for the quantitative viability analysis of...

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

DGA Consulting/Carisway

Nuclear Fuel Cycle Royal Commission – Final Report Page 1

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

[email protected]

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.


Nuclear Fuel Cycle Royal Commission – Final Report

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



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



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




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




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




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



Page 8

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.



Page 10

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.



Page 11

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.



Page 12

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).



Page 13

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


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.



Page 14

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



Page 15

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.



Page 16

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.



Page 17

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


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).



Page 18

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.



Page 19

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



Page 20

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.



Page 21

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).



Page 22

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.



Page 23

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.



Page 24

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



Page 25

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.



Page 26

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.


Residential category

High 1.00% pa Estimate by DGA Consulting/Carisway.






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.


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


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.



Page 27

2030 time horizon


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%.


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%.


38 CSIRO/ClimateWorks estimate EV annual demand of 3,108GWh by 2030. Hence market penetration of (3,108/555)*5% = 28%.



Page 28

Figure 20: Projections of customer category profiles for a day of maximum demand in South Australia in 2030 and 2050



Page 29

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



Page 30

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.

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





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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.

http://alliance.columbia.edu/files/newalliance/content/ASC%20for%20the%20Smart%20Grid.pdf



Page 34

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.



Page 35

Table 9: Derivation and source of key parameters for 2030

2030 time horizon


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.



Page 36

2030 time horizon


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


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.



Page 42

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



Page 43

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


Technology

Photovoltaics (PV)

Interconnector Constraint




Page 44

>

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



Page 45

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.



Page 46

:

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



Page 48

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



Page 49

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

http://www.kingislandrenewableenergy.com.au/



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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



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 Baseload 6,397 2,635

Scenario 2


285 Baseload 2,220 102


1,125 Baseload 7,459 1,546

Scenario 3

285 Load following 643 1,363

285 Baseload 2,190 132


1,125 Baseload 7,282 1,752

Scenario 4


285 Baseload 2,134 188


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 Baseload 82% 11% 93%


1,125 Baseload 65% 27% 92%

Scenario 2


285 Baseload 89% 4% 93%


1,125 Baseload 76% 16% 91%

Scenario 3


285 Baseload 88% 5% 93%


1,125 Baseload 74% 18% 92%

Scenario 4


285 Baseload 85% 8% 93%


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.



Page 55

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.



Page 56

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 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


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



Page 59

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


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%


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)


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


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


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)


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


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


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)


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


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)


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


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


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


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


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)


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



Moderate climate


scenario (IS2)

Strong climate


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



Moderate climate


scenario (IS2)

Strong climate


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



Page 74

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



Page 75

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.



Page 76

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


$- $50.0 $100.0 $150.0 $200.0 $250.0


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



Page 77

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.



Page 78

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.



Page 79

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



Page 80

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.



Page 81

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)


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



Page 82

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)



International Capital Cost of Large Nuclear Plant (USD$3167.25/kW)


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



Page 83

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%)



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



Page 84

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



Moderate climate


scenario (IS2)

Strong climate


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




Moderate climate


scenario (IS2)

Strong climate


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


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.



Page 85




Moderate climate


scenario (IS2)

Strong climate


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




Moderate climate


scenario (IS2)

Strong climate


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


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



Page 86




Moderate climate


scenario (IS2)

Strong climate


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




Moderate climate


scenario (IS2)

Strong climate


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.




Moderate climate


scenario (IS2)

Strong climate


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



Page 87




Moderate climate


scenario (IS2)

Strong climate


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


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



Moderate climate


scenario (IS2)

Strong climate


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



Page 88

Table 49: 2050 load following mode



Moderate climate


scenario (IS2)

Strong climate


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


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



Moderate climate


scenario (IS2)

Strong climate


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.



Page 89

Table 51: 2050 social discount rate



Moderate climate


scenario (IS2)

Strong climate


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%.



Page 90

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.



Page 91

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


Small Nuclear

Large Nuclear

CCGT Baseload

LCOE $/MWh

IS3 2050 IS2 2050 BIS 2050 IS3 2030 IS2 2030 BIS 2030

https://www.amacad.org/pdfs/book_game_changers.pdf



Page 92

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


Small Nuclear

Large Nuclear

CCGT Baseload

LCOE $/MWh

IS3 2050 IS2 2050 BIS 2050 IS3 2030 IS2 2030 BIS 2030



Page 93

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


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


Small Nuclear

Large Nuclear

CCGT Baseload

LCOE $/MWh

IS3 2050 IS2 2050 BIS 2050 IS3 2030 IS2 2030 BIS 2030



Page 94

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.



Page 95

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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


Nuclear Fuel Cycle Royal Commission – Appendix Report

Page 1

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

[email protected]

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.



Page 2

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



Page 3

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.



Page 4

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.



Page 5

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


Business category


2013 to 2050.





Residential category


2013 to 2050.






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.


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.



Page 6

2050 time horizon


Hot water load


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%.



Page 7

Table B2: Source and derivation of technology variables for 2050

2050 time horizon


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.


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.


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 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.



Page 8

2050 time horizon



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.


Installed capacity

High 650MW Capacity upgrade by 2016.

Low 2,000MW NFCRC relaxed constraint.

Medium 1,180MW Sourced from Dr Mark Diesendorf19.


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.



Page 9

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.



Page 10

Figure C2: HH summary of transformer load by Postcode for the 2030 High Load Scenario together with maxima and minima load envelopes



Page 11

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



Page 12

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.



Page 13

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



Page 14

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



Page 15

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



Page 16

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.



Page 17

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


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


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.



Page 18

𝐸𝑉𝑂𝑆𝑗 = ∑ 𝑒𝑣𝑜𝑠𝑖,𝑗

48

𝑖=1


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



Page 19

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


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)


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)


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


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


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)


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)



Page 23

Power Exported to the NEM

Dispatch

Rank

Power dispatch


Boundary conditions


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.



Page 24

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



Page 25

Parameter Most

likely


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




2014/15 values.


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.


Nuclear project development costs

(A$m) 308 631 158




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



Page 26

Parameter Most

likely


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



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



Page 27

Parameter Most

likely


FOM for CCS in 2030 ($/MW) 42,868 51,442 34,292

EY provided FOM most likely figures based on



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



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%



Page 28

Parameter Most

likely


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.



Page 29

Parameter Most

likely


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.



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.



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.



Page 30

Parameter Most

likely


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.



Page 31

Parameter Most

likely


% 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 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.






Page 32

Parameter Most

likely


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.






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.



Page 34


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.



Page 35


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.



Page 36


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.



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.



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 -


Auxiliary percentage for TUoS (%) 5% WSP-PB- Initial Business Case and Cost Estimates -


TUoS charges at 500kV ($/MW) 94,500 WSP-PB- Initial Business Case and Cost Estimates -


TUoS charges at 275kV ($/MW) 61,975 WSP- PB- Initial Business Case and Cost Estimates -




Page 37


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.



Page 38


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



Page 39

Time series parameters

Table G3: Time series parameters for the NPV economic model


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.



Page 40


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.



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











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









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











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










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











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









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











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










Page 43


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










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











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












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







MLF Small Plant (0.975 )




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










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









FOM Local Nuclear for small Plant in 2015 ($108035/MW)



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










FOM Overseas Nuclear for large plant in 2015 (USD$57400/MW)


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












Page 45



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











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












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












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












Page 46



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












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












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













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












Page 47