Equilibrium (CGE) Modelling of Investment in the Nuclear Fuel Cycle.
6 ibid.
7 ibid.
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2050 time horizon
Category Variable Value Comment
Hot water load
High 0.10% pa Estimate by DGA Consulting/Carisway. Projected from
2013 to 2030.
Low -0.20% pa Value has been informed from the trend line exhibited in
SA Power Networks’ historic data sets8.
Medium -0.10% pa Value has been sourced from SA Power Networks
historic data sets9.
Co generation Yes
Assumes co generation has taken hold in the market and
is equivalent to the hot water load phase shifted by
18:30 hours (i.e. 37 HH intervals) and deducted from the
residential demand.
No No impact on the demand from co generation.
Electric vehicle market
penetration
5%
Value has been sourced from SA Power Networks,
ESCOSA Demand Management Final Report, February
201510.
32% CSIRO, Future Grid Forum, Leaving the grid scenario11.
55% EY’s IS3 - Strong carbon/climate action scenario12.
80% CSIRO, ClimateWorks13.
Technology variables for the 2050 time horizon have been sourced from various entities, published materials and the work of
other advisers to the Commission, as per Table B2.
8 Note: Long term downward trend of the peak in hot water load has been significantly greater than the assumed value (as much as 4% per annum between 2002/03 and 2012/13). However the hot water load does not materially impact the calculations of the NPV so that the assumed decline is considered reasonable.
9 ibid
10 60,000 light electric vehicles in 2028 representing 5% of the market in South Australia. One transformer services 50 EV's with 5 transformers in the sample (c.f. IDS Analytics). Scaling factor of 240 is applied to the IDS Analytics sample data (c.f. Appendix C) yielding 555GWh of demand from EVs by 2050. The scaling factor for the 5% market penetration is used to scale up the other variable percentage penetrations.
11 A light electric fleet is assumed. CSIRO - FGF, leaving the grid scenario, estimates EV annual demand of 3,550GWh by 2050. Hence market penetration of (3,550/555)*5% = 32%.
12 A light electric fleet is assumed. CSIRO - EY’s IS3 scenario, estimates EV annual demand of 6,137GWh by 2050. Hence market penetration of (6,137/555)*5% = 55%.
13 A light electric fleet is assumed. CSIRO/ClimateWorks, estimates EV annual demand of 8,880GWh by 2050. Hence market penetration of (8,880/555)*5% = 80%.
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Table B2: Source and derivation of technology variables for 2050
2050 time horizon
Category Variable Value Comment
Photovoltaics
Business category
High 80% Estimate based on AEMO, Detailed Summary of 2015
Electricity Forecasts, June 2015, pp53, for 2030.
Low 40% Estimate by DGA Consulting/Carisway.
Medium 60%
Agrees with EY's BIS - Baseline climate change policy
scenario, IS2 - Moderate climate change/action policy
scenario & IS3 - Strong climate change/action policy
scenario.
Photovoltaics paired with storage
PV paired with battery
storage
High 80%
Estimate based on breakthrough battery technologies
such as those being worked on by Tesla14 for EVs that
can be transposes to in the home battery packs.
Low 50% Estimate by DGA Consulting/Carisway.
Medium 60% Estimate by DGA Consulting/Carisway.
Wind generation
Wind paired with grid
storage
High 80%
Assumes rapid uptake of grid storage technologies to
deal with issues of grid instability caused by intermittent
wind generation.
Low 20% Estimate by DGA Consulting/Carisway.
Medium 60%
Low-cost and highly scalable grid storage systems are
currently being trialed that can be scaled up from 500kW
to large scale applications in the hundreds of
megawatts15,16.
Wind installed capacity
Wind installed capacity High 4,421MW AEMO, SA Fuel and Technology Report, proposes the
potential for an additional 3,107MWe of wind generation
projects across South Australia17.
Low 1,314MW Estimated figure18.
Medium 3,000MW Estimate by DGA Consulting/Carisway.
14 https://www.teslamotors.com/en_AU/powerwall
15 Hughes Public Relations, News Release, Sand May Provide Energy Storage Solution, Adelaide firm bolstered by grant to commercialise concept, October 13, 2015.
16 Luke Griffiths, The Advertiser, ‘Hot on the trail to make a mark’, October 13 2015, pp23 & 25 – “Another business to have received AC funding is Latent Heat Storage (LHS), which has patented a low-cost and highly scalable thermal energy storage system (TESS) based on the latent heat properties of silicon derived from sand. It differentiates from competing technologies because of its scalability, from small scale 500kW applications through to large scale applications in the hundreds of megawatts.
17 http://bit.ly/1LqrPpv
18 1,314MW is an estimate of the maximum 5 minute generation (c.f. AEMO web site for conversion data) for an installed capacity of 1,473MW.
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2050 time horizon
Category Variable Value Comment
Solar Thermal Plant (STP)
Installed capacity
High 450MW
Agrees with EY's BIS - Baseline climate change policy
scenario, IS2 - Moderate climate change/action policy
scenario & IS3 - Strong climate change/action policy
scenario.
Low 0MW No STP plant.
Medium 280MW
AEMO, SA Fuel and Technology Report (2015) notes that
Arizona’s largest public utility recently commissioned a
solar array with a maximum output of 280MWe with
6 hours of molten salt storage.
Nuclear Plant
Switch Yes 1 Nuclear installation.
No 0 CCGT or CCGT with CCS installation.
Installation Low 285MWe Parsons Brinkerhoff, nuScale 6 x 47.5MWe reactors.
High 1,125MWe Parsons Brinkerhoff, AP1000 reactor.
Interconnector constraint
Installed capacity
High 650MW Capacity upgrade by 2016.
Low 2,000MW NFCRC relaxed constraint.
Medium 1,180MW Sourced from Dr Mark Diesendorf19.
Vehicle to Grid (V2G)
Percentage of electric
vehicles with V2G
installations
High 80% Discussions with NFCRC.
Low 40% Discussions with NFCRC.
Medium 65% Discussions with NFCRC.
19 Dr Mark Diesendorf, Institute of Environmental Studies, UNSW Australia, Response to questions posed in the Nuclear Fuel Cycle Royal Commission Issues Paper 3: Electricity Generation from Nuclear Fuels.
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APPENDIX C – ELECTRIC VEHICLES (EV) LOAD
Load profiles
Historic HH load data indicates that demand from centralised sources (i.e. major customers) remains relatively unchanged, both
temporally and seasonally. Step changes in demand (i.e. new base load demands) are therefore readily accommodated in the
forecast time horizons of 2030 and 2050 through discussions with respective subject matter experts (e.g. the dry fluoride
conversion and laser enrichment facility) who are able to inform on the likely quanta of demand to be serviced and the timing for
it to come on stream.
EV load, on the other hand, is dependent on the time users choose to charge their vehicles. Key considerations to determining the
impact of EV charging are therefore (i) the rate of uptake of EVs in the community and (ii) the number of EVs being charged. To
determine the impact of EVs on the network HH data of daily EV loads on the South Australian grid was sourced from a study
commissioned by SA Power Networks of the impact of this load on low voltage (LV) transformers carried out by ISD Analytics20. In
preparing their report, ISD Analytics estimated the rate of EV uptake and the load that this would present on LV transformers.
IDS Analytics produced a set of transformer load diagrams for four Postcodes as follows: 5109 (including Brahma Lodge); 5024
(including Fulham); 5049 (including Seacliff); and 5066 (including Beaumont).
In producing this data set, ISD Analytics considered three scenarios: Base; High; and Base with ToU. Mean and maximum
transformer loads, assuming each transformer supplied 50 household units, for each of these scenarios were projected over three
time horizons: 2020; 2030; and 2040 for both a week day (i.e. Wednesday) and weekend day (i.e. Saturday).
The traces in Figure C1 plot the maximum EV load on transformers for the High Load Scenario in 2020, 2030 and 2040 at HH
intervals. The traces indicate an almost doubling in growth in peak demand on LV transformers from 270kW in 2020 to 500kW in
2030 and further 40% rise to 700kW in 2040.
Figure C1: HH week day (Wednesday) High Load Scenario
The HH traces in Figure C2 summarise the maximum daily load duration curves by Postcode for the 2030 High Load Scenario. Also
shown is the maxima and minima load envelopes. The maxima envelope can be used to assess the impact of the EV load on the
South Australian grid and the minima to derive the release of controlled power from EV storage via V2G discussed in Appendix E.
20 SA Power Networks, ESCOSA Demand Management Final Report, February 2015, pp91-94.
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Figure C2: HH summary of transformer load by Postcode for the 2030 High Load Scenario together with maxima and minima load envelopes
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APPENDIX D – RENEWABLES
Photovoltaics (PV)
Detailed analysis of the HH data sets for PV generation clearly highlighted that although the peak of the scaled up gross PV output
is not coincident with the peak of the residential demand, it comes close to meeting the residential peak demand in summer but
not in winter. The bar chart in Figure D1 is of the expected value E(x) of a sunshine index and its probability of occurrence P(x) for
each month for the period from 2009/10 to 2012/13. The plot highlights that:
January is likely to have the greatest amount of sunshine with the highest probability of occurrence;
November, December and February have significant sunshine but with reduced probability of occurrence; and
May and June have least sunshine and even that is at risk of cloud cover.
Figure D1: Expected Value E(x) of the Sunshine Index and its Probability of Occurrence P(x) for the period 2009/10 to 2012/13
The traces in Figure D2 highlight the impact of unmitigated scaled up gross PV output on the system as a whole for a summer
month. Also illustrated are the negative values that can occur on a transformer with 100% PV penetration measured at 5 minute
intervals.
Figure D2: HH traces of system demand, scaled up gross PV output and grid balance for one week in summer (January 2013) and daily load traces on a transformer with 100% PV penetration
measured at 5 minute intervals
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Wind generation
A detailed analysis of HH historic wind data sets enabled a calculation of the expected value E(x) of the wind index in a respective
month to be made. This confirmed the seasonal relationship of wind intensity in South Australia with August, September and
October likely to be months with greatest wind intensity. The plot in Figure D3 of the expected value of the wind index E(x) and its
probability of occurrence P(x) for each month for a twelve month period from Jun-13 to May-14 at locations A and B confirmed
that: August is likely to have the greatest wind intensity but with the lowest probability of occurrence; June, March, April and May
have the least wind intensity with the highest probability of occurrence and other months have significant wind intensity but with
variable probability of occurrence.
Figure D3: Expected Value and Probability of Occurrence of Wind Index21 at location A and location B in 2013/14
As well as seasonal wind intensity variation, wind generation output is subject to locational wind variation. Wind generation in
South Australia contributes significantly to the generation mix. However, like PV, it is intermittent. But also, like PV, its value is
greatly enhanced if it can be stored and released when needed. Research and development currently underway into grid storage
looks promising and may see it becoming a reality in the near to medium term (i.e. 3 to 10 years).
21 SA Power Networks, ESCOSA Demand Management Final Report, February 2015, pp16.
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APPENDIX E – POWER RELEASE RULES
PV paired with batteries
Clearly, it is not possible to simulate perfectly the environment into which power from storage is released, however for the
purpose of generation dispatch, a very close approximation of optimal power release from storage for PV paired with batteries is
as shown in the Figures below for a peak demand day occurring in a ten day cycle in a mid-spring month (i.e. October).
Figure E1: HH customer demand and PV power output
Figure E2: HH surplus PV output fed back to the grid after demand has been met by PV generation
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Figure E3: HH surplus PV paired with batteries stored and thereafter released
Figure E4: HH exploded view of Figure B3 for the first 2 days of the 10 day cycle
Figure E5: HH DV paired with batteries released in proportion to demand using one day storage
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Inspection of the above Figures visually highlights the impact of battery storage and an optimal power release rule. Figure E1
compares a customer’s actual demand (blue shaded area) in mid spring to power generated from a PV installation of 5kW at
HH intervals. If the power generated by the PV installation is used to meet the customer’s demand (as is currently the case) then
for periods when there is surplus PV generation, this power is either spilled or fed back into the grid (beige shaded area) as shown
in Figure E2. Notice that this scenario does not address the issue of peak demand and can create grid instability particularly as the
aggregate installed PV capacity increases in the residential and business marketplace.
One way of addressing peak demand and grid instability is to pair PV with batteries. Figure E3 shows traces of demand (blue
shaded area) and surplus PV power stored in batteries (green shaded area) and immediately released power to the grid when PV
generation ceases at sunset. The resultant power supplied by the grid (beige shaded area) now no longer gives rise to the same
grid instability as was the case without battery storage but it does not deal with the issue of peak demand as highlighted in
Figures E3 and E4. The optimal release pattern is as shown in Figure E5. This assumes that battery storage technology will be
developed to the point where an entire day’s PV output can be stored and algorithms for power release can be formulated that
release all of the previous day’s stored power in proportion to the next day’s power demand. Notice that there is virtually no
surplus power fed back to the grid and the issue of peak demand has been dealt with (beige shaded area in Figure E5). This release
rule is formulated in more detail below.
HH generation and demand for the total PV generation stored in one day and total demand for one day can then be expressed as:
𝑃𝑉𝑗 = ∑ 𝑝𝑣𝑖,𝑗
48
1
𝐷𝑗 = ∑ 𝑑𝑖,𝑗
48
1
where i ranges from 1 to 48 HH and j is one of 365 days and:
𝑃𝑉𝑗 = Total energy stored in day j
𝑝𝑣𝑖,𝑗 = The PV power output at the 𝑖𝑡ℎ HH of day j
𝐷𝑗 = Total energy demand in day j
𝑑𝑖,𝑗 = The power demand at the 𝑖𝑡ℎ HH of the day j
The rule for releasing power 𝑝𝑣𝑠𝑖,𝑗+1from storage is then expressed as:
𝑝𝑣𝑠𝑖,𝑗+1 = (∑(𝑝𝑣𝑖,𝑗)/
48
𝑖=1
∑(𝑑𝑖,𝑗+1)) ∗
48
𝑖=1
𝑝𝑣𝑖,𝑗+1
or
𝑝𝑣𝑠𝑖,𝑗+1 = (𝑃𝑉𝑗
𝐷𝑗+1) ∗ 𝑝𝑣𝑖,𝑗+1
where i ranges from 1 to 48 HH and j is one of 365 days and:
𝐷𝑗+1 = Total energy demand in day j+1
𝑑𝑖,𝑗+1 = The power demand at the 𝑖𝑡ℎ HH on day j+1
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Wind paired with grid storage
To model the power released from grid storage, it is assumed that raw wind generated output feeding into South Australian grid is
stored for a period of time in grid storage and released in such a way that it follows the total system demand including EV load.
The graphs in Figure E6 present traces plotted HH for a month in summer (January) and winter (July) of the average of the HH
traces at two locations scaled up from their respective maximum 5 minute generation output to that of the total maximum 5
minute generation output for the installed capacity in the grid of 1,314MW22. The graphs also show the traces of the power
release when wind is paired with grid storage for the same period.
Figure E6: HH scaled up trace of wind generation and wind power released to the grid when paired with grid storage for a month in summer (January) and winter (July)
22 c.f. Table 8, Section 3.2.2 of the Report.
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Comparing the traces above it is seen that wind paired with grid storage (beige line) more closely follows the system demand than
the raw wind generation (black line) in both summer and winter months thus removing much of the intermittency and
unpredictability of wind generation.
Similarly to PV paired with batteries, wind generation paired with grid storage is dispatched according to the rule set out below.
Wind generation and system demand for the total wind generation stored in a period of time23 and total demand for the same
period of time and can then be expressed as:
𝑊𝑗 = ∑ 𝑤𝑖𝑛𝑑𝑜𝑖,𝑗
48
𝑖=1
𝐷𝑗 = ∑ 𝑑𝑖,𝑗
48
𝑖=1
where i ranges from an assumed 1 to 48 HH interval and j is a period of days:
𝑊𝑗 = Total wind energy stored in period j
𝑤𝑖𝑛𝑑𝑜𝑖,𝑗 = The wind power output at the 𝑖𝑡ℎ HH of period j
𝐷𝑗 = Total energy demand in time period j
𝑑𝑖,𝑗 = The power demand at the 𝑖𝑡ℎ HH of period j
The rule for releasing power 𝑤𝑖𝑛𝑑𝑠𝑖,𝑗+1 from storage is then expressed as:
𝑤𝑖𝑛𝑑𝑠𝑖,𝑗+1 = (∑(𝑤𝑖𝑛𝑑𝑜𝑖,𝑗)/
48
𝑖=1
∑(𝑑𝑖,𝑗+1)) ∗
48
𝑖=1
𝑤𝑖𝑛𝑑𝑜𝑖,𝑗+1
or
𝑤𝑖𝑛𝑑𝑠𝑖,𝑗+1 = (𝑊𝑗
𝐷𝑗+1) ∗ 𝑤𝑖𝑛𝑑𝑜𝑖,𝑗+1
Where i ranges from 1 to 48 HH intervals and j is a period of days and:
𝑤𝑖𝑛𝑑𝑠𝑖,𝑗+1 = Power released from storage at the 𝑖𝑡ℎ HH in period j+1
𝐷𝑗+1 = Total energy demand in period j+1
𝑑𝑖,𝑗+1 = The power demand at the 𝑖𝑡ℎ HH in period j+1
Vehicle to Grid (V2G)
For power release from EV batteries connected to the grid it is assumed that only premises with vehicle to grid installations will be
able to make use of this opportunity. In order to assess the amount of energy available for release, the minima load profile of the
graph illustrated in Figure C2 of Appendix C is assumed. This stored energy is then released applying a similar rule to that of PV and
wind release discussed above. Release of electric vehicle storage can then be dispatched according to the rule set out below.
23 This is dependent on the grid storage technology under consideration.
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𝐸𝑉𝑂𝑆𝑗 = ∑ 𝑒𝑣𝑜𝑠𝑖,𝑗
48
𝑖=1
𝐷𝑗 = ∑ 𝑑𝑖,𝑗
48
𝑖=1
where i ranges from 1 to 48 HH intervals and j is a period of days:
𝐸𝑉𝑂𝑆𝑗 = Total energy stored in EV batteries available for release via V2G in period j
𝑒𝑣𝑠𝑜𝑖,𝑗 = The EV battery power output available for release via V2G at the 𝑖𝑡ℎ HH of period j
𝐷𝑗 = Total energy demand in time period j
𝑑𝑖,𝑗 = The power demand at the 𝑖𝑡ℎ HH of period j
The rule for releasing power 𝑒𝑣𝑠𝑖,𝑗+1 from storage is then expressed as:
𝑒𝑣𝑠𝑖,𝑗+1 = (∑(𝑒𝑣𝑠𝑜𝑖,𝑗)/
48
𝑖=1
∑(𝑑𝑖,𝑗+1)) ∗
48
𝑖=1
𝑒𝑣𝑠𝑜𝑖,𝑗+1
or
𝑒𝑣𝑠𝑖,𝑗+1 = (𝐸𝑉𝑆𝑂𝑗
𝐷𝑗+1) ∗ 𝑒𝑣𝑠𝑜𝑖,𝑗+1
Where i ranges from 1 to 48 HH intervals and j is a period of days and:
𝑒𝑣𝑠𝑖,𝑗+1 = Power released from storage at the 𝑖𝑡ℎ HH in period j+1
𝐷𝑗+1 = Total energy demand in period j+1
𝑑𝑖,𝑗+1 = The power demand at the 𝑖𝑡ℎ HH in period j+1
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APPENDIX F – GENERATION DISPATCH
As discussed in Section 4.2 of the Report, generating plant is dispatched with nuclear in load following mode and base load mode.
At any time interval 𝑖 the total dispatch must comply with the following objective function:
(( ∑ 𝑛𝑢𝑐𝑖
17,520
𝑖=1
) 2⁄ + ( ∑ 𝑒𝑛𝑢𝑐𝑖
17,520
𝑖=1
) 2⁄ ) = 𝑀𝑎𝑥𝑖𝑚𝑢𝑚
where:
𝑛𝑢𝑐𝑖 < 𝑁𝑈𝐶 = nuclear generation supplying the SA grid in the 𝑖𝑡ℎ HH period such that it is less than the installed
capacity (𝑁𝑈𝐶) of the plant for the variables chosen for the scenario being considered
𝑒𝑛𝑢𝑐𝑖 < 𝑁𝑈𝐶 = nuclear generation exported to the NEM in the 𝑖𝑡ℎ HH period such that it is less than the installed
capacity (𝑁𝑈𝐶) of the plant for the variables chosen for the scenario being considered
Subject to the following constraints:
𝐺𝑆𝑖 = 𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑒𝑣𝑠𝑖 + 𝑠𝑡𝑝𝑖 + 𝑛𝑢𝑐𝑖 ≤ 𝑆𝑌𝑆𝑖
where
𝐺𝑆𝑖 = Power supplied to the SA grid in the 𝑖𝑡ℎ HH period
𝑝𝑣𝑜𝑖 < 𝑃𝑉𝑂 = Photovoltaic generation supplying the SA grid in the 𝑖𝑡ℎ HH period such that it is less than the
installed capacity (𝑃𝑉𝑂) of PV generation for the variables chosen for the scenario being considered
𝑝𝑣𝑠𝑖 < 𝑃𝑉𝑆 = Photovoltaic generation paired with batteries supplying the SA grid in the 𝑖𝑡ℎ HH period such that it
is less than the installed capacity (𝑃𝑉𝑆) of PV generation paired with batteries for the variables chosen
for the scenario being considered
𝑤𝑖𝑛𝑑𝑜𝑖 < 𝑊𝑂 = Wind generation supplying the SA grid in the 𝑖𝑡ℎ HH period such that it is less than the installed
capacity (𝑊𝑂) of wind generation for the variables chosen for the scenario being considered
𝑤𝑖𝑛𝑑𝑠𝑖 < 𝑊𝑆 = Wind generation paired with grid storage supplying the SA grid in the 𝑖𝑡ℎ HH period such that it is
less than the installed capacity (𝑊𝑆) of wind generation paired with grid storage for the variables chosen
for the scenario being considered
𝑒𝑣𝑠𝑖 < 𝐸𝑉𝑆 = V2G release supplying the SA grid in the 𝑖𝑡ℎ HH period such that it is less than the available EV
storage capacity (𝐸𝑉𝑆) and V2G availability for the variables chosen for the scenario being considered
𝑠𝑡𝑝𝑖 < 𝑆𝑇𝑃 = Solar Thermal Plant generation supplying the SA grid in the 𝑖𝑡ℎ HH period such that it is less than the
installed capacity (𝑆𝑇𝑃) of the plant for the variables chosen for the scenario being considered
𝑆𝑌𝑆𝑖 = Total system demand in South Australia in the 𝑖𝑡ℎ HH period
in addition to the above constraints, generating plant dispatch must also comply with the dispatch constraints as depicted in
Table F1 when supplying the South Australian grid.
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Table F1: Boundary conditions for power supplied to the South Australian grid
Power supplied to the South Australian grid
Dispatch
rank
Power dispatch
(nuclear in last dispatch mode)
Boundary conditions
1 PV only 𝐼𝐹 (𝑟𝑎𝑤 𝑝𝑣𝑜𝑖 > 𝑆𝑌𝑆𝑖 𝑡ℎ𝑒𝑛 (𝑝𝑣𝑜𝑖 = 𝑆𝑌𝑆𝑖), (𝑝𝑣𝑜𝑖 =
𝑟𝑎𝑤 𝑝𝑣𝑜𝑖))
2 PV paired with DS that follows the system load profile 𝐼𝐹 ((𝑝𝑣𝑜𝑖 + 𝑟𝑎𝑤 𝑝𝑣𝑠𝑖) > 𝑆𝑌𝑆𝑖 𝑡ℎ𝑒𝑛 𝑆𝑌𝑆𝑖 , (𝑝𝑣𝑜𝑖 +
𝑟𝑎𝑤 𝑝𝑣𝑠𝑖))
and
𝑝𝑣𝑠𝑖 = (𝑝𝑣𝑜𝑖 + 𝑟𝑎𝑤 𝑝𝑣𝑠𝑖) − (𝑝𝑣𝑜𝑖)
3 Wind only 𝐼𝐹 ((𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑟𝑎𝑤 𝑤𝑖𝑛𝑑𝑜𝑖) >
𝑆𝑌𝑆𝑖 𝑡ℎ𝑒𝑛 𝑆𝑌𝑆𝑖 , (𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑟𝑎𝑤 𝑤𝑖𝑛𝑑𝑜𝑖))
and
𝑤𝑖𝑛𝑑𝑜𝑖 = (𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑟𝑎𝑤 𝑤𝑖𝑛𝑑𝑜𝑖) − (𝑝𝑣𝑜𝑖 +
𝑝𝑣𝑠𝑖)
4 Wind paired with grid storage that follows the system
load profile
𝐼𝐹 ((𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑟𝑎𝑤 𝑤𝑖𝑛𝑑𝑠𝑖) >
𝑆𝑌𝑆𝑖 𝑡ℎ𝑒𝑛 𝑆𝑌𝑆𝑖 , (𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑤𝑖𝑛𝑑𝑜𝑖 +
𝑟𝑎𝑤 𝑤𝑖𝑛𝑑𝑠𝑖))
and
𝑤𝑖𝑛𝑑𝑠𝑖 = (𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑟𝑎𝑤 𝑤𝑖𝑛𝑑𝑠𝑖) −
(𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑤𝑖𝑛𝑑𝑜𝑖)
5 V2G EV release that follows the system load profile 𝐼𝐹 ((𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑟𝑎𝑤 𝑒𝑣𝑠𝑖) >
𝑆𝑌𝑆𝑖 𝑡ℎ𝑒𝑛 𝑆𝑌𝑆𝑖 , (𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑤𝑖𝑛𝑑𝑠𝑖 +
𝑟𝑎𝑤 𝑒𝑣𝑠𝑖))
and
𝑒𝑣𝑠𝑖 = (𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑟𝑎𝑤 𝑒𝑣𝑠𝑖)
− (𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑤𝑖𝑛𝑑𝑜𝑖
+ 𝑤𝑖𝑛𝑑𝑠𝑖)
6 STP 𝐼𝐹 ((𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑒𝑣𝑠𝑖 +
𝑟𝑎𝑤 𝑠𝑡𝑝𝑖) > 𝑆𝑌𝑆𝑖 𝑡ℎ𝑒𝑛 𝑆𝑌𝑆𝑖 , (𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑤𝑖𝑛𝑑𝑜𝑖 +
𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑒𝑣𝑠𝑖 + 𝑟𝑎𝑤 𝑠𝑡𝑝𝑖))
and
𝑠𝑡𝑝𝑖 = (𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑒𝑣𝑠𝑖 +
𝑟𝑎𝑤 𝑠𝑡𝑝𝑖) − (𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑒𝑣𝑠𝑖)
7 Nuclear or the CCGT alternative 𝐼𝐹 ((𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑒𝑣𝑠𝑖 + 𝑠𝑡𝑝𝑖 +
𝑟𝑎𝑤 𝑛𝑢𝑐𝑖) > 𝑆𝑌𝑆𝑖 𝑡ℎ𝑒𝑛 𝑆𝑌𝑆𝑖, (𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 +
𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑒𝑣𝑠𝑖 + 𝑠𝑡𝑝𝑖 + 𝑟𝑎𝑤 𝑛𝑢𝑐𝑖))
and
𝑛𝑢𝑐𝑖 = (𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑒𝑣𝑠𝑖 +
𝑠𝑡𝑝𝑖 + 𝑟𝑎𝑤 𝑛𝑢𝑐𝑖) − (𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑤𝑖𝑛𝑑𝑜𝑖 +
𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑒𝑣𝑠𝑖 + 𝑠𝑡𝑝𝑖)
8 Fossil fuels that are required to meet any generation
shortfall in the SA grid
𝐼𝐹((𝐺𝑆𝑖 = 𝑝𝑣𝑜𝑖 + 𝑝𝑣𝑠𝑖 + 𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑒𝑣𝑠𝑖 +
𝑠𝑡𝑝𝑖 + 𝑛𝑢𝑐𝑖) ≤ 𝑆𝑌𝑆𝑖)), (𝑓𝑜𝑠𝑠𝑖 = (𝑆𝑌𝑆𝑖 − 𝐺𝑆𝑖)),0)
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where:
𝑟𝑎𝑤 𝑝𝑣𝑜𝑖 < 𝑃𝑉𝑂 = unconstrained photovoltaic generation supplying the SA grid in the 𝑖𝑡ℎ HH period such that it
is less than the installed capacity (𝑃𝑉𝑂) of PV generation for the variables chosen for the scenario being
considered
𝑟𝑎𝑤 𝑝𝑣𝑠𝑖 < 𝑃𝑉𝑆 = unconstrained photovoltaic generation paired with batteries supplying the SA grid in the
𝑖𝑡ℎ HH period such that it is less than the installed capacity (𝑃𝑉𝑆) of PV generation paired with batteries
for the variables chosen for the scenario being considered
𝑟𝑎𝑤 𝑤𝑖𝑛𝑑𝑜𝑖 < 𝑊𝑂 = unconstrained wind generation supplying the SA grid in the 𝑖𝑡ℎ HH period such that it is less
than the installed capacity (𝑊𝑂) of wind generation for the variables chosen for the scenario being
considered
𝑟𝑎𝑤 𝑤𝑖𝑛𝑑𝑠𝑖 < 𝑊𝑆 = unconstrained wind generation paired with grid storage supplying the SA grid in the 𝑖𝑡ℎ HH
period such that it is less than the installed capacity (𝑊𝑆) of wind generation paired with grid storage for
the variables chosen for the scenario being considered
𝑟𝑎𝑤 𝑒𝑣𝑠𝑖 < 𝐸𝑉𝑆 = unconstrained V2G release supplying the SA grid in the 𝑖𝑡ℎ HH period such that it is less than
the available EV storage capacity (𝐸𝑉𝑆) and V2G availability for the variables chosen for the scenario
being considered
𝑟𝑎𝑤 𝑠𝑡𝑝𝑖 < 𝑆𝑇𝑃 = unconstrained Solar Thermal Plant generation supplying the SA grid in the 𝑖𝑡ℎ HH period such
that it is less than the installed capacity (𝑆𝑇𝑃) of the plant for the variables chosen for the scenario being
considered
𝑟𝑎𝑤 𝑛𝑢𝑐𝑖 < 𝑁𝑈𝐶 = unconstrained Nuclear Plant generation supplying the SA grid in the 𝑖𝑡ℎ HH period such that it
is less than the installed capacity (𝑁𝑈𝐶) of the plant for the variables chosen for the scenario being
considered
Surplus generating capacity is then dispatched into the NEM subject to the interconnector constraint 𝐼𝐶 below and as depicted in
Table F2.
𝐺𝐸𝑖 = 𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑒𝑒𝑣𝑠𝑖 + 𝑒𝑠𝑡𝑝𝑖 + 𝑒𝑛𝑢𝑐𝑖 ≤ 𝐼𝐶
where:
𝑒𝑝𝑣𝑜𝑖 < 𝑃𝑉𝑂 = PV generation in excess of 𝑆𝑌𝑆𝑖 in the 𝑖𝑡ℎ HH period such that it is less than the installed capacity
(𝑃𝑉𝑂) of PV generation for the variables chosen for the scenario being considered
𝑒𝑝𝑣𝑠𝑖 < 𝑃𝑉𝑆 = cumulative PV paired with storage in excess of 𝑆𝑌𝑆𝑖 in the 𝑖𝑡ℎ HH period such that it is less than
the installed capacity (𝑃𝑉𝑆) of PV generation paired with batteries for the variables chosen for the
scenario being considered
𝑒𝑤𝑖𝑛𝑑𝑜𝑖 < 𝑊𝑂 = cumulative wind generation in excess of 𝑆𝑌𝑆𝑖 in the 𝑖𝑡ℎ HH period such that it is less than the
installed capacity (𝑊𝑂) of wind generation for the variables chosen for the scenario being considered
𝑒𝑤𝑖𝑛𝑑𝑠𝑖 < 𝑊𝑆 = cumulative wind paired with storage release in excess of 𝑆𝑌𝑆𝑖 in the 𝑖𝑡ℎ HH period such that it is
less than the installed capacity (𝑊𝑆) of wind generation paired with grid storage for the variables chosen
for the scenario being considered
𝑒𝑒𝑣𝑠𝑖 < 𝐸𝑉𝑆 = cumulative V2G release in excess of 𝑆𝑌𝑆𝑖 in the 𝑖𝑡ℎ HH period such that it is less than the available
EV storage capacity (𝐸𝑉𝑆) and V2G availability for the variables chosen for the scenario being considered
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𝑒𝑠𝑡𝑝𝑖 < 𝑆𝑇𝑃 = cumulative STP generation in excess of 𝑆𝑌𝑆𝑖 in the 𝑖𝑡ℎ HH period such that it is less than the
installed capacity (𝑆𝑇𝑃) of the plant for the variables chosen for the scenario being considered
𝑒𝑛𝑢𝑐𝑖 < 𝑁𝑈𝐶 = cumulative nuclear generation in excess of 𝑆𝑌𝑆𝑖 in the 𝑖𝑡ℎ HH period such that it is less than the
installed capacity (𝑁𝑈𝐶) of the plant for the variables chosen for the scenario being considered
Table F2: Boundary conditions for power exported to the NEM
Power Exported to the NEM
Dispatch
Rank
Power dispatch
(nuclear in last dispatch mode)
Boundary conditions
1 PV only 𝐼𝐹 (𝑒𝑝𝑣𝑜𝑖 > 𝐼𝐶 𝑡ℎ𝑒𝑛 (𝑒𝑝𝑣𝑜𝑖 = 𝐼𝐶), 0)
2 PV paired with DS that follows the system load profile 𝐼𝐹 ((𝑒𝑝𝑣𝑜𝑖 + 𝑢𝑒𝑝𝑣𝑠𝑖) > 𝐼𝐶 𝑡ℎ𝑒𝑛 𝐼𝐶, (𝑒𝑝𝑣𝑜𝑖 +
𝑢𝑒𝑝𝑣𝑠𝑖))
and
𝑒𝑝𝑣𝑠𝑖 = (𝑒𝑝𝑣𝑜𝑖 + 𝑢𝑒𝑝𝑣𝑠𝑖) − (𝑒𝑝𝑣𝑜𝑖)
3 Wind only 𝐼𝐹 ((𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 + 𝑢𝑒𝑤𝑖𝑛𝑑𝑜𝑖) >
𝐼𝐶 𝑡ℎ𝑒𝑛 𝐼𝐶, (𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 + 𝑢𝑒𝑤𝑖𝑛𝑑𝑜𝑖))
and
𝑒𝑤𝑖𝑛𝑑𝑜𝑖 = (𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 + 𝑢𝑒𝑤𝑖𝑛𝑑𝑜𝑖) − (𝑒𝑝𝑣𝑜𝑖 +
𝑒𝑝𝑣𝑠𝑖)
4 Wind paired with grid storage that follows the system
load profile
𝐼𝐹 ((𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑢𝑒𝑤𝑖𝑛𝑑𝑠𝑖) >
𝐼𝐶 𝑡ℎ𝑒𝑛 𝐼𝐶, (𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑢𝑒𝑤𝑖𝑛𝑑𝑠𝑖))
and
𝑒𝑤𝑖𝑛𝑑𝑠𝑖 = (𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑢𝑒𝑤𝑖𝑛𝑑𝑠𝑖) −
(𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑜𝑖)
5 V2G EV release that follows the system load profile 𝐼𝐹 ((𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑠𝑖 +
𝑢𝑒𝑒𝑣𝑠𝑖) > 𝐼𝐶 𝑡ℎ𝑒𝑛 𝐼𝐶, (𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑜𝑖 +
𝑒𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑢𝑒𝑒𝑣𝑠𝑖))
and
𝑒𝑒𝑣𝑠𝑖 = (𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑠𝑖
+ 𝑢𝑒𝑒𝑣𝑠𝑖) − (𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖
+ 𝑒𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑠𝑖)
6 STP 𝐼𝐹 ((𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑒𝑒𝑣𝑠𝑖 +
𝑢𝑒𝑠𝑡𝑝𝑖) > 𝐼𝐶 𝑡ℎ𝑒𝑛 𝐼𝐶, (𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑜𝑖 +
𝑒𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑒𝑒𝑣𝑠𝑖 + 𝑢𝑒𝑠𝑡𝑝𝑖))
and
𝑒𝑠𝑡𝑝𝑖 = (𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑠𝑖 +
𝑒𝑒𝑣𝑠𝑖 + 𝑢𝑒𝑠𝑡𝑝𝑖) − (𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑜𝑖 +
𝑒𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑒𝑒𝑣𝑠𝑖)
7 Nuclear or the CCGT alternative 𝐼𝐹 ((𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑒𝑒𝑣𝑠𝑖 +
𝑒𝑠𝑡𝑝𝑖 + 𝑢𝑒𝑛𝑢𝑐𝑖) > 𝐼𝐶 𝑡ℎ𝑒𝑛 𝐼𝐶, (𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 +
𝑒𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑒𝑒𝑣𝑠𝑖 + 𝑒𝑠𝑡𝑝𝑖 + 𝑢𝑒𝑛𝑢𝑐𝑖))
and
𝑒𝑛𝑢𝑐𝑖 = (𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑠𝑖 +
𝑒𝑒𝑣𝑠𝑖 + 𝑒𝑠𝑡𝑝𝑖 + 𝑢𝑒𝑛𝑢𝑐𝑖) − (𝑒𝑝𝑣𝑜𝑖 + 𝑒𝑝𝑣𝑠𝑖 +
𝑒𝑤𝑖𝑛𝑑𝑜𝑖 + 𝑒𝑤𝑖𝑛𝑑𝑠𝑖 + 𝑒𝑒𝑣𝑠𝑖 + 𝑒𝑠𝑡𝑝𝑖)
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Power Exported to the NEM
Dispatch
Rank
Power dispatch
(nuclear in last dispatch mode)
Boundary conditions
8 Fossil fuels that are required to meet any generation
shortfall in the SA grid
No fossil fuel generation is exported
where:
𝑢𝑒𝑝𝑣𝑜𝑖 < 𝑃𝑉𝑂 = unconstrained surplus PV generation in excess of 𝐼𝐶 in the 𝑖𝑡ℎ HH period such that it is less than
the installed capacity (𝑃𝑉𝑂) of PV generation for the variables chosen for the scenario being considered
𝑢𝑒𝑝𝑣𝑠𝑖 < 𝑃𝑉𝑆 = unconstrained surplus PV paired with storage in excess of 𝐼𝐶 in the 𝑖𝑡ℎ HH period such that it is
less than the installed capacity (𝑃𝑉𝑆) of PV generation paired with batteries for the variables chosen for
the scenario being considered
𝑢𝑒𝑤𝑖𝑛𝑑𝑜𝑖 < 𝑊𝑂 = unconstrained surplus wind generation in excess of 𝐼𝐶 in the 𝑖𝑡ℎ HH period such that it is less
than the installed capacity (𝑊𝑂) of wind generation for the variables chosen for the scenario being
considered
𝑢𝑒𝑤𝑖𝑛𝑑𝑠𝑖 < 𝑊𝑆 = unconstrained surplus wind paired with storage release in excess of 𝐼𝐶 in the 𝑖𝑡ℎ HH period
such that it is less than the installed capacity (𝑊𝑆) of wind generation paired with grid storage for the
variables chosen for the scenario being considered
𝑢𝑒𝑒𝑣𝑠𝑖 < 𝐸𝑉𝑆 = unconstrained surplus V2G release in excess of 𝐼𝐶 in the 𝑖𝑡ℎ HH period such that it is less than the
available EV storage capacity (𝐸𝑉𝑆) and V2G availability for the variables chosen for the scenario being
considered
𝑢𝑒𝑠𝑡𝑝𝑖 < 𝑆𝑇𝑃 = unconstrained surplus STP generation in excess of 𝐼𝐶 in the 𝑖𝑡ℎ HH period such that it is less than
the installed capacity (𝑆𝑇𝑃) of the plant for the variables chosen for the scenario being considered
𝑢𝑒𝑛𝑢𝑐𝑖 < 𝑁𝑈𝐶 = unconstrained surplus nuclear generation in excess of 𝐼𝐶 in the 𝑖𝑡ℎ HH period such that it is less
than the installed capacity (𝑁𝑈𝐶) of the plant for the variables chosen for the scenario being considered
For nuclear (or the alternative CCGT or CCGT with CCS) plants operating in base mode their dispatch rank24 is changed from 7 to 3,
but all other constraints and boundary conditions remain the same.
24 c.f. Section 4.2, Table 11 of the main report for hierarchy of plant dispatch.
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APPENDIX G – KEY DATA INPUTS
The key data inputs to the NPV economic model have been derived and sourced from many entities that include expert
consultants to the NFCRC, published and unpublished reports, papers, notes and consultation with other experts in their fields. A
detailed summary of the key inputs is presented in the Tables below.
Range value key parameters
Table G1: Range values for key parameters for the NPV economic model
Parameter Most
likely
High Low Range explanation
Discount rate real - CCGT CCS (%)
10% 13% 7%
The most likely figures and the ranges were
specified by the NFCRC for all options in their
note dated 5th November 2015 on the ‘Financial
Modelling Methodology for NPP Business Case
Analyses’. The most likely rate is above the 11%
pre-tax real weighted average cost of capital
(WACC) estimates provided by Imperial College25
in their assessment of the WACC for nuclear
energy. It is also slightly below the 10.47%
calculated by KPMG as part of this assessment
for the NFCRC. However, the real discount rate is
high compared to some other recent studies
with an 8% real figure applied by FGF in 201326.
Discount rate real - small nuclear (%)
Discount rate real - large nuclear (%)
Discount rate real - CCGT (%)
Social discount rate - all options (%) 4% 5% 3%
Social discount rate as specified by the NFCRC for
all options in their note dated 5th November
2015 on the ‘Financial Modelling Methodology
for NPP Business Case Analyses’.
Capital cost of CCGT with CCS in 2030
($/kW) 2,567 3,594 2,054
EY technology costs applying in 2028/2048 when
the plant would need to be ordered. The costs
are based on EPRI data from 2015, but are
updated for the learning curve impacts from
AETA.
A +40/-20% range is applied to account for
exchange rate, general uncertainty and the risk
that the expected learning curve for CCS
technology does not emerge.
The range is wider than applied by WSP-PB for
the nuclear options. However, the nuclear
Capital cost of CCGT with CCS in 2050
($/kW) 2,492 3,482 1,994
25 Imperial College Centre for Energy Policy and Technology “Costs Estimates for Nuclear Power in the UK”, August 2012
26 Section 11 in Modelling the Future Grid Forum Scenarios, CSIRO and Roam Consulting, December 2013
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Parameter Most
likely
High Low Range explanation
options have exchange rate volatility that has
been treated as a separate variable and it is a
more mature technology that CCS.
International capital cost of small
nuclear plant (US$/kW) 4,008 4,797 3,393
Range provided by WSP-PB in updated
assumptions dated 21/10/2015. Numbers have
had one year of inflation applied to convert into
2014/15 values.
Local capital cost of small nuclear plant
(A$/kW) 3,588 4,295 3,044
International capital cost of large
nuclear plant (US$/kW) 3,167 3,495 2,942
Local capital cost of large nuclear plant
(A$/kW) 3,475 3,844 3,229
Capital cost of transmission backbone
and interconnection (A$m) 2,091 3,072 1,673
Range provided by WSP-PB in updated
assumptions dated 21/10/2015. Numbers have
had one year of inflation applied to convert into
2014/15 values.
Capital cost of CCGT in 2030 ($/KW) 1,579 1,895 1,263
These are EY technology costs applying in
2028/2048 when the plant would need to be
ordered. The costs are based on EPRI data from
2015, but are updated for the learning curve
impacts from AETA.
A 20% range is applied to account for exchange
rate and general uncertainty. The range is slightly
wider than that applied by WSP-PB for the
nuclear options, however the nuclear options
have exchange rate volatility treated as a
separate variable.
Capital cost of CCGT in 2050 ($/KW) 1,639 1,967 1,311
Nuclear project development costs
(A$m) 308 631 158
Range provided by WSP-PB in updated
assumptions dated 21/10/2015. Numbers have
had one year of inflation applied to convert into
2014/15 values.
Nuclear overseas project development
costs (US$m) 65 129 32
Regulatory and licensing and public
enquiry costs (A$m) 67 99 40
Life of CCGT with CCS (years) 40 50 30
Central figure is from the AETA 201227 report
(does state 40 to 50 years in the report, but this
is unproven, so the low end figure has been
used). Uncertain as to how long the CCS part of
the plant could last, so 10 year reduction has
been used. 10 year extension has been applied
for all plant.
27 Australian Energy Technology Assessment, Bureau of Resources and Energy Economics, 2012
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Parameter Most
likely
High Low Range explanation
Life of small nuclear plant (years) 60 70 45
Long lasting generation plant – global risks of
issues developing for the nuclear industry so
option of a shorter life is examined, but the
relatively high discount rates makes this fairly
low materiality. Most likely figures from WSP-PB
updated assumptions dated 21/10/2015.
Range considers the report from Reiss et al28 that
suggests an operating life of only 40 years.
However, this includes the bad experience of
some of the older plants and specific regulatory
circumstances facing some nuclear operators in
the US.
Life of large nuclear plant (years) 60 70 45
Life of CCGT (years) 40 50 35
Central figure is from the AETA 2012 report.
Small reduction as proven technology, with
10 year extension for all technologies
VOM of CCS in 2030 ($/MWh sent out) 14.7 17.6 11.8
EY provided VOM most likely figures based on
EPRI data for 2015 using AETA learning curves to
provide a figure for the 2030/31 start for the
plant.
VOM range of +/- 20% from EY/AETA figures
based on consistency with FOM range.
VOM small nuclear ($/MWh sent out) 0.1 0.12 0 VOM has very low materiality as it is mainly fixed
cost Included to indicate it has been identified.
Sensitivity is not material so set to zero or
increased by 20%. VOM large nuclear ($/MWh sent out) 0.1 0.12 0
VOM CCGT ($/MWh sent out) 1.8 2.2 1.5
EY provided VOM most likely figures based on
EPRI data for 2015 using AETA learning curves to
provide a figure for the 2030/31 start for the
plant.
VOM range of +/- 20% from EY/AETA figures
based on consistency with FOM range.
These figures are lower than other VOM
estimates from AETA, but this is offset by a much
higher level of FOM costs.
28 Quantifying Key Uncertainties in the Costs of Nuclear Power – Jenny Riesz, Claire Sotiriadis, Peerapat Vithayasrichareon, Joel Gilmore
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Parameter Most
likely
High Low Range explanation
FOM for CCS in 2030 ($/MW) 42,868 51,442 34,292
EY provided FOM most likely figures based on
EPRI data for 2015 using AETA learning curves to
provide a figure for the 2030/31 start for the
plant.
FOM range of +/- 20% from EY/AETA figures
based on consistency with nuclear calculations.
FOM local nuclear for large plant in
2015 (A$/MW) 98,503 118,183 78,720
WSP-PB provided range figures from
assumptions dated 21/10/2015. Figures need to
be escalated to obtain to the 2030 figures.
Numbers have had one year of inflation applied
to convert into 2014/15 values.
FOM small nuclear for large plant in
2015 (A$/MW) 108,035 129,663 86,408
FOM overseas nuclear for large plant in
2015 (US$/MW) 57,400 68,880 45,920
FOM overseas nuclear for small plant in
2015 (US$/MW) 50,123 60,065 40,078
Insurance large nuclear in 2015
($US/MW) 17,528 19,373 16,298
Insurance small nuclear in 2015
($US/MW) 20,295 24,293 17,220
FOM CCGT in 2030 ($/MW) 24,496 29,395 19,597
EY provided FOM most likely figures based on
EPRI data for 2015 using AETA learning curves to
provide a figure for the 2030/31 start for the
plant.
FOM range of +/- 20% from EY/AETA figures
based on consistency with nuclear calculations.
These figures are higher than other FOM
estimates from AETA, but this is offset by a much
lower level of VOM costs.
Annual escalation factor for O&M (%) 1.05% 1.25% 0.50%
EY calculation based on AETA learning curve.
Variable that applies over the life of the model
so is a material impact as applied to VOM/FOM
and decommissioning/storage costs.
The range tested includes an approximate 20%
increase and just over 50% reduction as costs
could level out over time with a learning curve
leading to efficiencies.
Efficiency of CCGT with CCS in 2030 (%) 48.1% 49.5% 46.1%
Most likely efficiencies from AEMO estimates
produced by ACIL Allen. The high estimates are
those provided by EY Technology Assumptions
based on AETA. The low estimates are mid-way
between the expected efficiencies by 2028 and
the level existing now.
Efficiency of CCGT in 2030 (%) 54.7% 55.1% 52.7%
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Parameter Most
likely
High Low Range explanation
Efficiency of CCGT with CCS in 2050 (%) 50.7% 57.5% 48.1%
Most likely efficiencies from AEMO estimates
produced by ACIL Allen. The high estimates are
those provided by EY Technology Assumptions
based on AETA. The low estimates are based on
the expected efficiencies achieve by 2028 with
no further improvement.
Efficiency of CCGT in 2050 (%) 56.6% 62.1% 54.7%
Cost of fuel small nuclear ($US/MWh) 9.3 11.2 7.5 WSP-PB estimates updated assumptions dated
21/10/2015. Numbers have had one year of
inflation applied to convert into 2014/15 values. Cost of fuel large nuclear ($US/MWh) 7.8 9.3 6.3
Percentage change in gas prices (%) 0.0% 20.0% -20.0%
The range in changes from gas price tracks were
provided by EY. These vary slightly between
climate change/action policy options, but
generally rise from around $9.2GJ in 2030 to
around $10.2GJ by 2040. This is in line with the
recent AEMO data.
These forecasts represent a material increase in
current gas costs and reflect expectations of a
growing LNG industry and prices reflecting
international costs. There is the possibility that
the LNG industry does not continue to develop
with weaker demand growth in China (and other
areas) and a glut of supply resulting in gas
continuing to be sold in Australia with prices
remaining closer to current levels rather than the
$10GJ + that are predicted.
There is also an alternative scenario of strong
growth in demand for gas emerging after the
recent price downturn in the gas/oil market
disappears in the longer term with increasing gas
fired generation replacing coal generation and
therefore increasing prices for gas.
A wide range of 20% around the most likely
value is therefore applied.
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Parameter Most
likely
High Low Range explanation
Cost of carbon sequestration ($/tonne) 45 80 20
The CO2CRC draft report used a range between
$5 and $80 per tonne. However, the geographic
conditions in South Australia would not allow the
bottom end of this range to be achieved. The
cost is built up using a lower cost of capital than
in the model used by DGA Consulting/Carisway.
The assessment carried out for this modelling
has therefore applied a most likely figure that is
towards the upper end of the numbers provided,
but has tested a fairly wide range of numbers.
These costs do not include costs of storage site
exploration and appraisal works, which can be
significant adding 14% to 25% to the total cost29.
MLF large nuclear (multiplier on
generation sent out) 0.965 0.990 0.965
Range provided for PWR in WSP-PB updated
assumptions dated 21/10/2015.
MLF small plant (multiplier on
generation sent out) 0.975 0.975 0.950
Range provided for SMR in WSP-PB updated
assumptions dated 21/10/2015. Figures used for
all small plant.
Large nuclear decommissioning cost
(US$m) 513 615 410
The range was provided for PWR/SMR in WSP-PB
updated assumptions dated 21/10/2015.
Numbers have had one year of inflation applied
to convert into 2014/15 values.
Small nuclear decommissioning cost
(US$m) 256 308 205
Levy to cover dry storage cost for large
nuclear (US$/MWh) 3.8 4.6 2.6
The calculation was done by WSP-PB based on a
cost of between US$1m and US$2m per tonne of
heavy metal provided by Jacobs.
This was converted into $/MWh using
assumptions on efficiency for the different plant
with a range provided by WSP-PB.
Numbers have had one year of inflation applied
to convert into 2014/15 values.
Levy to cover dry storage cost for small
nuclear (US$/MWh) 4.6 6.2 3.1
29 W Hou, G Allinson, I MacGill, PR Neal, MT Ho (2014), ‘Cost comparison of major low-carbon electricity generation options: an Australian case study’, Sustainable Energy Technologies and Assessments, 8:131–148 referenced in Australian Power Generation Technology Report, November 2015.
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Parameter Most
likely
High Low Range explanation
Exchange rate percentage change from
expected level (%) 0% 15% -10%
DGA estimate of percentage change from EY
estimates of the exchange rate. The assessment
looked at monthly exchange rates for last 5 and
10 years. In the last 5 years the minimum A$/U$
exchange rate was around 0.70 and in the last 10
years 0.64. In the period prior to the mining
boom the rate was lower. The maximum
monthly exchange rate in last 10 years was 1.09,
but this could be seen as an anomaly in the
middle of the mining boom. It was also as high
as 0.96 in 2008.
As any contract for generation will be long term
(including US$ costs over the course of the
project) the single high/low figures may not be
appropriate, but provide some view of the
extremes. There is also evidence of the rate
being consistently 20% above the long term rate
proposed during 2011 and 2012 and 15% above
the 2007 and 2008 rate for around a year.
To align with recent history and the current
mining slowdown, it is suggested a -10% and
+15% range be applied.
Variation in wholesale electricity price
without carbon price (%) 0% 10% -10%
The level of the wholesale electricity price will be
a key impact on the NPV and the base level is
determined by the selection of the climate
change/action policy assumptions, all of which
have a carbon price and wholesale electricity
price track.
The modelling assesses the impact of
movements away from the most likely level with
a range of +/-10% from the predicted level. As
the carbon price element is assessed separately,
this has been deleted from this parameter.
The calculation of the carbon price contribution
was based on the difference between a ‘No
Action Scenario’ and the ‘Climate Change
Scenario’ selected.
DGA Consulting/Carisway
Nuclear Fuel Cycle Royal Commission – Appendix Report
Page 31
Parameter Most
likely
High Low Range explanation
% change in the carbon price from the
most likely predictions (%) 0% 10% -10%
The level of the carbon price will have a key
impact on the wholesale electricity price and
therefore the NPV. The base level is determined
by the selection of the climate change/action
policy assumptions, all of which have a carbon
price and wholesale price track.
The modelling assesses the impact of
movements away from the most likely level with
a range of +/-10% from the predicted level.
The calculation of the carbon price contribution
to the wholesale electricity price was based on
the difference between a ‘No Action Scenario’
and the ‘Climate Change Scenario’. This
calculation is used to assess the impact of the
price changing by a further 10% in either
direction from the most likely value.
Time for delay of the CCGT with CCS
(years) - 1 0
Aligned with small nuclear in terms of
technology that is not widely established in
Australia, but no upside potential as it is
assumed there is only 2 year build program.
The time delay is assumed to result in cost
overruns and the level of these are specified in
the parameters below
Time for delay small nuclear (years) - 1 -1
Time delay indicated as the range in the WSP-PB
data input sheet dated 21/10/2015.
The time delay is assumed to result in cost
overruns and the level of these are specified in
the parameters below.
Time for delay large nuclear (years) - 2 -1
Time delay indicated as the range in the WSP-PB
data input sheet dated 21/10/2015.
Maximum delay is less than experienced in
recent French and Danish projects. However, the
selection of NOAK installations should mitigate
some of this risk.
The time delay is assumed to result in cost
overruns and the level of these are specified in
the parameters below.
DGA Consulting/Carisway
Nuclear Fuel Cycle Royal Commission – Appendix Report
Page 32
Parameter Most
likely
High Low Range explanation
Time for delay CCGT (years) - 1 0
A one year delay has been included. This aligns
with CCGT with CCS (may be too high as it aligns
with small nuclear that has a higher risk of
delay). However, the model needs the delay to
be an integer.
The time delay is assumed to result in cost
overruns and the level of these are specified in
the parameters below.
DGA Consulting/Carisway
Nuclear Fuel Cycle Royal Commission – Appendix Report
Page 33
Single value key parameters
Table G2: Single value key parameters for the NPV economic model
Parameter Value Justification
CCGT CCS capacity (MW sent out) 327 Taken from the AETA report from 201230 on standard
size for generating units (note that most costs and
benefits are based on $/KW so not material). CCGT capacity (MW sent out) 374
Small nuclear capacity (MW sent out) 285 Size aligned with WSP-PB updated assumptions dated
21/10/15. Large nuclear capacity (MW sent out) 1,125
Potential overrun of budget for CCGT with CCS (%) 25% New technology hence numbers aligned with the
nuclear experience. See explanation below.
30 Australian Energy Technology Assessment, Bureau of Resources and Energy Economics, 2012.
DGA Consulting/Carisway
Nuclear Fuel Cycle Royal Commission – Appendix Report
Page 34
Parameter Value Justification
Potential overrun of budget for small nuclear (%) 25%
Nuclear plants have a history of being over budget.
The US Department of Energy looked at 75 nuclear
power plants initiated between 1966 and 1977 and
found the average cost overrun to be 207%31. This is
however fairly old data and many lessons may have
since been learnt.
A more recent review of budget overruns was
undertaken by the ‘Centre for Energy and
Environmental Markets’ and ‘School of Electrical
Engineering and Telecommunications’, both at
UNSW32.
The analysis suggested that the central estimate of the
LCOE of the rest of world, excluding Asia, is $138MWh
without escalation and $428MWh with escalation. This
implies a contribution of cost escalation to the nuclear
costs of 210%.
The UNSW report considered the cost escalation from
the initial overnight capital costs that occurred during
both the pre-construction and construction periods of
a nuclear plant. The assessment had a range for the
real cost increases during construction of between
3.6% pa and 12.2% with a central estimate of 6.5%.
This applies over the expected construction duration,
which they have as a central estimate of 9.9 years and
a range between 4 and 20 years The UNSW report had
a large range for the increase in costs during the pre-
construction period with a cost escalation rate
between 0% and 20% with a central estimate of 15.4%.
31 Synapse Energy Economics – Nuclear Power Plant Construction Costs – July 2008, David Schlissel and Bruce Biewald.
32 Quantifying Key Uncertainties in the Costs of Nuclear Power – Jenny Riesz, Claire Sotiriadis, Peerapat Vithayasrichareon, Joel Gilmore.
DGA Consulting/Carisway
Nuclear Fuel Cycle Royal Commission – Appendix Report
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Parameter Value Justification
Potential overrun of budget for large nuclear (%) 25%
Within the UNSW report there is a best case sensitivity
applied that has a 3.6% cost escalation during
construction and a pre-construction escalation rate set
to zero. With a 5 year planned project construction
period this has been rounded up to 20% in the DGA
Consulting/Carisway modelling
The UNSW report leads to large numbers for the cost
overruns. However, it should be read in the context of
the cost numbers proposed by WSP-PB that are
significantly above the numbers outlined in the UNSW
article. As an example the mean cost estimate before
escalation, excluding Asia, was $5,075 per kW. This
compares with an estimate from WSP-PB of around
$7,500 per kW for a large nuclear power plant and
$8,500 per kW for a small nuclear power plant.
This cost difference would suggest that WSP-PB have
already incorporated many of the lessons from
previous projects. During pre-construction if an
established reactor design exists, which is the
assumption for a NOAK plant, then this should
minimise any escalation from design changes and
therefore the level of escalation should be related to
site characteristics, which represent a far smaller
amount of the cost.
During construction there is a risk of cost overrun due
to project delay. It is suggested that the budget
overrun could be based on extending the project from
5 to 6 or 7 years and assumes that this results in 3% to
4% cost increase as indicated in the best case
examples (to reflect a relatively high starting cost).
This would add around 25% to the cost estimates,
which can be tested with the sensitivity analysis.
Potential overrun of budget for CCGT (%) 10%
Australia has much more experience with CCGT plant,
which represents a less complex project than nuclear
or CCGT with CCS.
Time to build CCGT with CCS (years) 2 Figures from AETA 2012 report.
Time to build CCGT (Years) 2 Figures from AETA 2012 report.
Time to build small nuclear generator (years) 3 WSP-PB – Updated Assumptions 21/10/2015.
Time to build large nuclear generator (years) 5 WSP-PB – Updated Assumptions 21/10/2015.
Pre-construction time for nuclear 5 WSP-PB – Updated Assumptions 21/10/2015.
Pre-construction time for CCGT/CCGT with CCS 5 Set the same as nuclear (low materiality as relatively
small costs).
Ancillary services load for CCGT with CCS (%) 10% AETA 2012 report.
DGA Consulting/Carisway
Nuclear Fuel Cycle Royal Commission – Appendix Report
Page 36
Parameter Value Justification
Ancillary services load for CCGT (%) 3% AETA 2012 report.
Ancillary services load for small nuclear (%) 3% AETA 2012 report.
Ancillary services load for large nuclear (%) 3% AETA 2012 report.
Cost of transmission infrastructure ($m/MW) 0.11
Taken from AEMO 2014 figures for Adelaide as a
connection cost (connection cost done separately for
nuclear plant).
Assumed efficiency of gas plant setting the price (%) 40%
DGA Consulting/Carisway estimate is a mixture of
some older OCGT plants that may have much lower
efficiency than modern CCGT plants that could have
efficiencies close to 60%.
This parameter only impacts the sensitivity analysis
not the main results.
Percentage of time that gas is the marginal plant (%) 25%
DGA Consulting/Carisway estimate. In South Australia
there could be a growing amount of time when wind,
PV, solar thermal or storage from these sources is
setting the wholesale electricity price depending on
the storage release schedules and the level of demand.
This parameter only impacts the sensitivity analysis
not the main results.
Percentage of the changing costs that is passed
through in the wholesale electricity price (%) 50%
DGA Consulting/Carisway estimate. Some of the gas
plants may have ‘take or pay’ contracts, or long term
fixed price deals with the gas supplier that make their
operation independent of any change in the wholesale
electricity price. It is therefore assumed that not all
increases in the gas price will be passed through into
the wholesale electricity price and a value of 50% has
been set as a proxy.
This parameter only impacts the sensitivity analysis
not the main results.
Percentage of time electricity is purchased (%) 5% WSP- PB- Initial Business Case and Cost Estimates -
16th September 2015 – Applied to all generators.
Percentage of full load purchased (%) 5% WSP-PB- Initial Business Case and Cost Estimates -
16th September 2015.
TUoS annual cost ($/MW) 2,000 WSP-PB- Initial Business Case and Cost Estimates -
16th September 2015.
Auxiliary percentage for TUoS (%) 5% WSP-PB- Initial Business Case and Cost Estimates -
16th September 2015.
TUoS charges at 500kV ($/MW) 94,500 WSP-PB- Initial Business Case and Cost Estimates -
16th September 2015.
TUoS charges at 275kV ($/MW) 61,975 WSP- PB- Initial Business Case and Cost Estimates -
16th September 2015.
DGA Consulting/Carisway
Nuclear Fuel Cycle Royal Commission – Appendix Report
Page 37
Parameter Value Justification
Year before construction starts when the road is built
(year) 1 WSP-PB – Email clarification – 29th September 2015.
Year after construction starts when the rail is built
(year) 0 WSP-PB – Email clarification – 29th September 2015.
Year before planned commissioning when the
transmission line is built (year) 1 WSP-PB – Email clarification – 29th September 2015.
Year before planned commissioning when long
distance assets start (year) 2 WSP-PB – Email clarification – 29th September 2015.
Year before planned commissioning when cooling
water assets start (year) 3
DGA Consulting/Carisway estimate (only applies to
small plant and base value has cooling water not
required).
Planned first commissioning date (year) 2030 In line with Statement of Work (SoW).
Planned second commissioning date (year) 2050 In line with SoW.
Pre-construction investment CCGT with CCS (A$m) 30
Current figure is based on high estimate from LCOE
Model33 review. The £20 per kw was converted at
A$2.25 to £1 with a cost of A$45 per kW. The figures
have been doubled to reflect the differences between
CCGT and CCGT with CCS in capital costs and the
additional issues associated with gaining agreement to
this type of plant.
The pre-construction cost has used the higher estimate
to reflect the limited level of up to date project
delivery experience for CCGT with CCS by the time any
plant is commissioned in 2030.
Pre-construction Investment CCGT (A$m) 17
Current figure is based on high estimate from LCOE
Paper34 review reflecting limited project development
experience. The £20 per kw was converted at $2.25 to
A$1 with a cost of A$45 per kW.
The pre-construction cost has used the higher estimate
to reflect the limited level of up to date project
delivery experience for CCGT by the time any plant is
commissioned in 2030.
Life cycle carbon intensity of large nuclear
(kg CO2/MWh) 12
Median value from harmonised review of the
literature35.
33 LCOE models: A comparison of the theoretical frameworks and key assumptions – Prof John Foster, Dr Liam Wagner, Alexandra Bratanova – Funded by CSIRO Future Grid Flagship Cluster – Project 3: Economic and Investment Models for Future Grids.
34 LCOE models: A comparison of the theoretical frameworks and key assumptions – Prof John Foster, Dr Liam Wagner, Alexandra Bratanova – Funded by CSIRO Future Grid Flagship Cluster – Project 3: Economic and Investment Models for Future Grids.
35 Life Cycle Greenhouse Gas Emissions of Nuclear Electricity Generation – Systematic Review and Harmonisation, Ethan S Warner, and Garvin A Heath, Journal of Industrial Ecology, 2012.
DGA Consulting/Carisway
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Parameter Value Justification
Life cycle carbon intensity of small nuclear
(kg CO2/MWh) 14
Scaled up version of intensity of large nuclear
generation to reflect around 10% lower efficiency and
less scale economies available for small nuclear plant.
Scope 3 emissions for gas plant in SA (kg CO2/GJ) 10.2
Emission Factors - Review of Emission Factors for Use
in the CDE II – Report to Australian Energy Market
Commission – ACIL Allen Consulting
DGA Consulting/Carisway
Nuclear Fuel Cycle Royal Commission – Appendix Report
Page 39
Time series parameters
Table G3: Time series parameters for the NPV economic model
Parameter Value Justification
Availability for CCGT with CCS
90%
Range is set at +/-1%
around this value
Based on Excel data from AEMO - ACIL Allen 2014 -
20/365 days planned maintenance +4.5% forced
outages. This results in an availability level of 90%.
Availability for small nuclear
93%
This was applied flat for
each year with a
specified range
between 91% and 95%
WSP-PB updated assumptions dated 21/10/2015.
Unlike the large nuclear generator there is no plant
downtime for re-fuelling as there are six separate units
that are operating.
Availability for large nuclear 97% to 89% base
WSP-PB updated assumptions dated 21/10/2015. This
had a rate that is 97% for two of the years and 89% for
the other two years reflecting refuelling requirements.
The range goes from 96% to 85% for the low value
with 98% to 93% for the high values.
Availability for CCGT
92%
Range is set at +/-1%
around this range
Based on Excel data from AEMO - ACIL Allen 2014 -
20/365 days planned maintenance +2.5% forced
outages. Results in availability level of 92%.
Gas prices (3 range for BIS,IS2 & IS3) –
($/GJ)
Approximately $9.2/GJ
in 2030 rising to around
$10.2/GJ by 2040
Range similar for all options and is derived by EY from
AEMO data.
Gas prices do increase fairly steeply from $5.6/GJ in
2016 to $9.2/GJ by 2030. However, this is not applied
in the modelling herein.
Carbon prices (ranges for BIS,IS2 & IS3) =
($/tonne CO2)
BIS - $86 to $179
IS2 -$88 to $185
IS3 - $123 to $254
Range varies considerably between options, although
BIS and IS2 are relatively close after 2030.
Costs calculated by EY as the price needed to meet the
climate change/action policy assumptions specified by
the NFCRC.
Wholesale prices (ranges for BIS, IS2, IS3
and IS3 with large nuclear)
BIS - $124 to $154
IS2 - $125 to $162
IS3 - $138 to $186
IS3 W L nuclear - $105
to $148
IS3 W S nuclear -$130
to $176
Prices are derived from EY’s modelling using the
climate change/action policy assumptions and carbon
prices derived above.
The large nuclear plant is of a sufficient size to have a
material impact on the wholesale electricity price.
DGA Consulting/Carisway
Nuclear Fuel Cycle Royal Commission – Appendix Report
Page 40
Parameter Value Justification
Exchanges rates
Values starts at 0.755
by 2020 and grow
slowly. By2030 the
exchange rate is 0.766,
by 2040 it is 0.768, and
by 2050 it is 0.769.
Sourced from EY.
No values exist after 2050 and the modelling herein
has therefore assumed that the exchange rate will
continue at the same level.
Implied carbon contribution to the
wholesale electricity price ($/MWh)
Ranges from $12 to $57
MWh in 2030 up to $21
to $84 MWh in 2050
This Is a calculation comparing the ‘No Action’
wholesale electricity price with the wholesale
electricity price that applies under the different
climate change/action policy assumptions.
It is materially impacted if large nuclear is included as
this is assumed to reduce the electricity prices
dramatically.
Percentage increase in wholesale
electricity prices due to operating in mid-
merit order
BIS/IS2
16.8% to 20.4%
between 2030/31 and
2049/50
IS3
18.1% to 23.0%
between 2030/31 and
2049/50
Based on single value figures from EY for 2030/31 and
2049/50 with a straight line interpolation.
Applied to both CCGT plants.
Capacity factor to apply each year when
operating in mid-merit order
BIS
68,2% to 65.5%
between 2030/31 and
2049/50
IS3
66.9% to 64.1%
between 2030/31 and
2049/50
Based on single value figures from EY for 2030/31 and
2049/50 with a straight line interpolation.
Applied to both CCGT plants.
DGA Consulting/Carisway
Nuclear Fuel Cycle Royal Commission – Appendix Report
Page 41
APPENDIX H – SENSITIVITY CHARTS FOR ALL OPTIONS
Sensitivity charts for BIS in 2030
Monte Carlo charts for BIS in 2030
3594
13.0%
20.0%
-10%
80
1.00
-10%
46.1%
17.6
2054
7.0%
-20.0%
10%
20
-
10%
49.5%
11.8
-$900 -$800 -$700 -$600 -$500 -$400 -$300 -$200 -$100 $0
Capital Cost of CCGT with CCS in 2030 ($2567/kW)
Discount Rate Real CCGT CCS (10%)
Percentage change in Gas Prices (0%)
Variation in Wholesale Price without Carbon (0%)
Cost of Carbon Sequestration ($45/tonne)
Time & Cost for Delay CCGT with CCS (0 years)
% Change in Carbon Price from Most Likely Predictions (0%)
Efficiency of CCGT with CCS in 2030 (48.14%)
VOM CCS in 2030 ($14.7/MWh sent out)
NPV of CCGT with CCS in 2030 (M$AUD)Showing Values >= 100.0 M$AUD
13.0%
1.00
631
-10%
4797
-10%
4295
7.0%
- 1.00
158
15%
3393
10%
3044
-$3,500 -$3,000 -$2,500 -$2,000 -$1,500 -$1,000 -$500 $0
Discount Rate Real Small Nuclear (10%)
Time & Cost for Delay Small Nuclear (0 years)
Nuclear Project Development Costs (315.7 AUD $M)
Exchange Rate Change from Expected Level (0%)
International Capital Cost of Small Nuclear Plant (USD$4007.75/kW)
Variation in Wholesale Price without Carbon (0%)
Local Capital Cost of Small Nuclear Plant (AUD$3587.5/kW)
NPV of Small Nuclear in 2030 (M$AUD)Showing Values >= 200.0 M$AUD
13.0%
2.00
-10%
-10%
3495
631
3844
1.25%
118183
7.0%
- 1.00
15%
10%
2942
158
3229
0.50%
78720
-$12,000 -$10,000 -$8,000 -$6,000 -$4,000 -$2,000 $0
Discount Rate Real Large Nuclear (10%)
Time & Cost for Delay Large Nuclear (0 years)
Exchange Rate Change from Expected Level (0%)
Variation in Wholesale Price without Carbon (0%)
International Capital Cost of Large Nuclear Plant (USD$3167.25/kW)
Nuclear Project Development Costs (315.7 AUD $M)
Local Capital Cost of Large Nuclear Plant (AUD$3474.75/kW)
Annual Escalation Factor for O&M (1.05%)
FOM Local Nuclear for large Plant in 2015 ($98502.5/MW)
NPV of Large Nuclear in 2030 (M$AUD)Showing Values >= 500.0 M$AUD
13.0%
-10%
20.0%
1894.8
1.00
52.7%
7.0%
10%
-20.0%
1263.2
-
55.1%
-$200 -$100 $0 $100 $200 $300 $400 $500
Discount Rate Real CCGT (10%)
Variation in Wholesale Price without Carbon (0%)
Percentage change in Gas Prices (0%)
Capital Cost of CCGT in 2030 ($1579/KW)
Time & Cost for Delay CCGT (0 years)
Efficiency of CCGT in 2030 (54.68%)
NPV of CCGT in 2030 (M$AUD)Showing Values >= 100.0 M$AUD
DGA Consulting/Carisway
Nuclear Fuel Cycle Royal Commission – Appendix Report
Page 42
Sensitivity charts for IS2 in 2030
Monte Carlo charts for IS2 in 2030
13.0%
3594
20.0%
-10%
80
1.00
-10%
46.1%
17.6
7.0%
2054
-20.0%
10%
20
-
10%
49.5%
11.8
-$800 -$700 -$600 -$500 -$400 -$300 -$200 -$100 $0 $100
Discount Rate Real CCGT CCS (10%)
Capital Cost of CCGT with CCS in 2030 ($2567/kW)
Percentage change in Gas Prices (0%)
Variation in Wholesale Price without Carbon (0%)
Cost of Carbon Sequestration ($45/tonne)
Time & Cost for Delay CCGT with CCS (0 years)
% Change in Carbon Price from Most Likely Predictions (0%)
Efficiency of CCGT with CCS in 2030 (48.14%)
VOM CCS in 2030 ($14.7/MWh sent out)
NPV of CCGT with CCS in 2030 (M$AUD)Showing Values >= 100.0 M$AUD
13.0%
1.00
631
-10%
4797
-10%
4295
7.0%
- 1.00
158
15%
3393
10%
3044
-$3,500 -$3,000 -$2,500 -$2,000 -$1,500 -$1,000 -$500 $0
Discount Rate Real Small Nuclear (10%)
Time & Cost for Delay Small Nuclear (0 years)
Nuclear Project Development Costs (315.7 AUD $M)
Exchange Rate Change from Expected Level (0%)
International Capital Cost of Small Nuclear Plant (USD$4007.75/kW)
Variation in Wholesale Price without Carbon (0%)
Local Capital Cost of Small Nuclear Plant (AUD$3587.5/kW)
NPV of Small Nuclear in 2030 (M$AUD)Showing Values >= 200.0 M$AUD
13.0%
2.00
-10%
-10%
3495
631
3844
1.25%
118183
7.0%
- 1.00
15%
10%
2942
158
3229
0.50%
78720
-$12,000 -$10,000 -$8,000 -$6,000 -$4,000 -$2,000 $0
Discount Rate Real Large Nuclear (10%)
Time & Cost for Delay Large Nuclear (0 years)
Exchange Rate Change from Expected Level (0%)
Variation in Wholesale Price without Carbon (0%)
International Capital Cost of Large Nuclear Plant (USD$3167.25/kW)
Nuclear Project Development Costs (315.7 AUD $M)
Local Capital Cost of Large Nuclear Plant (AUD$3474.75/kW)
Annual Escalation Factor for O&M (1.05%)
FOM Local Nuclear for large Plant in 2015 ($98502.5/MW)
NPV of Large Nuclear in 2030 (M$AUD)Showing Values >= 500.0 M$AUD
13.0%
-10%
20.0%
1894.8
1.00
52.7%
7.0%
10%
-20.0%
1263.2
-
55.1%
-$100 $0 $100 $200 $300 $400 $500 $600 $700
Discount Rate Real CCGT (10%)
Variation in Wholesale Price without Carbon (0%)
Percentage change in Gas Prices (0%)
Capital Cost of CCGT in 2030 ($1579/KW)
Time & Cost for Delay CCGT (0 years)
Efficiency of CCGT in 2030 (54.68%)
NPV of CCGT in 2030 (M$AUD)Showing Values >= 100.0 M$AUD
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Nuclear Fuel Cycle Royal Commission – Appendix Report
Page 43
Sensitivity charts for IS3 in 2030
Monte Carlo analysis for IS3 in 2030
13.0%
1.00
631
-10%
4797
-10%
4295
-10%
7.0%
- 1.00
158
15%
3393
10%
3044
10%
-$3,000 -$2,500 -$2,000 -$1,500 -$1,000 -$500 $0
Discount Rate Real Small Nuclear (10%)
Time & Cost for Delay Small Nuclear (0 years)
Nuclear Project Development Costs (315.7 AUD $M)
Exchange Rate Change from Expected Level (0%)
International Capital Cost of Small Nuclear Plant (USD$4007.75/kW)
Variation in Wholesale Price without Carbon (0%)
Local Capital Cost of Small Nuclear Plant (AUD$3587.5/kW)
% Change in Carbon Price from Most Likely Predictions (0%)
NPV of Small Nuclear in 2030 (M$AUD)Showing Values >= 200.0 M$AUD
13.0%
3594
20.0%
-10%
80
1.00
-10%
46.1%
17.6
7.0%
2054
-20.0%
10%
20
-
10%
49.5%
11.8
-$500 -$400 -$300 -$200 -$100 $0 $100 $200 $300 $400 $500 $600
Discount Rate Real CCGT CCS (10%)
Capital Cost of CCGT with CCS in 2030 ($2567/kW)
Percentage change in Gas Prices (0%)
Variation in Wholesale Price without Carbon (0%)
Cost of Carbon Sequestration ($45/tonne)
Time & Cost for Delay CCGT with CCS (0 years)
% Change in Carbon Price from Most Likely Predictions (0%)
Efficiency of CCGT with CCS in 2030 (48.14%)
VOM CCS in 2030 ($14.7/MWh sent out)
NPV of CCGT with CCS in 2030 (M$AUD)Showing Values >= 100.0 M$AUD
13.0%
2.00
-10%
-10%
3495
631
3844
1.25%
118183
-10%
7.0%
- 1.00
15%
10%
2942
158
3229
0.50%
78720
10%
-$10,000-$9,000-$8,000-$7,000-$6,000-$5,000-$4,000-$3,000-$2,000-$1,000 $0
Discount Rate Real Large Nuclear (10%)
Time & Cost for Delay Large Nuclear (0 years)
Exchange Rate Change from Expected Level (0%)
Variation in Wholesale Price without Carbon (0%)
International Capital Cost of Large Nuclear Plant (USD$3167.25/kW)
Nuclear Project Development Costs (315.7 AUD $M)
Local Capital Cost of Large Nuclear Plant (AUD$3474.75/kW)
Annual Escalation Factor for O&M (1.05%)
FOM Local Nuclear for large Plant in 2015 ($98502.5/MW)
% Change in Carbon Price from Most Likely Predictions (0%)
NPV of Large Nuclear in 2030 (M$AUD)Showing Values >= 500.0 M$AUD
13.0%
-10%
20.0%
1894.8
1.00
52.7%
0.950
7.0%
10%
-20.0%
1263.2
-
55.1%
0.975
$0 $100 $200 $300 $400 $500 $600 $700 $800 $900
Discount Rate Real CCGT (10%)
Variation in Wholesale Price without Carbon (0%)
Percentage change in Gas Prices (0%)
Capital Cost of CCGT in 2030 ($1579/KW)
Time & Cost for Delay CCGT (0 years)
Efficiency of CCGT in 2030 (54.68%)
MLF Small Plant (0.975 )
NPV of CCGT in 2030 (M$AUD)Showing Values >= 100.0 M$AUD
DGA Consulting/Carisway
Nuclear Fuel Cycle Royal Commission – Appendix Report
Page 44
Sensitivity charts for BIS in 2050
Monte Carlo charts for BIS in 2050
13.0%
-10%
3489
20.0%
80
48.1%
1.00
17.6
7.0%
10%
1994
-20.0%
20
57.5%
-
11.8
-$500 -$400 -$300 -$200 -$100 $0 $100 $200 $300 $400
Discount Rate Real CCGT CCS (10%)
Variation in Wholesale Price without Carbon (0%)
Capital Cost of CCGT with CCS in 2050 ($2492/kW)
Percentage change in Gas Prices (0%)
Cost of Carbon Sequestration ($45/tonne)
Efficiency of CCGT with CCS in 2050 (50.7%)
Time & Cost for Delay CCGT with CCS (0 years)
VOM CCS in 2030 ($14.7/MWh sent out)
NPV of CCGT with CCS in 2050 (M$AUD)Showing Values >= 100.0 M$AUD
13.0%
1.00
631
-10%
4797
-10%
4295
1.25%
129663
-10%
7.0%
- 1.00
158
15%
3393
10%
3044
0.50%
86408
10%
-$3,000 -$2,500 -$2,000 -$1,500 -$1,000 -$500 $0
Discount Rate Real Small Nuclear (10%)
Time & Cost for Delay Small Nuclear (0 years)
Nuclear Project Development Costs (315.7 AUD $M)
Exchange Rate Change from Expected Level (0%)
International Capital Cost of Small Nuclear Plant (USD$4007.75/kW)
Variation in Wholesale Price without Carbon (0%)
Local Capital Cost of Small Nuclear Plant (AUD$3587.5/kW)
Annual Escalation Factor for O&M (1.05%)
FOM Local Nuclear for small Plant in 2015 ($108035/MW)
% Change in Carbon Price from Most Likely Predictions (0%)
NPV of Small Nuclear in 2050 (M$AUD)Showing Values >= 200.0 M$AUD
13.0%
2.00
-10%
-10%
1.25%
3495
631
3844
118183
68880
7.0%
- 1.00
15%
10%
0.50%
2942
158
3229
78720
45920
-$10,000-$9,000-$8,000-$7,000-$6,000-$5,000-$4,000-$3,000-$2,000-$1,000 $0
Discount Rate Real Large Nuclear (10%)
Time & Cost for Delay Large Nuclear (0 years)
Exchange Rate Change from Expected Level (0%)
Variation in Wholesale Price without Carbon (0%)
Annual Escalation Factor for O&M (1.05%)
International Capital Cost of Large Nuclear Plant (USD$3167.25/kW)
Nuclear Project Development Costs (315.7 AUD $M)
Local Capital Cost of Large Nuclear Plant (AUD$3474.75/kW)
FOM Local Nuclear for large Plant in 2015 ($98502.5/MW)
FOM Overseas Nuclear for large plant in 2015 (USD$57400/MW)
NPV of Large Nuclear in 2050 (M$AUD)Showing Values >= 500.0 M$AUD
13.0%
-10%
20.0%
54.7%
1966.8
1.00
0.950
10%
7.0%
10%
-20.0%
62.1%
1311.2
-
0.975
-10%
-$200 -$100 $0 $100 $200 $300 $400 $500 $600 $700
Discount Rate Real CCGT (10%)
Variation in Wholesale Price without Carbon (0%)
Percentage change in Gas Prices (0%)
Efficiency of CCGT in 2050 (56.56%)
Capital Cost of CCGT in 2050 ($1639/KW)
Time & Cost for Delay CCGT (0 years)
MLF Small Plant (0.975 )
% Change in Carbon Price from Most Likely Predictions (0%)
NPV of CCGT in 2050 (M$AUD)Showing Values >= 100.0 M$AUD
DGA Consulting/Carisway
Nuclear Fuel Cycle Royal Commission – Appendix Report
Page 45
Sensitivity charts for IS2 in 2050
Monte Carlo charts for IS2 in 2050
13.0%
-10%
3489
20.0%
80
48.1%
1.00
17.6
-10%
7.0%
10%
1994
-20.0%
20
57.5%
-
11.8
10%
-$300 -$200 -$100 $0 $100 $200 $300 $400 $500 $600
Discount Rate Real CCGT CCS (10%)
Variation in Wholesale Price without Carbon (0%)
Capital Cost of CCGT with CCS in 2050 ($2492/kW)
Percentage change in Gas Prices (0%)
Cost of Carbon Sequestration ($45/tonne)
Efficiency of CCGT with CCS in 2050 (50.7%)
Time & Cost for Delay CCGT with CCS (0 years)
VOM CCS in 2030 ($14.7/MWh sent out)
% Change in Carbon Price from Most Likely Predictions (0%)
NPV of CCGT with CCS in 2050 (M$AUD)Showing Values >= 100.0 M$AUD
13.0%
1.00
631
-10%
4797
-10%
4295
1.25%
-10%
129663
7.0%
- 1.00
158
15%
3393
10%
3044
0.50%
10%
86408
-$3,000 -$2,500 -$2,000 -$1,500 -$1,000 -$500 $0
Discount Rate Real Small Nuclear (10%)
Time & Cost for Delay Small Nuclear (0 years)
Nuclear Project Development Costs (315.7 AUD $M)
Exchange Rate Change from Expected Level (0%)
International Capital Cost of Small Nuclear Plant (USD$4007.75/kW)
Variation in Wholesale Price without Carbon (0%)
Local Capital Cost of Small Nuclear Plant (AUD$3587.5/kW)
Annual Escalation Factor for O&M (1.05%)
% Change in Carbon Price from Most Likely Predictions (0%)
FOM Local Nuclear for small Plant in 2015 ($108035/MW)
NPV of Small Nuclear in 2050 (M$AUD)Showing Values >= 200.0 M$AUD
13.0%
2.00
-10%
-10%
1.25%
3495
631
3844
118183
68880
7.0%
- 1.00
15%
10%
0.50%
2942
158
3229
78720
45920
-$10,000-$9,000-$8,000-$7,000-$6,000-$5,000-$4,000-$3,000-$2,000-$1,000 $0
Discount Rate Real Large Nuclear (10%)
Time & Cost for Delay Large Nuclear (0 years)
Exchange Rate Change from Expected Level (0%)
Variation in Wholesale Price without Carbon (0%)
Annual Escalation Factor for O&M (1.05%)
International Capital Cost of Large Nuclear Plant (USD$3167.25/kW)
Nuclear Project Development Costs (315.7 AUD $M)
Local Capital Cost of Large Nuclear Plant (AUD$3474.75/kW)
FOM Local Nuclear for large Plant in 2015 ($98502.5/MW)
FOM Overseas Nuclear for large plant in 2015 (USD$57400/MW)
NPV of Large Nuclear in 2050 (M$AUD)Showing Values >= 500.0 M$AUD
13.0%
-10%
20.0%
54.7%
1966.8
1.00
0.950
10%
7.0%
10%
-20.0%
62.1%
1311.2
-
0.975
-10%
$0 $100 $200 $300 $400 $500 $600 $700 $800 $900
Discount Rate Real CCGT (10%)
Variation in Wholesale Price without Carbon (0%)
Percentage change in Gas Prices (0%)
Efficiency of CCGT in 2050 (56.56%)
Capital Cost of CCGT in 2050 ($1639/KW)
Time & Cost for Delay CCGT (0 years)
MLF Small Plant (0.975 )
% Change in Carbon Price from Most Likely Predictions (0%)
NPV of CCGT in 2050 (M$AUD)Showing Values >= 100.0 M$AUD
DGA Consulting/Carisway
Nuclear Fuel Cycle Royal Commission – Appendix Report
Page 46
Sensitivity charts for IS3 in 2050
Monte Carlo charts for IS3 in 2050
13.0%
-10%
3489
20.0%
80
1.00
48.1%
17.6
-10%
0.950
7.0%
10%
1994
-20.0%
20
-
57.5%
11.8
10%
0.975
$0 $200 $400 $600 $800 $1,000 $1,200 $1,400
Discount Rate Real CCGT CCS (10%)
Variation in Wholesale Price without Carbon (0%)
Capital Cost of CCGT with CCS in 2050 ($2492/kW)
Percentage change in Gas Prices (0%)
Cost of Carbon Sequestration ($45/tonne)
Time & Cost for Delay CCGT with CCS (0 years)
Efficiency of CCGT with CCS in 2050 (50.7%)
VOM CCS in 2030 ($14.7/MWh sent out)
% Change in Carbon Price from Most Likely Predictions (0%)
MLF Small Plant (0.975 )
NPV of CCGT with CCS in 2050 (M$AUD)Showing Values >= 100.0 M$AUD
13.0%
1.00
631
-10%
4797
-10%
4295
1.25%
-10%
129663
7.0%
- 1.00
158
15%
3393
10%
3044
0.50%
10%
86408
-$2,500 -$2,000 -$1,500 -$1,000 -$500 $0
Discount Rate Real Small Nuclear (10%)
Time & Cost for Delay Small Nuclear (0 years)
Nuclear Project Development Costs (315.7 AUD $M)
Exchange Rate Change from Expected Level (0%)
International Capital Cost of Small Nuclear Plant (USD$4007.75/kW)
Variation in Wholesale Price without Carbon (0%)
Local Capital Cost of Small Nuclear Plant (AUD$3587.5/kW)
Annual Escalation Factor for O&M (1.05%)
% Change in Carbon Price from Most Likely Predictions (0%)
FOM Local Nuclear for small Plant in 2015 ($108035/MW)
NPV of Small Nuclear in 2050 (M$AUD)Showing Values >= 200.0 M$AUD
13.0%
2.00
-10%
-10%
1.25%
3495
631
3844
-10%
118183
68880
7.0%
- 1.00
15%
10%
0.50%
2942
158
3229
10%
78720
45920
-$8,000 -$7,000 -$6,000 -$5,000 -$4,000 -$3,000 -$2,000 -$1,000 $0
Discount Rate Real Large Nuclear (10%)
Time & Cost for Delay Large Nuclear (0 years)
Exchange Rate Change from Expected Level (0%)
Variation in Wholesale Price without Carbon (0%)
Annual Escalation Factor for O&M (1.05%)
International Capital Cost of Large Nuclear Plant (USD$3167.25/kW)
Nuclear Project Development Costs (315.7 AUD $M)
Local Capital Cost of Large Nuclear Plant (AUD$3474.75/kW)
% Change in Carbon Price from Most Likely Predictions (0%)
FOM Local Nuclear for large Plant in 2015 ($98502.5/MW)
FOM Overseas Nuclear for large plant in 2015 (USD$57400/MW)
NPV of Large Nuclear in 2050 (M$AUD)Showing Values >= 500.0 M$AUD
13.0%
-10%
20.0%
54.7%
1966.8
1.00
10%
0.950
7.0%
10%
-20.0%
62.1%
1311.2
-
-10%
0.975
$0 $200 $400 $600 $800 $1,000 $1,200
Discount Rate Real CCGT (10%)
Variation in Wholesale Price without Carbon (0%)
Percentage change in Gas Prices (0%)
Efficiency of CCGT in 2050 (56.56%)
Capital Cost of CCGT in 2050 ($1639/KW)
Time & Cost for Delay CCGT (0 years)
% Change in Carbon Price from Most Likely Predictions (0%)
MLF Small Plant (0.975 )
NPV of CCGT in 2050 (M$AUD)Showing Values >= 100.0 M$AUD
DGA Consulting/Carisway
Nuclear Fuel Cycle Royal Commission – Appendix Report
Page 47