electricity generation investment analysis information contained in this report is general in nature...

Electricity Generation Investment Analysis

Final Report

14 April 2011

Deloitte Touche Tohmatsu

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Deloitte: Electricity Generation Investment Analysis Page 1

Statement of Responsibility

This report was prepared for the Department of Resources, Energy and Tourism (DRET) for

the review of investment activities in the Australian electricity generation sector.

In preparing this Report we have relied on the accuracy and completeness of the information

provided to us by DRET and from publicly available sources. We have not audited or

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© 2011 Deloitte Touche Tohmat

Contents

Executive Summary 5

Scope of Work and an Overview of Our Approach 5

Key Messages 6

Literature Review and Historic Trends 7

Discussions with Market Participants 9

Modelling Approach and Scenarios 11

Model Results 13

1 Introduction 14

1.1 Scope of Work 14

2 Historical Analysis and Literature Review 16

2.1 Changes in Capacity Mix 16

2.2 Changes in Generation Mix 18

2.3 Changes in Reliability Performance 20

2.4 Impact of Policy Uncertainty 21

3 Methodology 26

3.1 Interviews with Market Players 26

3.2 Modelling Analysis 26

4 Summary of Discussions with Market Participants 34

4.1 Investment Decisions 34

4.2 Carbon Prices and Targets 36

4.3 Proposed CPRS Design 38

4.4 Impact of Policy Uncertainty 41

4.5 Attitude of Lending Institutions 43

4.6 Key Points 45

5 Model Results: CPU Estimates 47

5.1 Key Assumptions 47

D


5.2 Model Results: CPU Estimates 47

6 Appendix A: Market Participants Interviewed 50

6.1 List of Market Participants Interviewed 50

6.2 Key Questions 51

7 Appendix B: Illustrative Example of CPU 53

8 Appendix C: Detailed Modelling Assumptions 56

8.2 Capital Cost Assumptions 60

8.3 Fuel Price Assumptions (Real 2010 dollars per GJ) 60

8.4 Detailed Generator Data 61

D


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Acronyms

AEMC Australian Energy Market Commission

AEMO Australian Energy Market Operator

AER Australian Energy Regulator

CCGT Combined Cycle Gas Turbine

CCS Carbon Capture and Storage

CPRS Carbon Pollution Reduction Scheme

CPU Cost of Policy Uncertainty

DRET Department of Resources, Energy and Tourism

DSM Demand Side Management

ESAA Energy Supply Association of Australia

ETS Emissions Trading Scheme

Genco Generation Company

Gentailer Vertically integrated generator-retailer

IDGCC Integrated Drying Gasification Combined Cycle

IEA International Energy Agency

IGCC Integrated Gasification Combined Cycle

LRMC Long Run Marginal Cost

LTM Deloitte's Long Term Model

MRET Mandatory Renewable Energy Target

NEM Australian National Electricity Market

NERC North American Reliability Council

NGF National Generators Forum

OCGT Open Cycle Gas Turbine

PPA Power Purchase Agreement

QGS Queensland Gas Scheme

REC Renewable Energy Certificate

RET Renewable Energy Target

SRMC Short Run Marginal Cost

SWIS South West Interconnected System

USE Unserved Energy

WACC Weighted Average Cost of Capital

WEM Wholesale Energy Market


Executive Summary

Scope of Work and an Overview of Our Approach

Deloitte has been engaged by the Department of Resources, Energy and Tourism (DRET) to

undertake a review of investment activities in the Australian electricity generation sector. The

objective of the review is to examine any inefficiencies including sub-optimal investment in

power stations and/or future supply reliability concerns that may have arisen out of the

continuing uncertainties surrounding the introduction of a carbon price. Our work is also related

to advice on energy sector investment being provided by the Investor Reference Group (IRG)

and some of the assumptions and policy scenarios, that form part of our analysis, reflect the

views expressed by the IRG.1

The major focus of the study is the investment trend in the Australian National Electricity Market

(NEM), but the analysis extends to other power systems in Australia, most notably the

Wholesale Energy Market (WEM) in Western Australia. Specific issues that are covered in the

present work include:

A review of historical investment decisions and how uncertainties surrounding a carbon

price regime may have impacted on these decisions;

Eliciting views of market participants on the impact of carbon policy uncertainties on their

investment decisions; and

Quantitative analysis to estimate the cost of policy uncertainty (CPU) going forward.

Our methodology comprises the following three steps:

A survey of the existing literature including national and relevant international studies;

Discussion with participants from the power and banking industry including generators,

industry bodies and major commercial banks; and

Market modelling to assess the short, medium and long-term cost implications of sub-

optimal investment in electricity generation.

We have first stated the key messages from our study followed by a summary of the findings for

each of the three areas above.

1 IRG has been appointed by the Minister for Resources and Energy to advise on energy sector

investments. Further information on IRG is available on the DRET website:

http://www.ret.gov.au/energy/Documents/IRG_Fact_Sheet_FINAL.doc



Key Messages

Major findings of our analysis are as follows:

Distinct shifts in fuel and technology have already taken place: Analysis of

investment trend and generation mix over the last decade reveals that the Australian

electricity industry has gone through major changes in investment pattern. There has

been a paradigm shift from coal to gas over 2000-2005, followed by a combination of

gas and renewable in more recent years. The reliability performance of the system has,

however, remained strong through this period of changes in fuel and technology. New

investment has occurred and going by the list of proposed and anticipated projects, will

continue to occur.

National and international studies as well some of the opinions expressed by market

participants in Australia suggest that:

o uncertainties around policy would discourage investment in capital-intensive

baseload technologies, and low-capital flexible investment options would

instead be favoured; and

o The recent trend of renewables and gas is a reflection of the Renewable

Energy Target (RET) policy as well as a tendency for investors to minimise

“capital at risk”.

Baseload gas generation investment will continue to be affected due to policy

uncertainty: Policy uncertainty around carbon pricing has been cited as the most

significant concern in most cases, although several market participants also observed, it

is a combination of both carbon and RET policies that has driven their investment

decisions. Owners of existing coal power stations have ruled out the possibility of

building any new coal-fired power station in the foreseeable future. Banks have echoed

their views citing the massive uncertainty on revenue from coal-fired generation. In fact,

our discussions with prospective investors in gas generation suggest the revenue

uncertainty is more endemic than just coal investment. Baseload gas investment such

as a Combined Cycle Gas Turbine (CCGT) plant also has a considerable degree of risk,

especially if the developer does not have an upstream gas position to have some

control over input costs. Since the market as a whole needs new capacity to meet new

load growth and render the system secure, especially with the influx of significant

intermittent generation, the onus naturally falls on peaking Open Cycle Gas Turbine

(OCGT) as a low capital and arguably flexible form of investment.

Cost of policy uncertainty depends on a number of factors: The impact of policy

uncertainty therefore goes beyond a shift in fuel, i.e., less coal and more gas. It would

also affect the type of gas plant being built and how these plants are operated. The cost

of policy uncertainty needs to reflect this. As no formal carbon policy is in place,

investment in CCGT as well as other capital-intensive baseload plant is risky. Since the

delivered cost of electricity to final consumers from OCGT to meet new load growth

would come at a significantly higher cost compared to baseload CCGTs, the societal

cost can also be material. The cost of policy uncertainty is likely to grow over time with

load growth because there is a higher baseload generation requirement and hence the

lack of investment in baseload power station has higher opportunity costs. Cost of

policy uncertainty would also depend on other factors including the entry of renewable


generation, gas prices and expected carbon reduction. As part of our present analysis,

we have assumed that the RET target will be met by 2020, gas prices will be linked to

international gas market in the long term, and carbon reduction targets by 2030 as

envisaged in the proposed “CPRS-5” scenario would be met.2

Cost of policy uncertainty (CPU) estimates range from $1-2 billion per year in the

short- to medium-term but can be much higher at $5 billion per year in the longer

term: Ultimately, the cost of policy uncertainty hinges on whether baseload investment in

any form is needed in the first place which is an empirical issue. We have assessed the

difference between investment under a “Policy Uncertainty” scenario and an optimal

portfolio of investment under “Policy Certainty”. We have undertaken a market modelling

analysis to assess CPU assuming baseload generation investment would stall for a period

due to uncertainty. We have constructed a forward-looking analysis in such a way that the

only difference between the Policy Certainty and Policy Uncertainty scenarios is the carbon

policy that restricts baseload gas investment until 2017 (short-term), or 2020 (medium-

term), or 2025 (long-term). In all three cases, we have assumed the same RET and carbon

reduction target. The RET policy would have a considerable impact on generation

investment going forward and accounts for a very significant share of additional energy

requirement till 2020. The balance requirement of energy over and above the part met by

renewable generation is relatively small. Hence, the baseload generation requirement is

greatly diminished, which in turn reduces CPU. The key findings of our modelling analysis

are summarised below:

o Firstly, we have assessed the impact of restricting baseload gas investment

until 2017. An early resolution of uncertainty will add below $5 per Mega

Watt-hour (MWh) to the (wholesale) cost of energy, if baseload investment

resumes by 2017;

o Secondly, we have examined the impact of delaying baseload investment to

2020. The additional delay nearly doubles CPU to approximately $2 billion

per year by 2020;

o Finally, we have also constructed a scenario around further continuation of

uncertainty at the request of the IRG, which suggests CPU could escalate

to almost $5 billion by 2025.

Literature Review and Historic Trends

Our review of literature in both an Australian and an international context reveals the following

key points:

Investment in “green-field” coal generation has stalled: There has been a

noticeable change in baseload generation investment in Australia away from coal,

driven by climate change policies among other things. Increased level of gas and wind

generation has significantly altered the dominance of coal-fired capacity development in

the recent past. There are instances of new coal projects being proposed and shelved

since 2005, and several thousand Mega Watts (MW) of gas-based investment have

taken place over the last few years. Market participants in some instances have made

2 Carbon Pollution Reduction Scheme (CPRS-5) scenario CO2 targets are based on the following analysis: MMA,

Impacts of the Carbon Pollution Reduction Scheme on Australia‟s Electricity Markets, Report to Federal Treasury, 11

December, 2008., p.3.


public statements about climate change policy uncertainties shaping their decisions on

generation investment;

(But) electricity generation mix has not changed significantly yet: However, there

has been a less drastic change in generation mix – the share of coal-based generation

has largely stabilised and diminished slightly over the last few years;

There is no significant reliability concern to date: The system reliability has also

generally been maintained well within the standard through a period of change in

investment pattern. Although there have been significant outages in January 2009,

these events were associated with a “1 in 100 year” weather event that is well outside

the current planning standard. That said, we recognise that beyond the economic merits

of coal versus CCGT/OCGT for energy generation, the policy uncertainty also has

broader implications for system reliability. System security and reliability is a function of

the age of current fleet of generators, generation/demand response technology

employed and their characteristics (e.g., conventional versus intermittent generation,

non-firm demand side response). Although our review of recent Australian electricity

market history does not reveal any immediate cause for alarm, significant changes in

generation mix including large scale intermittent generation entry raises potential

reliability issues that need to be closely monitored going forward;

Cost of policy uncertainty estimates vary significantly: While the available studies

both nationally and internationally indicate some degree of sub-optimality in generation

investment under policy uncertainties, the precise form and magnitude have been

debated. There have been two recent studies for the National Electricity Market (NEM),

namely Nelson et al (2010) from AGL Energy and Frontier Economics (2010). 3 Both

these studies highlight that consideration of renewable energy policy may have a

substantial impact on cost of carbon policy uncertainty. Absent any consideration of the

RET, there is typically a much greater need for generation. Therefore, RET effectively

reduces the requirement for (non-renewable) generation, which in turn would reduce the

cost impacts associated with delayed baseload generation investment. The study by

Nelson et al (2010a) from AGL, for instance, has estimated the impact of uncertainty to

be significant at $8.60 per MWh of rate impact (or, $2 billion per annum) absent any

consideration of RET, but significantly lower at $1.15 per MWh with RET. The AGL

Study did not consider any carbon target/price. Frontier Economics (2010) has used a

more detailed modelling tool to estimate the cost of policy uncertainty at $3.40 per MWh

(for New South Wales) including the impact of RET and a carbon price being introduced

from 2014. RET and carbon targets/prices have therefore been identified as major

drivers that determine the cost of policy uncertainty.

International studies also generally support that cost of policy uncertainty can be

significant:

o The international literature concurs that the sub-optimality of investment would

occur under policy uncertainty because there is less capital at risk. Studies

undertaken by the International Energy Agency (IEA), Oxford Energy Institute

and Stanford University have shown that policy uncertainty may lead to more

3 References:

1. T. Nelson, S. Kelly, F. Orton and P. Simshauser (2010a), Delayed Carbon Policy Certainty and Electricity

Prices in Australia, Economic Papers, Vol 29, No 4, December 2010, pp.446-465 We have interchangeably

referred to it as the “AGL study” in the remainder of this report.

2. Frontier Economics, What‟s the Cost of Carbon Uncertainty, Frontier Economics Report, November 2010.


expensive short-term investment that entail greater flexibility to adapt to future

changes; and

o The international studies also suggest that the changes in investment need to

be assessed in a more holistic sense including other policy changes around

renewable, smart grid and the collective impact of these policies on system

reliability. The collective impact may explain a higher level of investment in

peaking generation to best meet all policy requirements.

Discussions with Market Participants

We have interviewed over 10 organisations covering a mix of generation companies, including

pure generation businesses as well as generation companies who have retail, and/or upstream

fuel positions. The gencos also differ in terms of fuel mix, location across NEM regions and

Western Australia. We have also interviewed banks and power industry bodies such as the

National Generators Forum (NGF). The general theme of the discussions was based around the

following three key issues:

How did the organisation go about planning new investment and operation and

maintenance of existing assets?

What would constitute policy certainty for the organisation?

What was the attitude of lending institutions towards supporting baseload generation

investment?

The key findings from our discussions with market participants are as follows:

Certainty on carbon reduction policy design is the most critical issue

Certainty around the basic form of the carbon reduction policy, i.e., tax or permits, is

paramount. All market participants noted the importance of minimising regulatory risks.

Apart from clarity and upfront disclosure of the complete legislation, the preferred attributes

of the scheme design were stated as:

o Market-based with minimal non-market intervention and minimal regulation; and

o Minimal disruption to the operation of the NEM.

The issue of compensation for existing assets was primarily raised by the brown coal

generators. There was a general view that the government may “buy out” a substantial part

of the emissions from brown coal to achieve the near term emissions target and also impart

some degree of certainty to the market. Absent such measures that would effectively force

shutting down some of the generators, gencos expressed their intent to keep these power

stations operational well into the 2030‟s.

One of the major banks noted that international permits would be critical to achieve the

emission reduction target in the long term (i.e., beyond 2020) at the lowest cost. An orderly

transition mechanism as well as international permits was deemed to be critical to the

success of the scheme.


Views on generation investment – coal versus gas

Although uncertainty on carbon policy was one of the key factors, all market participants

noted that it is a combination of factors rather than carbon policy alone, which is causing a

lack of investment in baseload generation investment. Other drivers that were raised

included:

o Low pool prices;

o Uncertainties around gas prices; and

o The impact of enhanced RET that is taking up a significant share of the market.

None of the coal-based generation companies in the NEM that we have interviewed was

considering any new investment in coal-fired power stations. The situation seemed to be

worse for some generators than others. The current state of uncertainty had reduced growth

capex from what used to be substantial amounts (e.g., in several hundred million dollars for

each of the major gencos). The situation was different in Western Australia where a major

refurbishment of an existing coal-fired power station is being planned. However, market

participants from WA stated that coal based development in the region was being driven by

the high gas prices in the state and certainty on long term coal contracts that are not linked

to the global coal market.

On the other hand, three major gentailers with significant gas generation portfolios stated

that they have major gas-fired generation projects in planning and development stage.

However, the climate policy uncertainty is having an impact on the type of gas plants that

may be built and hence added costs of up to $2 billion per year to the system arising from

policy uncertainty. For instance, AGL has stated the following: “Without mandatory

performance standards that reflect the long-term emission reductions required or a broad-

based ETS with long term targets, “investment paralysis‟ is entirely predictable. This

effectively leaves investors with one option for investment to ensure security of supply,

OCGT plant, because it minimises “capital at risk”.4

Nevertheless, there are countervailing views from some market participants that suggest

the extent of sub-optimality at $2 billion per year is an “overestimate”. This opinion in part

stems from the view that there are other policy constraints including the RET and

Queensland Gas Scheme (QGS) that also contribute to sub-optimality.

Views on carbon prices

All market participants emphasised a need to have clarity on the emissions reduction target

and noted that it is impossible to form a view on carbon prices absent the target. That said,

in response to our question on specific target and carbon prices, the predominant view was

to adopt the proposed “CPRS-5” reduction scenario and Treasury carbon prices with an

assumption that the introductory year will have a fixed CO2 price in the range $10-30 per

tonne. CPRS-5 rather than “CPRS-15” is considered “realistic” given that even the former

4 T. Nelson, S. Kelly, F. Orton and P. Simshauser, Delayed Carbon Policy Certainty and Electricity Prices

in Australia, AGL Report, 2010. P.8


requires approximately 27 per cent reduction in the electricity sector relative to the

Reference scenario and therefore is a fairly challenging and expensive task in itself.5

Variable carbon prices with a reasonable degree of certainty and “orderly development” in

the next 3-5 years to reach a firm 2020 target are generally desirable.

Attitude of lending institutions

There has been a noticeable reduction in financing activities for power generation. Some

generators noted that all banks are much more stringent on pure merchant plants. Banks

have also noted their preference for investments backed up by long term power purchase

agreements (PPA).

One of the major banks noted that the continued shift in carbon policy has made it

extremely difficult for lending institutions to assess the risk faced by fossil fuel generation.

Even renewable projects that are not backed up by PPAs are not attractive in Australia

given the swings in Renewable Energy Certificate (REC) prices.

Banks have commented that the days of financing fully merchant stand-alone baseload

projects such as Pelican Point, Callide and Millmerran have ended. Only those generators

who have the ability to inject significant equity and can absorb the risk through their retail

position are in a position to invest in Australia. However, major gentailers also share

significant concerns, at least for baseload gas investment. Most market participants with an

interest in overseas projects, especially in Asia, noted that the terms they can get in other

countries are more favourable than those in Australia.

That said, finance is generally available and in particular for wind generation projects,

subject to these being underwritten by long-term contract. It is more difficult for green-field

coal generation projects although only one bank to date has publicly announced their

unwillingness to provide finance for coal generation projects. Major issues noted by market

participants in relation to investment in baseload generation are:

o Significant unpredictability of revenue from these assets. Commonwealth Bank has

recently written down its 2 per cent share in Hazelwood down to zero in view of the

uncertain future of the asset;

o Short-term debt facilities with typical maturity period of three years is not suited for

most baseload investment including coal; and

o Gearing for baseload power stations has declined from 65-70 per cent in 1995/96 to

40-45 per cent today.

Modelling Approach and Scenarios

The purpose of the modelling exercise is to develop an objective assessment of the cost of

policy uncertainty. While historic data and views expressed by market participants provide a

useful context and insights, modelling is necessary to form a forward-looking “system-wide”

impact of policy uncertainty and assess CPU.

5 MMA, Impacts of the Carbon Pollution Reduction Scheme on Australia‟s Electricity Markets, Report to Federal

Treasury, December, 2008.


We have used Deloitte‟s Long Term Model (LTM) to undertake the modelling analysis. LTM

performs an inter-temporal optimisation of the Australian electricity sector (including NEM,

Western Australia and Northern Territory systems) till 2030 incorporating a generation

investment and dispatch optimisation.

Scenario definitions

One of the major outcomes that we have tried to capture through our modelling is the sub-

optimality of generation investment. The degree of sub-optimality is measured as the difference

in system cost across the following two scenarios:

a. We have developed a “Policy Certainty” counterfactual case that is a

hypothetical view of the system if carbon policy were known with certainty. This

scenario involves simulating investment and dispatch over 2000-2030 without

any restriction on baseload investment;

b. We have developed a “Policy Uncertainty” scenario that

- uses the actual investment schedule over 2000-2010 and simulates

dispatch and emission outcomes; and

- simulates future capacity expansion over 2011-2030 and supply

costs incorporating restrictions on baseload generation investment

that may be encountered due to policy uncertainty.

We have created three Policy Uncertainty sub-scenarios, namely:

Scenario 1 – Short-term uncertainty: We assume the proposed CPRS

may be implemented at some point over the next few years for

baseload investment to resume from 2017; and

Scenario 2 – Medium-term uncertainty: We assume the proposed

CPRS may be implemented at some point over the next few years for

baseload investment to resume from 2020; and

Scenario 3 – Long-term uncertainty: At the request of Investor

Reference Group (IRG), we have also constructed a scenario of

continued uncertainty that would render baseload investment to be

stalled till 2024. This scenario is intended to capture how CPU may

escalate over the years.

Finally, we have calculated CPU that reflects the difference in system costs between Policy

Uncertainty and Policy Certainty scenarios. This includes difference in capital, operating and

any unserved energy costs. We have used the following definition of system cost (or,

alternatively termed as resource costs) and cost of policy uncertainty:

Total system cost (TSC) = Annualised capital cost for new investment + Fuel costs + Variable and fixed operations and maintenance costs + Cost of unserved energy Cost of Policy Uncertainty (in dollars) = TSCPolicy Uncertainty – TSCPolicy Certainty


Model Results

A comparison of system costs between Policy Uncertainty and Policy Certainty scenarios over

the years reveals the CPU trend. Figure 1 shows the total annual cost of policy uncertainty (in $

million).

Figure 1: Cost of Policy Uncertainty: Undiscounted (real 2010) $ million

We observe that:

An early resolution of uncertainty would limit damage quite considerably. For instance,

CPU is at the most $1.2 billion per year for Scenario 1, but is close to $5 billion if

baseload investment is delayed significantly till 2025.

The average CPU estimates, calculated as the average cost impact per MWh of

wholesale supply, show that

o the average cost to the market of this uncertainty is $4.73 per MWh in 2016 for

Scenario 1. If we express this increase in cost as a percentage of residential

tariff of $188 per MWh in 2009, short-term CPU represents a 2.5 per cent

increase in residential tariff in 2016;6 but

o the cost impact would be over $16 per MWh, if baseload investment is

significantly delayed till 2025 in Scenario 3.

For all Policy Uncertainty scenarios, CPU decreases over a 3-4 year period once

baseload investment resumes;

6 Retail price cited in ABARES, Energy in Australia 2011, 2011.

0

1000

2000

3000

4000

5000

6000

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f P

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ain

ty (

$ m

illio

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Scenario 2: Baseload 2020 - 100% RET



1 Introduction

1.1 Scope of Work

Deloitte has been engaged by the Department of Resources, Energy and Tourism (DRET) to

undertake a review of investment activities in the Australian electricity generation sector. The

objective of the review is to examine any inefficiencies including sub-optimal investment in

power stations and/or future supply reliability concerns that may have arisen out of the

continuing uncertainties surrounding the introduction of a carbon price. Our work is also related

to advice on energy sector investment being provided by the Investor Reference Group (IRG)

and some of the assumptions and policy scenarios reflect the views expressed by the IRG.7

Specific issues that need to be covered in the proposed review include:

A review of historical investment decisions and how uncertainties surrounding a carbon

price regime may have impacted on these decisions;

Eliciting views of market participants on carbon policy and its impact on the Australian

power industry; and

Modelling analysis to estimate the cost of policy uncertainty.

Specific tasks undertaken to address these issues include:

A historical analysis of generation investment decisions covering 1999-2010 to identify any

discernible trend in investment pattern. We have analysed the historic trend in generation

investment, publicly available estimates of the projected cost of policy uncertainty and also

key international studies that include relevant analyses and observations on the impact of

policy uncertainty;

Interviews with relevant stakeholders including generators and potential investors,

government departments and major energy bodies; and

Forward-looking analysis, focusing on the period up to 2030.

Figure 2 provides a schematic description of the approach adopted.

7 IRG has been appointed by the Minister for Resources and Energy to advise on energy sector investments.

Further information on IRG is available on the DRET website: http://www.ret.gov.au/energy/Documents/IRG_Fact_Sheet_FINAL.doc



Figure 2: Key Elements of Methodology

The remainder of the report is organised as follows:

Section 2 provides a review of historical investment trend and presents findings of

national and international studies on policy uncertainty;

Section 3 presents the methodology that we have adopted;

Section 4 summarises the findings of our discussions with market participants, industry

bodies and financial institutions; and

Section 5 summarises the cost of policy uncertainty estimates derived using Deloitte‟s

electricity model.

Literature Review, Historic Analysis

and Data Development

Interviews with Electricity Market

Participants and Apex Bodies

Scenario Development

Modelling Analysis


2 Historical Analysis and Literature Review

The Australian electricity supply industry has historically benefitted from an abundance of

relatively cheap primary energy resources and a steady growth in electricity demand. However,

the recent financial crisis combined with carbon policy uncertainty has unsettled the investment

climate. As a recent review by Deloitte (2010) titled “Energy Security 2010-2020: Overcoming

investor uncertainty in power generation” published in August 2010 noted, “...in the last 18 or so

months the Australian electricity generation sector has entered a period of unprecedented

uncertainty after 14 years of operating in a relatively constant investment climate, and producing

and supplying power at very competitive costs by international standards”. 8

We have reviewed the recent data and studies to provide an overview of the trends in capacity,

generation/fuel mix and reliability for the Australian power sector.

2.1 Changes in Capacity Mix

Figure 3 and Figure 4 summarise the generation capacity including existing capacity as well as

plants that are under construction, or are in advanced planning stage. We note that:

Investment in coal generation has slowed down over the past 11 years. The total coal

capacity addition over 1999-2010 is 3,900 MW, compared to existing capacity of over

25,000 MW prior to 1999. Although new coal investment has taken place as recently as

2009 with Bluewaters II coming online in Western Australia, and Kogan Creek

(Queensland) in 2007, these investment decisions largely preceded the carbon policy

debate. Since the start of the Emissions Trading Scheme (ETS) debate, new coal

projects in the NEM have hardly been discussed. There is also some discussion

underway on closure of existing coal-fired power stations. International Power has, for

instance, indicated that shutting down Hazelwood power station in Victoria would

require governments to support the phased closure of all generating units over an

agreed term, in return for a fixed capacity payment.

OCGTs form a significant share of new investment in recent years.

A significant part of the investment over 1999-2003 was driven by government

initiatives. For instance, government owned generation companies in Queensland have

developed substantial baseload coal generation capacity in the state through late

nineties.

The investment scenario in WA is however different where refurbishment of mothballed

power stations are being planned. Coal is significantly more competitive in the region

8 Deloitte Energy Security 2010 report is available online:

http://www.deloitte.com/assets/Dcom-Australia/Local%20Assets/Documents/Industries/Energy%20and%20resources/Deloitte_Energy_Security_2010.pdf


because of WA‟s high gas prices. Verve Energy is planning to refurbish Muja A and B

coal plants (240 MW from 2013) and also looking at investment in High Efficiency Gas

Turbines (Kwinana – HEGT) and renewable projects (namely, Grasmere Wind Farm, 14

MW from 2012; Mumbida Wind Farm, 55 MW from 2013 and Greenough River Solar

PV, 10 MW from 2012).

There has been a remarkable uptake in gas based capacity including a large quantum

of gas projects being commissioned, to the tune of 4,500 MW in 2009/10. This trend

has been foreshadowed in discussions since the start of the ETS, including more recent

statements made by Origin that suggested “gentailers” will need a switch to gas based

generation capacity as part of a hedging strategy against uncertain carbon policies.9

Investment in wind also increased substantially, especially since 2008 - prior to 2008

only 901 MW of wind was installed compared to 2,850 MW being targeted by 2013.

Future projects under construction or in advanced stages of planning are primarily gas,

or wind, as we discuss further below.

Figure 3: Capacity Installed, Under Construction or in Advanced Planning (All Australia): Technology Mix

Source: Data compiled from ESAA Electricity and Gas Statistics 2010

Looking ahead, some of the recent trends are likely to continue. Specifically,

Investment in gas and renewable generation is likely to continue;

o Gas based capacity is projected to dominate new investments with 4,850 MW

capacity projected to enter the market over the next 3 to 4 years. There is, in

fact, over 18,000 MW of gas-based generation projects if we consider all

proposed developments although many of these projects are competing for the

same market share;

o There are few coal projects with only ~700 MW of proposed coal projects;

o Wind will also continue to be a preferred investment choice as there are many

opportunities for investment with a moderate investment cost. AEMO‟s

assessment of relative merits of renewable technologies identifies wind as

having moderate cost and being a nearly mature technology with “unlimited”

resource potential. AEMO's estimate suggests 5,500 MW of wind capacity is

9 For example, statement by Grant King of Origin Energy in his presentation at a CEDA seminar to the

Committee for Economic Development of Australia‟s CEO Vision Series, April 13, 2010.

0

500

1000

1500

2000

2500

3000

3500

4000

4500

Ca

pa

cit

y (M

W)

Other

Wind

Water

Solar/diesel

Oil Products

OCGT

CCGT

Coal Seam Methane

Black Coal


needed to meet the RET, as per the Statement of Opportunities published by

AEMO in October 2010;

Figure 4: Capacity Installed, Under Construction or in Advanced Planning (All Australia) – Composition of Public and Private Investment

Although the investment forecast highlights a significant change in fuel and technology

mix, recent AEMO assessments found installed and committed capacity in the NEM is

sufficient until 2013/14 to meet the reliability standard. This view is also reflected in the

State of the Energy Market study published by the AER. We also note that:

o QLD will be the first state to require new investment beyond committed projects

by 2015/16, due to economic growth and also in part due to the retirement of

Swanbank B; and

o VIC and SA require additional capacity by 2015/16, NSW by 2016/17, and TAS

by 2019/20.

Key investment drivers that have been noted in the recent industry discussions and also

reflected in AEMO, AEMC and AER publications include:

o Climate change policies;

o Emergence of new generation technologies;

o RET; and

o Other issues including smart meters, smart grids, introduction of electric

vehicles and energy efficiency schemes.

2.2 Changes in Generation Mix

We have reviewed the available data and studies on fuel and generation mix in Australia.

Figure 5 and Figure 6 summarise the major trends based on the Energy Supply Association of

Australia (ESAA) Statistics including:

Fuel mix at a high level has not changed significantly indicating that the current

generation system is still heavily dominated by coal-based generation. In comparison

to the change in capacity mix, the generation mix has not changed as much in relative

terms since 2005;

0

500

1000

1500

2000

2500

3000

3500

4000

4500

Ca

pa

cit

y (M

W)

Combination

Government

Private


There has been, however, a slight decline in black coal generation although brown coal

generation remains largely unchanged over the past five years. It however shows that

the share of coal generation has stabilised. This marks a significant departure from a

rising trend in coal-based generation for three decades prior to 2000;

Wind and gas generation, on the other hand, are steadily increasing, albeit these still

account for a relatively small share of overall generation.

In order to provide a context to the discussion on cost of policy uncertainty, we have

calculated an implied cost of carbon associated with the observed displacement of coal

with gas. We have used available estimates of short run marginal cost (SRMC) of

generation of existing coal and gas generators and their emission intensity to calculate

an implied cost of CO2 that would be associated with a switch from coal to gas

generation.10

A switch from brown coal with emission intensity of 1.5 tonne per MWh but

SRMC below $5 per MWh, to lower emitting CCGT with SRMC of $30-35 per MWh

(associated with gas price of $4.0-$4.5 per GJ), implies a cost of CO2 below $30 per

tonne. A switch from a less efficient black coal power station with SRMC in the range of

$14-20 per MWh to gas also incurs a cost below $30 per tonne of CO2, and may also

be below $20 per tonne of CO2 in some cases.

Figure 5: Fuel Use in the National Electricity Market

Source: ESAA Electricity and Gas Statistics 2010

Figure 6 compares generation mix between 05/06 to 08/09, and shows:

10

The implied cost of CO2 is analogous to the concept of a shadow price of carbon, albeit the options are restricted to

generation rescheduling among existing generators.


o more wind and gas generation in 08/09 than 05/06; and

o While coal generation in NSW has increased slightly, it has remained fairly

stable in all other regions.

Figure 6: Generation Mix 2005/06 and 2008/09

Source: ESAA Electricity and Gas Statistics 2010

2.3 Changes in Reliability Performance

We have looked at the reliability trend primarily in the Australian NEM in order to identify

any noticeable deviation in supply standard in recent years.

NEM bulk/wholesale supply reliability has generally been satisfactory as the recent

Reliability Panel review notes:

.. since the commencement of the NEM, the security and reliability of electricity

supply has been sound. Technical performance has been maintained and

market signals have promoted acceptable performance against the Reliability

Standard. Over the past 10 years, the average annual USE was well within the

Reliability Standard of 0.002% for all regions and for the NEM as a whole.”11

The Reliability Panel‟s Annual Report in 2008/09 noted that the load shedding events in

VIC and SA on 29-30 January, 2009 caused the USE to exceed 0.002 per cent in both

regions for the 2008/09 fiscal year. However, they had noted that the long term

Reliability Standard was not breached due to this load shedding. In particular, AEMO

had concluded that the temperature observed on January 29-30, 2009 were more

consistent with a 1 per cent probability of exceedance (POE) event and also

recommended reviewing the implications of reduced generator capacity at high

temperature and Basslink availability for projected assessment of system adequacy

(PASA) calculations.12

Table 1 provides further details on regional reliability level over the past 10 years.

Average unserved energy over the past 10 years was well within the standard. The

NEM reliability standard is designed to cover 1 in 10 year extreme demand events. The

11

Source: Review of the Reliability and Emergency Reserve Trader (RERT), Reliability Panel AEMC. 12

AEMC, Annual Market Performance Review 2008-09, December 2009.

0

10000

20000

30000

40000

50000

60000

70000

80000

NSW VIC QLD SA WA TAS NT SNY

Ge

ne

rati

on

(G

Wh

)

Wind

Solar

Oil products

Natural gas

Coal seam methane

Brown Coal

Black Coal

Biomass

Hyrdo

0

10000

20000

30000

40000

50000

60000

70000

80000

NSW VIC QLD SA WA TAS NTG

en

era

tio

n (

GW

h)

Wind

Solar

Oil products

Natural gas

Coal seam methane

Brown Coal

Black Coal

Biofuels

Hydro

Generation Mix 2005/06 Generation Mix 2008/09


drop in reliability in 2008/09 therefore should be recognised as an extreme event

beyond the planning standard;

Investment has kept pace with demand and overall wholesale reliability of the system

has been maintained;

The extent of new and proposed investment in intermittent generation (mainly wind) has

raised concerns about system security and reliability. Since 31 March 2009, new wind

generators greater than 30 MW are classified as semi-scheduled. This allows AEMO to

limit the output of these generators if necessary to maintain system integrity. Although

our review of recent Australian electricity market history does not reveal any cause for

alarm, potential lack of baseload investment coupled with large scale entry of

intermittent generation are areas of concern that need to be closely monitored going

forward. It is also an issue that is getting considerable attention internationally including

much larger interconnected systems in North America, as we have discussed later.

Table 1: NEM Reliability Standard (percentage of unserved energy)

QLD NSW VIC SA TAS

2009/10 0.0000% 0.0000% 0.0000% 0.0000% 0.0000%

2008/09 0.0000% 0.0000% 0.0040% 0.0032% 0.0000%

2007/08 0.0000% 0.0000% 0.0000% 0.0000% 0.0000%

2006/07 0.0000% 0.0000% 0.0000% 0.0000% 0.0000%

2005/06 0.0000% 0.0000% 0.0000% 0.0000% 0.0000%

2004/05 0.0000% 0.00005% 0.0000% 0.0000% 0.0000%

2003/04 0.0000% 0.0000% 0.0000% 0.0000%

2002/03 0.0000% 0.0000% 0.0000% 0.0000%

2001/02 0.0000% 0.0000% 0.0000% 0.0000%

2000/01 0.0000% 0.0000% 0.0000% 0.0000%

1999/00 0.0000% 0.0000% 0.0004% 0.0019%

Average 0.0000% 0.0000% 0.00044% 0.00051% 0.0000%

Source: Review of the Effectiveness of NEM Security and Reliability Arrangements in light of Extreme Weather Events, AEMC, 31 May, 2010.

2.4 Impact of Policy Uncertainty

Deloitte (2010) undertook a qualitative assessment of investment in baseload generation in

Australia.13

This work noted that there is a significant capital expenditure to the tune of $68-69

billion expected in the generation sector over the next five years. 14

However, investors do not

have sufficient certainty to invest in coal or even gas generation technologies, given the impact

a price on carbon is likely to have on future returns. The Deloitte study has collected information

on the likely impact of ETS on a typical black coal plant from three recent studies that shows the

value impact could be anywhere from +$923 million to $(-)915 million. The Deloitte study also

collated information on likely changes in capacity and generation mix. The analysis shows that

by 2029/30, gas based generation is likely to substitute for coal to meet new demand growth.

Coal generation is expected to decline from nearly 80 per cent of total generation to 50 per cent

over the next 20 years. Gas is likely to be the major alternative, up to an estimated cost of CO2

of $50 per tonne.

13

Deloitte, Energy Security 2010-2020, August 2010. 14

ESAA 2009 estimate that includes refinancing, capex on existing and new units, climate change related investment and financing permits. Also cited in Deloitte (2010), Table 2.2, page 20.


However, the choices between CCGT and OCGT technologies remain wide open under policy

uncertainty, and more generally investment in any form of capital-intensive baseload generation

requires a closer scrutiny. Carbon price can also have a reasonable impact on a baseload gas

generator, especially considering all of the uncertainties associated with access to, and the cost

of, capital, gas price risks, demand side risks and transmission risks over and above policy

uncertainties.

As AGL put it succinctly in one of their recent papers “Without mandatory performance

standards that reflect the long-term emission reductions required or a broad-based ETS with

long term targets, “investment paralysis‟ is entirely predictable. This effectively leaves investors

with one option for investment to ensure security of supply, OCGT plant, because it minimises

“capital at risk”.15

OCGT incurs typically 30 to 40 per cent less capital for the same capacity

therefore from an investor facing significant uncertainty on revenue, an OCGT option lowers the

risk of asset stranding. While an OCGT investment lowers capital at risk, the operating costs

and emissions of OCGT generation are significantly higher than that of a CCGT. Therefore, the

overall cost of supply increases. As Nelson et al (2010a) states, “The short to intermediate-run

consequences of this situation are dire for the power industry. Until certainty is provided,

investors will seek to minimise capital costs (and therefore the risk of asset stranding) by

investing in OCGT to maintain security of supply”.

Nelson et al (2010a) and Frontier Economics (2010) have undertaken empirical analyses of the

cost of policy uncertainties in Australia and in particular addressed the issue of CCGT versus

OCGT investment:16

Nelson et al (2010a) constructed LRMC estimates for 2010, 2017 and 2020 using a

relatively simple screening curve model. They constructed alternative scenarios of

carbon policy uncertainty to determine the change in capacity/generation mix that such

uncertainty would cause, which in turn would be reflected in a higher LRMC of

generation. In particular, their analysis focused on the difference in capacity and LRMC

of two major scenarios – Delayed Certainty and Immediate Certainty – where the latter

is used as a counterfactual for optimal capacity mix under perfect certainty. They draw

two significant conclusions, namely:

o The impact of policy uncertainty is significant with a major swing of 3,800 MW

of OCGT being built instead of baseload CCGT by 2017. Even with three years

to partially correct for this inefficiency, the 2020 mix is “still 2,500 MW

overweight OCGT and underweight CCGT”. This translates into an LRMC

increase of

$8.60 per MWh absent any consideration of RET and energy

efficiency. In absolute terms, the additional cost of policy uncertainty

could be up to $2.1 billion annually by 2020 absent energy efficiency

measures;

$3.97 per MWh incorporating energy efficiency measures, but absent

any consideration of RET. The degree inefficiency can be greatly

reduced through energy efficiency measures. For instance, Nelson et

al have concluded the sub-optimal capacity can be reduced from 2,500

15

AGL (2010), Delayed Carbon Policy Certainty and Electricity Prices in Australia, AGL Report, November

2010. Report authored by T. Nelson, S. Kelly, F. Orton and P. Simshauser. 16

Frontier Economics, What‟s the Cost of Carbon Uncertainty, Frontier Economics Report, November 2010.


MW to less than 500 MW by 2020 if energy efficiency measures are

introduced; and

$1.15 per MWh incorporating the RET impact but ignoring any energy

efficiency considerations. 17

o Studies by Nelson et al (2010a and 2010b) do not consider any carbon price.

Any consideration of carbon price would add to the cost of policy uncertainty

because OCGTs typically have 0.2-0.3 tonne per MWh higher emission

intensity compared to a CCGT.

Frontier Econmics (2010) presents a more detailed analysis using a multi-year

intertemporal capacity optimisation model WHIRLYGIG – a proprietary modelling tool

of Frontier Economics. Their analysis largely follows the same construct of scenarios

and assumptions as Nelson et al (2010a), to compare and contrast the inefficiency

measures. Frontier Economics has noted that “The cost of uncertainty is an empirical

question and depends on the need for new baseload capacity. The sooner the

baseload capacity is required (and the larger the requirement), the greater the cost of

policy.” Their analysis essentially concludes that the requirement of new baseload plant

has been significantly overestimated by Nelson et al (2010a) that has resulted in a

significant overestimate of cost of policy uncertainty at $8.60 per MWh. The

WHIRLYGIG analysis shows new CCGT investment is needed under perfect certainty

from 2015 only, and only in some regions. The cost of delay is estimated at $3.40 per

MWh for New South Wales. Frontier‟s analysis considered a carbon price being

introduced from 2014 and also considered the RET. Frontier‟s estimate is in the $1.15-

$8.60 per MWh range estimated by Nelson et al (2010b). Frontier‟s estimate is closer

to the low end of AGL estimate reflecting the impact of RET, albeit it also incorporates

impact of carbon costs that has not been considered in Nelson et al (2010a and

2010b). Frontier also concluded that energy efficiency measures would greatly diminish

the cost impact.

The issue of investment choice under policy uncertainty has also received attention

internationally. The International Energy Agency (IEA) commissioned a study in 2006 that

provides a number of useful insights:18

The introduction of a carbon price acts as a “shock” to return on generation investment.

Climate change policy uncertainty in general creates a financial incentive to “wait”, i.e.,

delay investment in order to gain more information which would allow a more optimal

investment choice that minimises downside risk.

Any near term or immediate investment decision must overcome this value of waiting –

the gross margin would need to be in excess of not merely the capital cost but also an

additional threshold level above it:

o The bigger the shock the greater the value of waiting.

o The less time there is available before the shock, the higher the gross margin

would have to be to overcome the value of waiting.

These two observations have significant implications, namely:

o The choice of coal versus gas, or CCGT versus OCGT, in the short term would

be driven more by fuel prices rather than by expectations of a carbon price;

17

Nelson et al (2010b), Delayed Carbon Policy Certainty and Electricity Prices in Australia, Economic Papers,

Vol 29, No 4, December 2010, pp.446-465. 18

W. Blyth and M. Yang, Impact of Climate Change Policy Uncertainty in Power Investment, IEA Report # LTO/2006/02.


o The ability to retrofit carbon capture and storage (CCS) at a later date acts as a

good hedge for coal plant against higher than expected carbon prices.

The growing volume of academic literature also generally supports these general conclusions.

Tuthill (2008) has, for instance, constructed alternative stochastic models of carbon price

expectation to assess the electricity investment choice decisions to conclude that: “...uncertain

environmental policy leads to a reduction in investment in clean generation capacity and a delay

in capacity investment in general, particularly when it is possible for emissions regulation to

become less stringent in the future”.19

Although these studies are conducted in a different context, their general findings are in

agreement with those of the Australian studies. For instance, it reconfirms that the sub-

optimality of investment, namely OCGT instead of CCGT, would occur under policy uncertainty

because there is less investment/value at risk. The international studies suggest that the degree

of sub-optimality can be material with clean technology capacity reducing to half, under an

uncertain policy environment. The sub-optimality is also shown to be most pronounced under a

scenario where the government retains the right to loosen emissions caps in the future. A great

emphasis has therefore been placed on sufficiently stringent policy to which the government will

remain credibly committed. It should be noted that although these conclusions have been drawn

in the specific context of carbon policy, these have been known for a while in a more general

context. For instance, the study by Ishii and Yan (2001) ran largely the same analysis around

capacity investment for 24 Independent Power Producers across 13 US regional wholesale

markets under market restructuring uncertainties to also conclude that investors are likely to

commit to significant baseload investment only if restructuring is a near certainty over the

following two years at most.20

As already noted, the big question globally is around gas prices – as Richard O‟Neil, Chief

Economic Adviser to the Federal Energy Regulatory Commission (FERC), puts it - will the gas

prices stay low? This will ultimately determine the choice not only between CCGT versus

OCGT, but also more generally between coal (or, clean coal) versus gas.21

Dr O‟Neill has also

succinctly described the vast impact of climate change policy uncertainty on new investment:

In 2007, there were 231 new coal projects in the pipeline. By 2010, nearly half of them

have been cancelled. This signifies the extent of uncertainty associated with coal

based investment;

The majority of the CCS pilot projects around the world have also been shelved or

progressing very slowly at best. The big question in Dr O‟Neil‟s words is: Does $30-

50/tonne make it uneconomic?

The prospect of more nuclear capacity is also questionable given its very high upfront

capital cost and low flexibility.

Joskow (2009) also makes another pertinent observation regarding the practicality of achieving

the “optimal” outcome that has often been advocated in various studies (including the Australian

studies). As he observed, “...Can we avoid the cost overruns and inefficiencies that were

19

L. Tuthill, Investment in Electricity Generation Under Emissions Price Uncertainty: The Plant-type Decision”.

Oxford Institute for Energy Studies Report EV 39, June 2008. 20

J. Ishii and J. Yan, An Empirical Model of Electricity Generation Investment under Regulatory Restructuring, Stanford University Report, January 2001. 21

Richard O‟Neil, Carbon Policy – Where is the Light Good?” in Harvard Electricity Policy Group, December 2010.


experienced under regulation during the last wave of investment as regulated utilities begin to

build power plants again?... traditional vertically integrated utilities no longer have any

experience managing large construction projects. Most traditional vertically integrated utilities

have not built major generation projects for 15 years or more. ... This increases the likelihood

that absent appropriate incentives to control costs, regulated generation projects will be

excessively costly and that the cost overruns will be largely borne by consumers.”22

Beyond the economic merits of coal versus CCGT/OCGT for energy generation, the policy

uncertainty also has an impact on system reliability which is a function of age of current fleet of

generators, generation/demand response technology employed and their characteristics (e.g.,

conventional versus intermittent generation, non-firm demand side response). These issues are

getting considerable attention internationally including much larger interconnected systems in

North America. Many of the concerns and issues that arise in other systems share common

elements with the Australian market:

The North American Reliability Council (NERC) has noted that climate change would

lead to an “unprecedented shift in North America‟s resource mix”. The

recommendations of the latest NERC study include:

a) Regional solutions are needed to respond to climate change initiatives driven by

unique system characteristics and existing infrastructure. NERC have quite aptly

pointed out that some of the local reliability impacts may be severe, even for

relatively modest emission reduction targets and therefore one reliability policy for

all may not be a pragmatic approach; and

b) The addition of new resources may disproportionately increase the need for

transmission and energy storage and balancing resources. In NERC‟s opinion,

successful addition of renewable and other cleaner forms of generation would

require massive changes to the existing transmission system.

Policy uncertainty may delay new investment and also hasten the retirement of existing

units (e.g., accelerated harvesting of ageing coal units as has been the practice in some

of the North American utilities23

– there may be significant detrimental impact on

reliability for at least some parts of the system.

22

P. Joskow, US Electricity Sector and the Climate Change Policy Challenge, posted in Harvard Electricity Planning

Group, December 2009. 23

See for instance, V.M. Bier and J.D Glyer, Preventive Maintenance Strategies for Deregulation, Montgomery

Research Inc, Utilities Project, 2002.


3 Methodology

3.1 Interviews with Market Players

Interviews with market participants are intended:

o To elicit rationale on historic decisions including withdrawing from investment

decision, as well as new build decisions, including the impact of policy directions (or

lack of it) as well as other factors that may have played a role in the decision;

o To collect additional data and related studies that may have been undertaken

including internal studies that may become available for the review; and

o To seek advice on reasonable scenarios that should be constructed for the

modelling analysis.

We developed an initial list of potential candidates for interviews based on our knowledge of the

state of play in generation investment and our involvement in various projects for the

government bodies. We had selected 14 candidates for our interview in conjunction with DRET.

Appendix A provides a list of the candidates interviewed and the key questions that formed the

basis of our discussion with these participants.

3.2 Modelling Analysis

The purpose of the modelling analysis is to develop an objective assessment of CPU. While

historic data and views expressed by market participants and other policy making bodies

provide a useful context and inputs, modelling is necessary to form a “system-wide” estimate of

CPU taking into account all policy considerations. Specifically, we have explored the following

issues around an electricity market model that covers all Australian regions:

What would be the cost of policy uncertainty if uncertainties around carbon

policy faced by market participants and financial institutions stall baseload

generation investment:

a. In the short- to medium-term – for instance, if, as the literature suggests,

baseload investment in CCGT over the next few years is displaced by

OCGT and baseload CCGT investment resumes over 2017-2020; and

b. In the longer term – for instance, if uncertainty around carbon policy

persists for new baseload CCGT plants to be deferred till 2025.

We have provided an illustrative example in the next sub-section to develop an understanding

of the assumptions that the calculation of CPU rests on and also the factors that influence CPU.


3.2.1 Illustrative Example: Cost of Policy Uncertainty

Our literature review and historical analysis suggest that the key issue underlying CPU is

around the sub-optimality of capacity mix. The balance of peaking and mid-merit/baseload

investment mix to meet new load growth needs to be maintained in a well-designed system for

the cost of supply to be minimised. As Figure 7 shows, growth in demand and/or retirement of

existing capacity would create a requirement for new capacity for peak load (that typically runs

below 15 per cent capacity factor or utilisation), mid-merit load (15-70 per cent capacity factor)

and baseload (greater than 70 per cent capacity factor). CCGTs typically meet the baseload and

mid-merit generation requirements, whereas OCGTs normally run during peak periods only. If

there is perfect certainty on carbon policy, one can optimise the capacity mix considering all

parameters including carbon price as “known” and an underlying cost of capital that reflects

such certainty. However, uncertainty on future utilisation would effectively increase the risk

premium and would discourage any capital-intensive CCGT or other baseload projects. We

have reflected such a change in capacity mix by restricting baseload investment over the

short/medium term (2013-2019) i.e., if the present uncertainty on carbon policy stalls baseload

CCGT investment such that baseload investment resumes from 2020.

Let us consider the potential cost of such change in capacity and generation mix. Table 2 below

shows the annualised fixed cost (including capex and fixed O&M) along with other parameters

for the NEM from a recent study for the Tasmanian regulator.

Table 2: Illustrative Example: CCGT and OCGT Costs

Technology Annualised

Fixed Cost ($/kW/Yr)

Heat Rate (GJ/MWh)

Variable O&M

($/MWh)

Fuel Cost ($/GJ)

Emission intensity

(tCO2 per MWh)

Availability (%)

CCGT 158 7.50 1.05 5.30 0.42 95%

OCGT 106 12.41 7.93 6.63 0.69 95%

Source: IES, Review of Wholesale Prices for 2010-2013, May 2010.

Figure 7: Load Growth: Illustrative Example

Load growth

8760 hours

MW

Peak

Mid-merit

Base


Over the next 10 years, the total requirement for new capacity in the NEM would be in the range

of 12,000-15,000 MW depending on assumptions of load growth, energy efficiency etc. If new

baseload/mid-merit requirement is met by CCGT and peak load is met through OCGT, the

optimal mix is approximately an equal proportion of these two technologies, ignoring any

renewable entry for the time being. Hence, there will be approximately 6,000-7,500 MW of

peaking capacity needed by 2020 for a well-designed system that one may expect under a

“Policy Certainty” scenario. This requirement may, however, be significantly more at the

expense of less mid-merit/baseload CCGT capacity if the risk premium due to policy uncertainty

is high to warrant such a change in capacity mix.

The maximum level of sub-optimality, i.e., OCGT capacity displacing CCGT in the optimal

portfolio, has been estimated at around 3,800 MW by Nelson et al (2010), although a

subsequent study by Frontier Economics has stated this may be an overestimate.24

In other

words, the addition of OCGT may need to be substantially more than 50 per cent. Also, OCGTs

that would displace CCGT is expected to run in a mid-merit to baseload role. If we assume that

on average these “replacement” OCGTs run at 60 per cent capacity factor, using the

parameters in Table 2, we can calculate CPU as follows,

Every MW of OCGT would save capex of $52,000/MW/year relative to a MW of

CCGT;

On the other hand, every MWh of OCGT generation would cost approximately

$40/MWh more due to the inefficiency of an OCGT relative to CCGT and a higher cost

of gas (assuming the change in operating pattern of an OCGT would not lower its gas

price);

Therefore, if 3,800 MW of OCGT generators run on average at 60 per cent capacity

factor (ignoring any carbon reduction in 2020):

o There will be a reduction in capex of 3,800 X 52,000, or $198 million per year;

o There will be an increase in fuel and variable O&M costs of 3,800 X 8760 X

60% capacity factor X $40/MWh, or $799 million;

o This net increase of ($799-$198), or $601 million per year results in an

additional $2.61 per MWh (spread uniformly across 230 TWh of annual

generation in 2020).

While this provides a point estimate of the cost of policy uncertainty, in reality, it would of course

depend upon a range of factors, including:

Duration of uncertainty – if we were to assume the period of uncertainty has

commenced earlier, or would continue beyond 2020, CPU would be higher because it

implies there will be a higher level of inefficient investment in OCGTs. The market

modelling analysis presented in the next section incorporates impact of policy

uncertainty since 2000 and also explores continued impact of uncertainty beyond 2020.

Therefore, our estimate of CPU, all other things being equal, would be higher than the

$2.61 per MWh estimate derived above;

The degree of sub-optimality, i.e., the extent to which the 3,800 MW estimate may

change upward or downward. If all of the baseload CCGT (up to 7,500 MW) is

displaced by OCGT, the CPU would be $1.2 billion pa or $5.16 per MWh. This may

represent an upper bound to CPU in the near term barring the case where some of the

existing coal generation may retire opening up further baseload opportunity. The degree

24

Nelson et al (2010a) and Frontier Economics (2010), ibid.


of sub-optimality would also depend on the period of uncertainty. The latter scenarios

would require a higher level of baseload generation than merely meeting the new

baseload growth and hence CPU may exceed $1.2 billion pa;

Assumption made on carbon price/target – in particular, if the long term emissions

reduction target (e.g., proposed CPRS-5) is to be met, the cost of uncertainty would be

higher because OCGTs also have higher emission intensity compared to CCGTs. If we

were to assume an implicit, or explicit, carbon price being introduced in 2020 of $60 per

tonne of CO2, the effective cost of OCGT inclusive of carbon costs would be $56.20 per

MWh higher than that of a MWh from CCGT (instead of $40/MWh ignoring carbon

costs). As a result,

o 3,800 MW of additional OCGT case: CPU would increase from $2.61 per MWh

to $4.01 per MWh;

o 7,500 MW of additional OCGT case: CPU would increase from $5.16 per MWh

to $7.93 per MWh; and

o Therefore, the increased CPU incorporating a carbon price would be $1.40 to

$2.77 per MWh, higher compared to a case that ignores introduction of carbon

price.

Capital costs of technologies – any relative increase in CCGT cost would reduce CPU;

The change in cost of capital that will change the annualised cost estimates and would

also have an impact on the capacity mix;

Fuel prices relevant to OCGT at higher load. Since the heat rate of OCGT is

approximately 5 GJ per MWh higher compared to a CCGT, every dollar per GJ increase

in gas price would render the a MWh from OCGT $5 per MWh more expensive.

Therefore, the difference between OCGT and CCGT cost would increase from $40 per

MWh to $45 per MWh, if gas prices were $1 per GJ higher. An increase of gas price by

$4 per GJ that would reflect long term gas prices reaching parity with international price,

would increase CPU from $5.16 per MWh to $8.58 per MWh;

Policies such as Renewable Energy Target that essentially mandate entry of generation

may have a profound influence on the CPU. In the present example, we have assumed

up to 7,500 MW of OCGT capacity may need to run at 60 per cent capacity factor (if

baseload investment is stalled due to policy uncertainty), i.e., meet 39,420 Giga Watt-

hours (GWh) per year of mid-merit/baseload generation requirement. A 20 per cent

RET would require up to 31,000 GWh of new renewable generation by 2020 and hence

potentially take up a significant share of the additional baseload generation

requirement. If, for instance, we assume 25,000 GWh of renewable generation for

OCGT generation that would otherwise be needed, the cost of carbon policy uncertainty

would be greatly diminished. More precisely, the OCGT capacity requirement to meet

(39,420 – 25,000) or 14,420 GWh at 60 per cent capacity factor would be only 2,743

MW instead of 7,500 MW.25

This will drastically reduce the CPU from $5.16 per MWh to

$1.88 per MWh; and

25

Intermittent form of renewable generation would typically require significant level of back-up peaking capacity.

However, this addition cost of back-up capacity would largely be the same across the two scenarios we are comparing

with and without carbon policy certainty. Therefore, considering back-up generation would not have a material impact on

cost of policy uncertainty that is calculated as the difference in system cost between these two scenarios. The objective

of the present analysis is not to calculate the cost of the RET policy, or any potential sub-optimality in the generation mix

that may be caused by RET. In other words, we assume the cost of RET as exogenous to the calculation of cost of

carbon policy uncertainty.


Combination of RET and carbon price/target – the former would depress CPU, while the

latter would increase it. For instance, a $60 per tonne CO2 price may drive CPU up from

$1.88 per MWh (ignoring carbon price) to $4.65 per MWh (including carbon price).

The collective impact of these parameters is that a broad range is possible for the cost of

uncertainty. As the discussion above suggests, some of the drivers in the short to medium term

including the extent to which baseload CCGT investment is affected and escalation of gas

prices may render the CPU to rise over $8 per MWh or around $2 billion pa. On the other hand,

a significant uptake of renewable generation driven by the RET policy would drastically reduce

the baseload generation requirement and hence may drive CPU below $2 per MWh absent any

carbon price. An implicit or explicit carbon price is likely to increase the CPU by $1-3 per MWh.

The resultant CPU inclusive of RET and carbon constraint for an average degree of sub-

optimality in this illustrative example is therefore expected to be in a range of $3-5 per MWh.

The discussion that follows elaborates how we have implemented these calculations including

the choice of modelling tool, the policy certainty and uncertainty scenarios constructed to

calculate the CPU and modelling steps.

3.2.2 Deloitte’s Long Term Model for Electricity Market Simulation

We have used our proprietary Long Term Model (LTM) to calculate the CPU. LTM models all

existing major power stations in Australia, demand by region and interconnection among the

regions. LTM performs an inter-temporal optimisation of the Australian electricity sector

(including NEM, WA and NT systems) incorporating a generation investment and dispatch

optimisation. The model, therefore, enables us to calculate CPU taking into account the existing

stock of power stations, load profile by region and load growth over the years. Similar

methodology has been adopted for analysing investment and dispatch behaviour in the

Australian context. A detailed discussion on the underlying methodology and previous

applications is available in Chattopadhyay (2010).26

We have calibrated the model using historic data over the past 10 years. The model has a

flexible constraint structure to capture a range of limits around generation investment choices

available over time, minimum level of gas generation, maximum CO2 emissions, minimum

renewable generation stipulated by the RET, etc. We have used LTM to calculate the CPU for

alternative scenarios including restriction on baseload investment over different timeframe and

different levels of renewable entry.

LTM also models key facets of the NEM including dispatch rules and bidding by the generators

and hence price formation in the electricity market. LTM includes a bidding module, based on

Cournot game, to reflect the bidding strategy adopted by generation companies in the market.27

26

D. Chattopadhyay, Modeling Greenhouse Gas Reduction From the Australian Electricity Sector, IEEE Transactions

on Power Systems, May 2010. (http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5406007)

27 Cournot models have been extensively used for electricity market competition analyses . For instance, J.Bushnell, et

al, “An International Comparison of Models for measuring market power in electricity” Energy Modeling Forum, Working

Paper 17.1, March 24, 1999, Stanford University.


1.1.1 Scenario Development: Policy Certainty versus Uncertainty

One of the major outcomes that we have tried to capture through our modelling is the sub-

optimality of generation investment. The degree of sub-optimality is measured as the difference

in total system costs over 1999/2000 to 2029/2030 across:

„Policy Certainty‟ (or “Certain”) scenario: All investment decisions are optimised

assuming perfect certainty on carbon policy. These include:

o Past investment decisions: In order to assess any sub-optimality in past

investment decisions, we have included a “re-optimisation” of these decisions

as part of the Policy Certainty scenario. Investments made over the last 10

years have been “removed and re-optimised” to investigate how policy certainty

would have changed these investment decisions had there been perfect

certainty on carbon policy design and target. The re-optimisation is carried out

also assuming perfect certainty on the RET being fully met, demand growth,

supply costs and gas prices; and

o Future investment decisions: We have also optimised the future supply

scenario assuming a 5 per cent reduction (from 2000 level) by 2020 target

would be in place. The longer term 2030 annual emissions target for the

electricity generation sector is set at 181 million tonne (adjusted for off-grid

generation) following the Treasury modelling.28

„Policy Uncertainty‟ (or, “Uncertain”) scenario: This scenario reflects the current reality

of uncertainty around carbon policy stalling baseload generation investment.

o Past investment decisions: reflects actual investment profile and includes all

existing generation investments including the recent developments over the

past 10 years; and

o Future investment decisions: Going forward, we have assumed there will be

continued uncertainty for several more years (e.g., until 2017) that would

prevent any form of baseload generation investment, to develop an estimate of

the additional cost that such uncertainty may introduce. We have created two

broad uncertainty scenarios, namely:

Short- to medium-term uncertainty: We assume the proposed CPRS

may be implemented at some point over the next few years for

baseload investment to resume from 2017, or from 2020; and

Longer term uncertainty: In order to illustrate how the CPU may

escalate over the years, at the request of Investor Reference Group

(IRG), we have also constructed a scenario of continued uncertainty

that would render baseload investment to be stalled till 2024.

The cost of policy uncertainty goes beyond a fuel choice, i.e., less coal and more gas. It would

also affect the type of gas plant being built and how they are operated. As no formal carbon

policy is in place, even investing in CCGT plant is becoming difficult due to the risk of coal plant

being built in the future and subsequently superseding the CCGT dispatch. As the simple

example in the preceding sub-section demonstrates, any restriction on baseload CCGT would

entail higher costs. The cost of policy uncertainty is likely to grow over time with load growth

because there is a higher baseload generation requirement and hence the lack of investment in

28

MMA, Impacts of the Carbon Pollution Reduction Scheme on Australia‟s Electricity Markets, Report to Federal

Treasury, 11 December, 2008., p.3.


baseload power station has higher opportunity costs. Cost of policy uncertainty would also hinge

on renewable entry, gas prices and carbon reduction. Figure 8 illustrates how we intend to use

the scenarios and sensitivities around them to assess the trend of cost of policy uncertainty.

Figure 8: Characterising Cost of Policy Uncertainty

Table 3 below lists the core assumptions relevant for historic and forward analysis. Table 3: Assumptions for Historic and Forward Analysis

Timeframe 1999/00-2009/10 2010/11-2029/30

Parameter Historic – Policy Uncertainty

Historic – Policy Certainty

Forecast – Policy Uncertainty

Forecast – Policy Certainty

Technology Availability

Existing generators including new build over 1999-2010 modelled.

Existing build over 1999-2010 removed from modelling, build re-optimised

No new baseload build until 2017

29, or

2020, or 2025

No restriction on new build

Carbon Policy Carbon target applied based on actual emissions

No carbon policy until 2010

Long term emissions reduction target is met

Long term emissions reduction target is met

Renewable Energy Target

Actual renewable entry

RET is being met till 2010

LRET target of 41 TWh is met by 2020.

LRET target of 41 TWh is met by 2020.

Table 4 summarises three scenarios we have analysed to understand the cost of policy

uncertainty trend.

29

We note that 2017 was also identified in the AGL Study as the earliest year baseload build would be available

if certainty surrounding carbon policy occurred in 2013.

Cost of Policy

Uncertainty

Time

Critical assumptions:

RETGas priceCarbon reduction

Early resolution of

uncertainty

Continued

Uncertainty


Table 4: Summary of Scenarios

Scenario Description Baseload investment

resumes from

Renewable Energy Target

1 Early resolution of uncertainty over the next two years

2017 100% met by 2020

2 Delayed resolution of uncertainty 2020 100% met by 2020

3* Continued uncertainty 2025 100% met by 2020

Note: All scenarios assume proposed CPRS-5 emissions reduction is achieved by 2030 as per Treasury modelling and gas prices based on average of Scenario 1 and Scenario 2 of ACIL Tasman 2010. * Scenario 3 reflects advice received from the IRG to study the implications of longer term uncertainty.

The key output from a comparison of these two scenarios is the cost difference between the two

scenarios.

We have used the following definition of system cost (or, alternatively termed as resource costs) and cost of policy uncertainty:

Total system cost (TSC) = Annualised capital cost for new investment + Fuel costs + Variable and fixed operations and maintenance costs + Cost of unserved energy Cost of Policy Uncertainty (in dollars) = TSCPolicy Uncertainty – TSCPolicy Certainty

TSC can be calculated for each year (including annualised capital cost for new investment for

the specific year), or for a number of years. In addition, we have also calculated an average cost

of policy uncertainty by dividing the TSC with the total energy requirement across Australia. We

note that resource cost increase and long run marginal cost increases in an optimised system

are consistent and to that extent our estimate of CPU also reflects marginal cost impacts.

Appendix B provides a simple example to illustrate the interrelationship between TSC and

LRMC. However, in an imperfectly competitive market such as the NEM, there would be

additional price impact due to changes in bidding behaviour and also further impact of

wholesale prices on customer consumption. Given the complexity of the pricing impact issues,

we have restricted the scope of our analysis to purely cost impacts.


4 Summary of Discussions with Market Participants

This section summarises the key points and views that emerged from our discussions with

market participants.

4.1 Investment Decisions

Has your organisation undertaken planning and investigations into investing, or

committed to investing in generation in the past five years?

Does the organisation’s valuation approach explicitly take into account the

impact of policy uncertainty in investment decisions (e.g., put a value on

flexibility of OCGT that can be converted to CCGT at a later date)? How does the

organisation price risks?

None of the coal gencos in the NEM that we have interviewed is considering any new

investment in coal-fired power stations. The situation seems to be worse for some

generators than others.

o For instance, one of the gencos has abandoned all plans to invest in Australia and

has advanced generation development proposals in developing countries. The

current state of uncertainty has severely constrained availability of growth capital

from what used to be substantial amounts (e.g., in several hundred million dollars

for each of the major gencos).

o The situation is different in WA where new coal investment is being planned.

However, coal based development is being driven by the high gas prices in WA and

certainty on long term coal contracts that is not linked to the global coal market.

Generators in WA also noted that their generation investment plan has not changed

significantly over the past 6 to 7 years and in this regard their experience differs

remarkably from their counterparts in the NEM.

o One of the WA generators is in the process of refurbishing two of their (total 240

MW) 40 year old coal fired generating units with an expected spend of $100 Million.

The motivation of the WA generators stems partly from the fact that the gas prices

are already linked to net back LNG prices. It also stems partly from their confidence

in the new Wholesale Electricity Market design that in part provides a greater

degree of certainty of capacity payment to baseload generators and provides

payment to intermittent generators in proportion to their average capacity factor.


One of the coal generators in the NEM noted that their highest priority is to maintain their

asset value which is steadily diminishing as a result of policy uncertainties that include both

carbon policy as well as others including the RET, solar flagships, Queensland Gas

Scheme, etc. Some gencos have noted that they do explicitly consider the impact through

an adjustment in discount rate. The term “sovereign risk” has featured in several of our

discussions with gencos. The general view from the coal gencos is that the policy

uncertainty is a root cause for damaging their asset value that amounts to a significant level

of sovereign risk.

While uncertainty on carbon policy was noted as one of the key factors, all market

participants stated that it is really a combination of factors rather than carbon policy alone

that is causing a lack of investment in coal. That said, there is a strong feeling that raising

debt for any new coal project in the current environment is a near impossibility. While some

of the other gencos noted access to finance is less critical an issue given that they have

overseas partners, it was nonetheless evident to them that the Australian energy sector is

increasingly being viewed as a risky destination given the current uncertainty on carbon

policy. Other drivers of investment decisions that were raised include:

o Low pool prices – in particular Victorian brown coal generators have expressed

significant concern over lack of profitability of baseload generators;

o The impact of enhanced RET that is taking up a significant share of the market, its

impact on pool prices and funds available in the power sector; and

o Other significant events around privatisation in NSW and consolidation in QLD, the

impact of which on the NEM is largely unknown at this stage.

However, none of the coal gencos we have interviewed had any plan to retire any assets in

view of climate change uncertainty. The short-term uncertainty was having an impact on

some of the maintenance and upgrades that would have delivered both higher capacity and

lower emission rates. This included some of the work on improving turbine blade efficiency

for one of the generators that had been put on hold as a result of the delay in carbon policy.

Nevertheless, we understand these gencos expect their assets to continue in baseload or

“some sort of mid-merit role” for their remaining life as has been planned before the ETS

discussion had started.

On the other hand, three major gentailers with significant gas generation portfolios

(including the newly acquired NSW assets by TRUenergy), have major gas-fired generation

projects in the pipeline. We have also spoken to others including NGF and a major

renewable generator who had indicated their interests in gas generation investments.

However, owners of gas generation who do not have a substantial retail position or

upstream gas position, i.e., in their view is not a “portfolio gas player”, seemed to have a

very different view on new gas investment. One of the gencos with no ownership of gas had

noted that they were considering new gas investment in Asia but were not interested in

similar investment in Australia in view of the significant risk arising from policy uncertainty.

They had noted that ownership of fuel is critical because all the benefits from a lower

emission generation would ultimately flow through to the gas developer via a higher gas

price.


The climate policy uncertainty was having an impact on the type of gas plants that may be

built. As we have also discussed in our literature review, a general view of the market

participants is that investment in baseload generation is deemed risky given the

uncertainties surrounding the form of carbon pricing. It basically leaves OCGT as the sole

choice for investment for many of the market investors. In our discussion with AGL, they

noted that they have estimated the cost of policy uncertainty around $2 billion per year in

the context of the “investment paralysis” that the policy uncertainty has caused.

There were however also countervailing views from some market participants that suggest

the extent of sub-optimality at $2 billion per year is an “overestimate”. This opinion in part

stemmed from the view that gas generation had largely been “forced into the mix” through

policy constraints such as the Queensland Gas Scheme and through the recent changes in

the NEM through privatisation of NSW assets that has seen formation of three “mega”

vertically integrated players. As a result, at least part of the baseload gas generation was

deemed sub-optimal in the first place. Final customer prices may be more affected by the

impact of such policy constraints around gas, renewable energy and network and retail

costs, than carbon policy uncertainty per se. In light of these observations, some market

participants are of the view that while the impact of carbon policy uncertainty is “not

insignificant”, it must be seen in the broader context of all these other factors.

4.2 Carbon Prices and Targets

What reduction targets and carbon prices did the organisation take into account?

What is the organisation’s current standing on a carbon price mechanism and

how is it being incorporated it in its current set of investment decisions?

Given the uncertainty, generators were using a mix of business-as-usual and

various “CPRS” scenarios as part of their internal planning and investment

decisions over the past 5-6 years;

Generators had conducted a significant amount of work (both internally as well

as through the NGF) over the past five years. There have been a number of

updates to the (forecast) carbon prices over the years as new information on

technology costs and availability of technology became available. The initial

work (e.g., done by Frontier Economics for AGL, CRA for NGF in 2006/07)

predicted relatively “low” (long term) CO2 prices of around $40 per tonne.30

Somewhat higher long term prices around $60 per tonne were also estimated

by Treasury as part of the CPRS-5 scenario including a significant role played

by Carbon Capture and Storage (CCS) at that price. Generators had

subsequently revised their view on carbon prices especially because CCS costs

were revised upwards at around “$80-100 per tonne” of CO2 abated. The coal

generators also noted that they had undertaken various internal technical

studies to form a view on short to medium term options and concluded the

30

References:

1. AGL, Frontier Economics and WWF-Australia, Options for moving to a lower emission future, May 2006.

2. Charles River Associates (CRA), Greenhouse gas reduction from the Australian electricity sector, prepared for the National Generators Forum, 2007. (http://www.ngf.com.au)

http://www.ngf.com.au/


majority of the options that justified operational efficiency improvements (e.g.,

cooling tower packs and condenser improvements) are likely to cost well above

$60 per tonne;

Opinions expressed in various public forums and studies conducted by the NGF

in the past have estimated CO2 price in the range of $20-$40 per tonne would

be needed to drive substantial substitution of coal with gas for baseload

power.31

We should note that these views on CO2 prices are broadly consistent

with historic SRMC and emission intensity of existing power stations. As we

have discussed in section 2.2, our historic analysis of SRMC and emission

intensity data suggests CO2 prices below $30 per tonne would have been

adequate to justify a switch from some of the existing brown and black coal

power stations to existing baseload CCGT. CO2 prices well above $30 per

tonne would however be needed to replace existing coal-fired generation with

new baseload CCGT development;

In response to our question on specific target and carbon prices that were being

used in investment analysis by generators, the predominant view was to adopt

the CPRS-5 reduction scenario and Treasury carbon prices with an assumption

that the introductory year will have a fixed CO2 price in the range $10-20 per

tonne. CPRS-5 rather than CPRS-15 was considered “realistic” given that even

the former required approximately 27 per cent reduction in the electricity sector

emissions relative to the Business-as-usual (BAU) scenario and therefore is a

fairly challenging and expensive task in itself. 32

In addition to the Treasury

modelling, NGF has also undertaken a series of modelling analysis and has

estimated that the CO2 prices in 2030 could be significantly higher, at around

$80 per tonne.33

Carbon price projections are applicable for the NEM as well as other markets

except the underlying gas prices can vary significantly. Verve Energy has noted

that “Plant dispatch scenario analysis looks at a range of price scenarios,

however prices based on CPRS-5 can be considered the base case (starting

from around $26 and finishing around $53/t CO2 in 2030 in real dollar terms)”.

As a result 2020 wholesale electricity price projections can be in the $80-100

per MWh across NEM regions. It is unclear though the relative emphasis placed

on these alternative projections for specific investment decisions;

31

For instance, 1. The Courier Mail article titled “Origin Energy boss expects higher costs after ETS abandoned” on May 18,

2010, noted that “With a carbon price above $40 per tonne, gas became the lowest-cost fuel for new baseload electricity generation”.

2. A n article in The Age dated March 16, 2011quoted AGL stating, “...that wholesale power prices have to rise by about $20 a megawatt hour to drive investment in gas”.

3. The NGF study “Analysis of Greenhouse Gas Policies for the Australian Electricity Sector” published in 2007 showed significant gas generation would occur in the long term above $30/t absent nuclear technology.

32

MMA, Impacts of the Carbon Pollution Reduction Scheme on Australia‟s Electricity Markets, Report to Federal

Treasury, December, 2008. 33

National Generators Forum: Submission to Garnaut Climate Change Review ETS Discussion Paper, April 2008.

http://www.garnautreview.org.au/CA25734E0016A131/WebObj/D0848830ETSSubmission-

NationalGeneratorsForum/$File/D08%2048830%20ETS%20Submission%20-

%20National%20Generators%20Forum.pdf


Some gencos had noted that the policy uncertainty makes it very hard to put

any price on their assets even two years into the future. While they do have a

view on likely targets and associated carbon prices, any scenario that includes

a carbon price reflects a very substantial impact on their generation. Such

uncertainties are pervasive and require a very high return associated with their

assets.

Some market participants were of the view that the NEM has substantial

baseload capacity and does not need any more baseload till 2020. There is a

need for peaking capacity in the market and that is precisely why industry is

building OCGTs.

4.3 Proposed CPRS Design

Certainty around the basic form of the carbon reduction policy, i.e., tax or permits, is

paramount. All market participants noted the importance of minimising regulatory risks

because it makes investment in capital-intensive baseload generation extremely difficult.

Several participants also emphasised the need for a CPRS legislation that is “clear and

upfront”. There was a concern that even if the proposed CPRS starts, there may be further

major changes introduced at a later stage which in a way is “worse than the current state of

uncertainty” because the participants may have irreversibly committed to their investment

by then. Again, the regulatory risk around legislation and the prospect of making the system

too complex were stated as major concerns. The generators did not want another “mini tax

department” to manage the proposed CPRS but a system that is simple, transparent and

provides a reasonable degree of certainty on the basic design and its parameters over the

long term.

Apart from clarity and upfront disclosure of the complete legislation, the key attributes of the

scheme design are stated as:

o Market based with minimal non-market intervention and minimal regulation; and

o Minimal disruption to the operation of the NEM.

What would constitute policy certainty to you? In particular, which of the

following design aspects create the biggest form of uncertainty:

a. Scheme design? Including:

o transitional assistance for existing assets

o fixed versus floating carbon prices

b. The target?

c. Link to international schemes?

d. 1/5/10/20/30 year forward carbon prices?


Transitional assistance for existing assets was important for all coal generators including

both NEM and WA coal plants. Verve Energy has noted that “...the level of importance

placed on assistance will vary depending on (a) what carbon prices eventuate ie. will prices

be high enough to result in material reduction in output or stranding of these assets over

their remaining life and (b) to what extent will Verve be able to pass its carbon costs through

to customers contractually (c) level of assistance potentially received by competitors with

similar plant.34

Variable carbon prices with a reasonable degree of certainty and “orderly development” in

the next 3-5 years to reach a firm 2020 target were stated as desirable form of pricing.

However, there were differing views on the target, driven mainly by the concern that there

may be ad-hoc changes to the scheme introduced at a later stage if sufficient degree of

emissions reduction (economy wide) have not been achieved closer to 2020, and the onus

is passed onto the electricity sector at a late stage. For example, one of the generators had

noted that the target is “extremely important as it will provide a foundation or key

assumption input around which investment decisions can be made. The target would allow

for a clearer understanding as to possible carbon price ranges”. Others had noted that more

clarity on the relative contribution of electricity sector, gas prices, transition arrangement are

also important.

Significant discussions have taken place in various industry forums on the importance of

certainty around target for sometime including comments made in various public forums. In

particular, the following key comments are cited from a paper published by AGL and

comments made by Origin Energy in various public forums:

o AGL has studied the difference between “immediate” and “delayed” certainty

scenarios – the latter scenario provides a firm target only by 2013. AGL noted that

“...there is a substantial difference between the Delayed Certainty and Immediate

Certainty scenarios. The difference in timing for the provision of regulatory certainty

significantly skews the distribution of optimal plant to meet demand. By 2017, there

is 3,800 MW less CCGT and more OCGT in the Delayed Certainty scenario relative

to the Immediate Certainty scenario. The other stark conclusion is that even with

three years to correct this imbalance, the 2020 mix is still 2,500 MW overweight

OCGT and underweight CCGT”;35

and

o Origin Energy has commented that “The federal government's commitment to a five

per cent reduction in carbon levels by 2020 remains the most important driver for

energy in Australia”. Also the Chairman of Origin Energy has previously commented

that “we'd argue to get the scheme right for the long run,....A nice, simple, effective

scheme that endures in the long run will do more for certainty than a price today or

tomorrow."36

Origin has also noted that the 5 per cent reduction by 2020 is a

challenging task. Similar to the AGL view, Origin also notes that the lack of certainty

would mean short term inefficiency in gas usage through open cycle. The Chairman

of Origin Energy was cited the The Courier Mail in August 2010 stating that, “...The

policy uncertainty could see investment in baseload (gas plant development) set

back a decade. Australia won't run out of power. The industry will invest. But

34

Written comments sent to Deloitte on 14 February, 2011 and 9 March, 2011. 35

Nelson et al (2010), ibid. 36

Grant King cited in ClimateSpectator, http://www.climatespectator.com.au/news/aust-emissions-target-very-big-origin , December, 2010.


(without a carbon price) the investment will be committed on a "least risk basis”.

37.The Courier Mail article goes on to say, “That meant the industry would build so-

called open-cycle gas generators rather than the more expensive but more efficient

closed-cycle plants that the gas industry hopes will one day replace much of today's

coal-fired generation capacity”.

One of the transitional arrangements that was considered important by the (large) coal

generators perspective is the working capital for permits. These generators typically have

tens of millions of tonnes of CO2 emissions and hence they would require several hundred

million dollars worth of permits to continue operation. Their view was that absent a transition

mechanism to facilitate purchase of sufficient permits, the banks will own them and there is

a significant credit risk issue that may jeopardise an ETS right at the beginning of the

scheme. More generally, all gencos observed that there needs to be considerably more

detail needed around credit risk, the deferred payment scheme, and timing of auctions to

ensure proper management of cash flows.

The issue of compensation for existing assets was primarily been raised by the brown coal

generators. It is a common knowledge that International Power has offered to shut down

Hazelwood units for a payment. More generally, there is a view that the government may

“buy out” a substantial part of the emissions from brown coal to achieve the near term

emissions target and also impart some degree of certainty to the market. Absent such

measures that would effectively force shutting down some of the generators, gencos stated

their intent to keep these power stations operational well into the 2030s.

One of the major banks noted that international permits would be critical to achieve the

emission reduction target in the long term at the lowest possible cost. CO2 emissions from

the Australian power sector have increased over the last few years and the reduction target

even for CPRS-5 is quite significant relative to a Business-as-usual scenario. The longer

term deeper cuts beyond 2020 would imply very significant increase in carbon price if a

significant part of the emissions reductions are to be achieved from the electricity sector.

The carbon prices would put the existing brown coal generators under major financial stress

and therefore an orderly transition mechanism including considerations given to

international permits was considered to be critical to the success of the scheme.

37

Article titled, “Origin Energy boss expects higher costs after ETS abandoned” The Courier Mail, May

18, 2010.


4.4 Impact of Policy Uncertainty

The existing coal generators observed that the major cost of uncertainty has been in the

form of withholding their decisions on spending capital to improve operational efficiency.

The extent to which gencos have already spent money, or foregone savings from capital

spent that could have been realised, could not be quantified easily. For example, there were

major engineering studies undertaken and special taskforce on emissions trading formed

that were in some cases major expenses and in many cases the significant

recommendations of these studies could not be implemented due to uncertainty. One of the

participants observed that they could have made more capacity available and lower

emissions by now if the proposed CPRS were adopted 2 years ago.

A stronger view was expressed by gencos who also own the coal mines. In their view, a

composite mine-power station asset requires a long term plan encompassing several

decades up to 50 years. It was simply not possible for these entities to change their strategy

in response to an array of unpredictable policies, much less squeeze down the operating life

to five years. One of the gencos noted that their 30 year mining right was approved a few

years ago just before the proposed CPRS came into being. Continued operation of the coal

plants was therefore fraught with difficulties and refinancing of existing assets to longer term

had proven extremely difficult in some cases.

Non-coal generators noted that they would have brought forward their capex programme if

the proposed CPRS scheme were locked in by 2008/09. In one case, a market participant

noted that their bid for a major NEM asset a few years ago included a substantial value

component arising from the proposed CPRS and at that point including an upside for non-

coal assets was the norm.

Would the organisation have made a different set of investment decisions if

the CPRS were to be operational by now?

Is there a certain carbon price required to make the switch from coal to gas

economical?

Are there any plans for retirement of plants?

a. If so how is the uncertainty around a carbon price mechanism

impacting on these decisions?

b. Has there been a noticeable change in capex and opex spend for the

existing portfolio of plants (especially coal plants), across pre-2005,

2005-2010 and current plan for post-2010?

What impact did increased RET target have on the organisation’s investment

decisions?


AGL also noted how their forecast from 2006 has changed significantly to 2010 as a result

of changes in the RET target (from 2 per cent to 20 per cent). In their analysis, they had

noted the increased RET would require a significant displacement of CCGT by OCGT.38

The policy uncertainty is also impacting on the contract market making it difficult for

baseload coal generators to sell flat contracts. One of the participants observed “the market

does not know if carbon price should be factored in”. The lack of liquidity in the contract

market was cited as a key concern.

As noted before, the breakeven carbon price for baseload gas to become more economic

than coal is estimated to be in the range $20-$40 per tonne of CO2 in the NEM. Given the

high gas price in Western Australia, the generators in WEM noted the carbon price had to

be significantly higher (around $70 per tonne of CO2) for baseload gas to be competitive.39

As discussed above, the general consensus among the gas generation developers is that

with policy certainty they would have embarked on CCGT development but the propensity

to minimise capital risk in the face of uncertainty means building OCGT was their preferred

choice, albeit at the expense of significant additional costs.

Notwithstanding the high carbon prices, the consensus from coal generators seemed to be

no retirement of existing coal plants in the short- or even medium-term for vast majority of

the plants. This includes majority of the brown coal generators in Victoria believing their

assets should live well into the 2030s. One of the market participants observed that with the

potential exception of Hazelwood, Wallerewang and Northern power stations, “it would take

a long time to drive the remaining generators out of the mix”. A view was also expressed

that an interesting prospect would be for the government to “buy some or all of these three

plants out” and thereby achieve a significant emissions cut in the short to medium term.

We have not been able to secure detailed information on operating costs trend.

Nevertheless, one of the coal generators noted that their growth capital has steadily

decreased from $500 million in 2005 down to practically zero today.

The baseload coal/gas generators were of the view that the increased RET had caused

great uncertainty by taking away 30,000 GWh of baseload generation permanently and

costing consumers up to $100 per MWh more than it would using conventional baseload

generation. One of the participants specifically noted that the some of the observed

propensity to build gas-based peakers rather than baseload CCGT were, at least in part,

been driven by RET.

Verve Energy made the following pertinent observations: 40

o The likely influx of intermittent wind generation only served to enhance the business

case for introducing OCGTs given these plants being ideally suited to dealing with

wind.

38

Presentation by Jeff Dimery at UBS Australian Utilities conference, 29 April, 2010, Available online:

http://www.agl.com.au/Downloads/UBS%20-%20Australian%20Utilities%20Conference%20Apr10.pdf 39

The $70 per tonne CO2 price is needed to induce a significant shift in output from existing coal generation to existing

and new baseload CCGT, i.e., it is calculated using short run marginal cost of existing coal rather than the long run

marginal cost. 40

Written comments sent to Deloitte on 14 February, 2011 and 9 March, 2011.

http://www.agl.com.au/Downloads/UBS%20-%20Australian%20Utilities%20Conference%20Apr10.pdf


o Similarly it serves to conduct feasibility on pumped storage hydro which otherwise

would not have had as much impetus had it not been for the aggressive RET target.

o The increased RET has likely resulted in us investing the feasibility of more wind

projects than we would have undertaken otherwise. While wind degrades the

condition and operation of our baseload plant, it is recognised that it is currently the

cheapest renewable technology and given the good wind resources in the state

there could be a further influx of wind onto the system. Investing in wind farms may

at least provide some element of control over the size of the wind farm and

potentially some element of control over dispatch.

o The increased RET has also increased the attractiveness of development of solar

(albeit with a significant government grant) since it is a better fit for WA load profile

and can be offered as a complementary renewable product to wind.

The Victorian generators note that the increased RET had also caused additional

transmission congestion which was affecting them and influenced their decision to augment

additional capacity.

Some generators expressed concern about expensive measures such as geothermal

considering the cost of drilling, uncertain nature of the resource and additional transmission

costs.

One of the market participants observed the increased RET is an expensive measure that

has little contribution to meeting energy growth requirement and has little impact on carbon

reduction.

4.5 Attitude of Lending Institutions

What was the attitude of lending institutions towards supporting baseload

generation investment?

How is Australia viewed as an investment destination compared with other

countries?

One of the coal generators noted that there was a noticeable change in the attitude

of the lending institutions. This genco noted that the “banks are more aggressive”

given the unpredictable nature of revenue from coal-fired power stations and

lending is being provided on a “deeply discounted basis”.

One of the gencos noted in the context of obtaining finance for refurbishment of an

old coal plant, “...while not intended to be run in pure base load mode and with a life

of only 10-15 years, being an old highly carbon intensive coal plant made it difficult

for the project to be banked – banks were very much aware of their reputational risk

in supporting this project”, and went on to say “...Currently acknowledged that it


would be extremely difficult to bank a baseload coal plant until there is certainty

around carbon”.

The gearing for baseload power stations, one of the gencos observed, used to be

around 65-70 per cent back in 1995/96 but is down to 40-45 per cent today.

One of the generators also noted that international banks are not interested in

financing Australian projects unless it is part of a consortium that includes an

Australian bank that can manage the sovereign risk.

One of the major banks noted that the continued shift in carbon policy starting with

the initial talks of extremely low $2 per tonne carbon price, followed by significant

transition measures such as 10 years of free permits and the present realities of

uncertainties, had made it extremely difficult for lending institutions to assess the

risk faced by fossil fuel generation. Even renewable projects that are not backed up

by PPAs are not attractive in Australia given the swings in Renewable Energy

Certificate (REC) prices and uncertainties around carbon policy that would impact

on REC prices. However, banks noted that wind farms with PPAs in Australia

remain good candidates for project finance.

Major gencos‟ expressed lack of confidence of banks, relatively illiquid contract

market and very little interest in baseload investment. These views are in

agreement with those expressed by some of the market participants that their

overseas partners are exploring investment opportunity in other markets.

Banks have commented that “the days of financing fully merchant stand-alone

baseload projects such as Pelican Point, Callide and Millmerran have ended”.

Another bank also observed that the Australian market has lost significant ground

from the days of the start of the NEM, mainly due to a state of confusion in the

policy development – both for carbon and renewables. Only those generators who

have the ability to inject significant equity and can absorb the risk through their retail

position are in a position to invest in Australia. However, as we have noted the

major gentailers also have significant concerns, at least for baseload gas

investment. Most market participants with an interest in overseas projects,

especially in Asia, noted that the terms they can get in other countries are more

favourable than those in Australia.

The situation seemed to be better in WA where one bank noted that the PPA terms

for baseload investment are favourable for supporting financing for near term

projects.

There was significant concern expressed on the issue of refinancing existing

assets. There was a general view that some of the existing coal assets would

eventually be at the mercy of the banks considering the uncertainty over their value

even two years out and the significant burden on working capital that buying

permits would impose. Conditions were stated to be very different in most major

Asian markets where the baseload investment activities are very buoyant including

some 40,000 MW of super-thermal coal fired projects being under construction in

India alone. Demand for coal from these markets was already exerting pressure on


domestic coal prices in Australia, and affecting the competitiveness of even recently

built efficient black coal projects in NSW and QLD.

Gas project investment is in a better state but as one of the market participants

observed these projects are worthwhile only if the investor also owns the gas. As

such, the active investment community in Australia is centred around a few major

gentailers who can finance new projects with significant equity injection.

4.6 Key Points

In summary, we note that our discussions with market participants reconfirm several

observations from the literature review as well as the findings of our historic analysis. In

particular, the following key points form the basis of our modelling scenarios that we have

analysed in the next section:

1. No new investment in coal projects: Practically no investment in coal is forthcoming in

Australia, with the exception of some refurbishment projects in Western Australia.

Investors in the NEM are currently not considering coal as an investment option due to

the uncertainty surrounding carbon policy, and at least one of the lending institutions

have also noted their reservations about financing carbon intensive projects;

2. Gas projects are investments of choice: Investment in gas projects is ongoing with a

significant number of planned gas-fired generation projects, albeit with a higher share of

OCGTs. However, the majority of the proponents of gas projects also have substantial

retail or upstream gas positions. Other players still consider gas investment risky and

prefer to consider investments in other countries;

3. Baseload generation investment in Australia is diminishing: There is a reduction in

baseload generation investment in Australia. While there is significant level of activity

for investment in renewable generation and also in OCGTs, baseload generation

development including CCGT has diminished in recent years. Investors are choosing

the investments with the least capital risk, namely OCGTs instead of CCGTs. As noted

in the preceding discussion, AGL have stated that uncertainties surrounding the ETS

have left the market with “investment paralysis”, OCGT is considered the only

investment option in order to minimise capital risk;

4. The contract market has become increasingly illiquid: It is currently very difficult for coal

generators to sell flat contracts as it is uncertain whether they will be impacted by a

carbon policy;

5. No retirement of coal: None of the coal generators stated any intention to retire coal

plants, with the exception of Hazelwood who are willing to shut down if the government

effectively buys them out of the market. The uncertainty surrounding carbon policy has

also affected decisions on planned retirement;

6. Maintenance and upgrades are being delayed: Decisions on spending capital to

improve operational efficiency are being deferred. This is currently resulting in less

efficient plant operating than would have been possible if policy had been certain;

7. Diminishing asset value: Policy uncertainty is diminishing asset value. Some of the coal

generators observed that their assets that are currently worth billions of dollars may

practically be written down in a few years time. This view is also being echoed in the

financing committee and has affected the availability of growth capital; and


8. Banks are imposing more stringent standards: Raising debt for new coal is almost

impossible in the current environment. Additionally, it is becoming increasingly difficult

to refinance existing coal assets. The Australian energy sector is increasingly being

viewed as a risky investment given current uncertainty surrounding carbon policy.


5 Model Results: CPU Estimates

5.1 Key Assumptions

This section details the input assumptions used in the modelling. The key assumptions include

the following:

1. Our modelling covers the period 1999/2000 to 2029/2030 (financial years);41

2. Our forecasts are based on a medium economic growth and average peak load

associated with a 50 per cent probability of exceedance (POE) estimate;

3. We have assumed delay of baseload generation investment to occur under policy

uncertainty scenarios and have constructed three scenarios – Scenario 1-3- that

assumes baseload generation investment resumes in 2017, 2020 and 2025,

respectively;

4. All our forecasts assume meeting the long term CPRS-5 scenario emissions target by

2030;

5. We have assumed the Renewable Energy Target in 2020 as a “policy constraint” in all

our scenarios;

6. We have used the most recent estimates of capital costs based on ACIL Tasman

September 2010 study prepared for the AEMO/DRET42

;

7. We have used the most recent estimates of fuel prices based on ACIL Tasman (2010)

study used for AEMO scenario modelling (except for Northern Territory where we have

used our own estimates of fuel prices). In particular, we have used an average gas

price of Scenario 1 (“Fast rate of change”) and Scenario 2 (“An uncertain world”) in

ACIL Tasman (2010) for East and West coast, as suggested by DRET; and

8. All our cost estimates are in real 2010 dollars.

Appendix C presents full details of model data and assumptions.

5.2 Model Results: CPU Estimates

A comparison of system costs between Policy Uncertainty and Policy Certainty scenarios

reveals the cost of policy uncertainty. Figure 9 and Figure 10 show the total annual cost of

policy uncertainty (in $ million) and average cost of policy uncertainty (in $/MWh), respectively.

An early resolution of uncertainty would limit damage quite considerably. For instance,

the CPU is at the most $1.2 billion per year for Scenario 1, but is close to $5 billion if

baseload investment is delayed significantly till 2025;

41

We have labelled 1999/2000 as “2000” etc for brevity in the discussion of results for brevity. 42

ACIL Tasman „Preparation of energy market modelling data for the Energy White Paper, supply assumptions report‟, prepared for AEMO/DRET, September, 2010.


For all Policy Uncertainty scenarios, CPU decreases over a 3-4 year period once

baseload investment resumes. The gap between Policy Certainty and Policy

Uncertainty diminishes as baseload capacity and generation increases, although the

inefficient investment in OCGT over the years means the system cost continues to

remain higher for the Policy Uncertainty scenario;

The average CPU estimates similarly show that

o an early resolution of uncertainty will add $4.73 per MWh to the (wholesale)

cost of energy if baseload investment resumes by 2017, i.e., in Scenario 1. If

we express this increase in cost as a percentage of residential tariff of

$188/MWh in 2009, the cost of policy uncertainty represents a 2.5 per cent

increase in residential tariff;43

;

o delaying baseload investment till 2020 would increase CPU to $7.05 per MWh

in 2019, i.e., in Scenario 2; and

o the cost impact would be over $16 per MWh, or four times, if baseload

investment is significantly delayed till 2025.

Figure 9: Cost of Policy Uncertainty: Undiscounted (Real 2010) $ million

43

Retail price cited in ABARES, Energy in Australia 2011, 2011.

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Figure 10: Cost of Policy Uncertainty: Average cost in (Real 2010) $ per MWh

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6 Appendix A: Market

Participants Interviewed

6.1 List of Market Participants Interviewed

Table 5 shows the list of market participants that we developed in discussion with DRET.

Table 5: List of Market Participants Interviewed

SL No Organisation Coverage

Current involvement Specific Issues

1 International Power VIC

Brown coal & gas generation & retail

Brown coal issues, retirement of Hazelwood

2 TRU Energy NEM Gentailer Developed new generation recently and secured a large GenTrader in NSW

3 AGL NEM Gentailer Largest retailer and gentailer

4 Verve Energy WA Generator Leading generator in WA

5 Loy Yang VIC Brown coal generation Brown coal generator

6 Horizon Energy WA Gentailer Remote area generation – Pilbara

7 NGF National APEX body Industry body for all generators with significant inputs to policy making

8 ANZ National Lender institution

Elicit views on financing baseload generation investment

9 Commonwealth Bank National

Lender institution


10 Westpac National Lender institution


11 Eraring Electricity NSW Generator

Include views of NSW black coal generators

12 Macquarie Generation NSW Generator

Include views of NSW black coal generators

13 Hydro Tasmania TAS Generator Views on RET and carbon policy from a renewable generator perspective

14 ERM Power QLD Gentailer Gas-fired generator and retailer


6.2 Key Questions

Our approach was to keep the interviews reasonably informal and emphasise the specific

viewpoints of different types of market participations (e.g., sole generators, gentailers, lending

institutions, etc). Nevertheless, the general theme of the discussions was based around the

following key questions:

1. Has your organisation undertaken planning and investigations into investing, or

committed to investing in generation in the past five years?

2. What reduction targets and carbon prices did the organisation take into account?

3. What would constitute policy certainty to you? In particular, which of the following

design aspects create the biggest form of uncertainty:

a. Scheme design? Including:

o transitional assistance for existing assets

o fixed versus floating carbon prices

b. The target?

c. Link to international schemes?

d. 1/5/10/20/30 year forward carbon prices?

4. Would the organisation have made a different set of investment decisions if the

proposed CPRS were to be operational by now?

5. What impact did increased RET target have on the organisation‟s investment

decisions?

6. What was the attitude of lending institutions towards supporting baseload generation

investment?

7. What is the organisation‟s current standing on a carbon price mechanism and how is it

being incorporated it in its current set of investment decisions?

8. Are there any plans for retirement of plants?

a. If so how is the uncertainty around a carbon price mechanism impacting on

these decisions?

b. Has there been a noticeable change in capex and opex spend for the existing

portfolio of plants (especially coal plants), across pre-2005, 2005-2010 and

current plan for post-2010?


9. Does the organisation‟s valuation approach explicitly take into account the impact of

policy uncertainty in investment decisions (e.g., put a value on flexibility of OCGT that

can be converted to CCGT at a later date)?

10. How does the organisation price risks?

11. Is there a certain carbon price required to make the switch from coal to gas

economical?

12. How is Australia viewed as an investment destination compared with other countries?

If uncertainty is noted:

o What timeframe is industry making investment decisions during this period of

uncertainty?

o In the current state of uncertainty, how is this affecting organisation‟s ability to enter

into contracts and accessing finance?

Depending on the specific context for an organisation, we have also added questions on how

future uncertainty is being managed, including:

o How are you managing the uncertainty around future carbon price?

o Is uncertainty around the future carbon price stalling investment?

o What factors would you factor into your investment decisions, regardless of a carbon

price?

o What do you consider to be the main influences on electricity prices?


7 Appendix B: Illustrative

Example of CPU

We have provided an illustrative example to explain the calculation of cost of policy uncertainty

including the interrelationship between marginal cost and resource costs.

Consider a system with the load duration curve shown in Table 6.

Table 6: Load Duration Curve: Illustrative Example

Peak Shoulder Base

Load (MW) 10,000 5,000 2,000

Duration (hours) 1,000 5,000 2,760

Energy (GWh) 10,000 25,000 5,520

We assume that this load needs to be met by building a combination of new CCGT and OCGT

that have the following cost characteristics:

CCGTs have an annualised fixed cost of $158,000/MW/year and a short run marginal

cost (SRMC) of $40 per MWh;

OCGTs have a lower annualised fixed cost of $106,000/MW/year but a higher short run

marginal cost (SRMC) of $90 per MWh;

We do not consider any reserve requirement or any other side constraint such as RET etc in

this example.

Table 7 shows the optimal generation, total system cost (or, resource cost) and prices for the

system for two cases with and without considering the CCGT investment.


Table 7: Optimal Generation (MW), Resource Costs ($ billion) and Prices ($/MWh)

Peak Shoulder Base

Case 1: Both CCGT and OCGT Allowed: Total cost of $3.1908 billion

Generation

CCGT (5,000 MW) 5,000 5,000 2,000

OCGT (5,000 MW) 5,000

Price ($/MWh) 196.00 40.40 40.00

Case 2: Only OCGT Allowed: Total cost of $4.7068 billion

Generation

OCGT (10,000 MW) 10,000 5,000 2,000

Price ($/MWh) 196.00 90.00 90.00

The following observations are in order:

1. The peak price of $196 per MWh for both Case 1 and Case 2 reflects the fact that 1

MW increase in peak demand requires an additional MW of OCGT to be installed (i.e.,

5,001 MW instead of 5,000 MW in Case 1 and 10,001 MW instead of 10,000 MW in

Case 2) and the extra MW needs to be operated for 1,000 hours of peak during the

hour. Hence, the marginal cost to meet the extra MW is:

a) $106,000 in fixed cost;

b) 1000 MWh X $90, or $90,000 in variable cost; and

c) The total cost of $196,000 for 1,000 MWh yields a peak long run marginal cost

of $196 per MWh.

2. The shoulder price of $40.40 per MWh for Case 1 similarly reflects that an increase in

shoulder demand from 5,000 MW to 5,001 MW requires an additional MW of CCGT that

operates during shoulder period and also during peak. It therefore reduces the peaking

OCGT capacity requirement from 5,000 MW to 4,999 MW. Hence, the marginal cost for

shoulder period is:

a) $158,000 MW in fixed cost, less $106,000 avoided cost of peaking generation,

or a net cost of $52,000;

b) 6,000 hours of running cost of the CCGT or 6,000 MWh X 40, or $240,000 in

variable cost; less

c) 1,000 MWh of less peaking generation or 1,000 X 90, or $90,000; and

d) Therefore, the net cost is $52,000+$240,000-$90,000 = $202,000, which

spread across the 5,000 hour shoulder period yields $40.40 per MWh.

3. The off-peak price simply reflects the SRMC of CCGT because there is plenty of

surplus capacity to meet any increase in off-peak demand.

4. The revenue adequacy principle is observed in both cases. For instance,

Case 1:

a) Load-weighted price is $78.74 calculated by multiplying the price for each block

with the energy supplied for the block and dividing by the total energy requirement

for the year;

b) Total system cost or resource cost of $3.1908 billion matches the load-weighted

price of $78.74 per MWh multiplied by total energy of 40,520 GWh.

Case 2:


c) Load-weighted price is $116.15 calculated by multiplying the price for each block with

the energy supplied for the block and dividing by the total energy requirement for the

year;

d) Total system cost or resource cost of $4.7068 billion matches the load-weighted price of

$116.15 per MWh multiplied by total energy of 40,520 GWh.

Therefore, the increase in resource cost due to exclusion of CCGT option of (4.7068-3.1908) or

$1.516 billion is entirely consistent with the increase in long run marginal cost for shoulder and

off-peak period. For instance, one can also calculate the increase in resource cost using load-

weighted price increase from $78.74 per MWh to $116.15 per MWh, or an increase of $37.41

per MWh, multiplied by the total energy of 40,520 GWh.


8 Appendix C: Detailed

Modelling Assumptions

8.1.1 Demand

Electricity demand is modelled using an annual load duration curve (LDC) approach

where the demand over the (financial) year is divided into 40 unequal load blocks. This

provides a reasonable approximation to actual load and system cost.44

A sample LDC for NSW is shown in Figure 11. Block sizes are smaller over the peak

periods to more accurately capture the peak load shape.

Figure 11: NSW Demand 2009/10

Demand growth is based on the forecasts presented in:

NEM Statement of Opportunities (SOO) 2010;

WA Independent Market Operator‟s (IMO) Statement of Opportunities;

NT 2008-09 Power System Review forecasts; and

44

We have undertaken comparisons of dispatch optimisation using detailed chronological models and LDC-based

approximations to conclude that the difference in system cost is typically quite small. See for example, the discussion in

section 5.6 of the IEA study by, Chattopadhyay et al, Assessing the Value of Demand Response in the NEM, prepared

for the International Energy Agency, December 2006.

(http://www.demandresponseresources.com/Portals/0/Australia/Australia_CRA%20Report%20on%20Demand%20Resp

onse%20Dec%2006.pdf )

4

5

6

7

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11

12

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GW

)

Half Hour

LDC

Blocks


ABARE forecast energy demand for those years that the NEM/WA/NT planning

documents mentioned above do not cover the forecast period up to 2030.45

Energy and peak demand for the Medium 50POE forecast are shown in Figure 12 and Figure

13.

Figure 12: Historic and Forecast Energy Demand (Medium 50POE)

Figure 13: Historic and Forecast Peak Demand (Medium 50POE)

45

Australian Energy Projections to 2029-30, ABARE March 2010

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8.1.2 Supply

Existing and committed generators as reported in the NEM SOO 2010, WA IMO SOO

2010 and NT 2008-09 Power System Review are modelled.

All new capacity addition decisions are optimised. Deloitte‟s LTM model allows the

optimal selection of capacity given capital and operating costs, demand assumptions

and reliability constraint which is modelled using a deterministic capacity reserve

constraint set to reflect the NEM reliability standard of 0.002 per cent of expected

unserved energy.

Capital Cost Assumptions: There are multiple sources for capital cost estimates used in

the study, namely:

Capital costs and technology learning curves have been obtained from the

report „Preparation of energy market modelling data for the Energy White

Paper, supply assumptions report‟ (Sep 2010), prepared by the ACIL Tasman

for AEMO/DRET.

Biomass and small hydro capital cost assumptions are based on our own

estimates, as well as those that were used for prior NEM studies such as the

study conducted for the National Generators Forum.46

The wind cost estimate is based on the average of the small, medium and large

forecasts provided in the ACIL report. The geothermal cost estimate is based

on the average of the EGS and HSA estimates.

Figure 14 and Figure 15 below show the learning curves for all the technology

types modelled.

Figure 14: Capital Cost Learning Curve for Coal and Gas Plant

46

Charles River Associates, Analysis of Greenhouse Gas Policies for the Australian Electricity Sector, Report

prepared for the National Generators Forum, 2007.

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Brown Coal

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OCGT


Figure 15: Capital Cost Learning Curve for Renewable Plant

Fuel Price Assumptions: Fuel prices for East and West coast generators are derived

from the ACIL Tasman report " Preparation of energy market modelling data for the

Energy White Paper, supply assumptions report" (Sep 09). Average of scenarios 1 and

2 are used. NT fuel prices are based on our own assumptions.

Variable and fixed operations and maintenance costs are from the ACIL Tasman report

"Fuel Resource, New Entry and Generation Costs in the NEM" (Feb 09). For WA and

NT the costs are based on the average of the NEM data for similar generator type.

For the historical analysis, capital costs are obtained from „Fuel and capital costs in the

NEM‟ (Oct 2008) prepared by ACIL Tasman for the QCA. Fuel prices are obtained from

„SRMC and LRMC of Generators in the NEM‟ (March 2003), prepared by ACIL Tasman

for the IRPC and NEMMCO.

8.1.3 Operating assumptions

1. Auxiliary, heat rate and emissions rates are from the ACIL Tasman report "Fuel

Resource, New Entry and Generation Costs in the NEM" (Feb 09). For WA and NT the

rates are based on the average of the NEM data for the generator type.

2. Forced outage and maintenance rates are from the NEMMCO report "2008 ANTS

Consultation: Issues Paper". For WA and NT the rates are based on the average of the

NEM data for the generator type.

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30

Ca

pit

al C

os

t ($

/kW

)

Wind

Geothermal

Smallhydro

Biomass


8.2 Capital Cost Assumptions

Technology Capital costs in 2015 ($/kW installed)

Capital costs in 2030 ($/kW installed)

IGCC - Brown coal $5,025 $2,934 IGCC - Brown coal with CCS $6,262 $3,374 IGCC - Black coal $4,201 $3,232 IGCC - Black coal with CCS $5,233 $3,726 Supercritical PC - Brown coal $3,571 $3,214 Supercritical PC - Black coal $2,676 $2,408 CCGT - Without CCS $1,368 $1,170 CCGT - With CCS $2,359 $1,757 OCGT - Without CCS $985 $872 Wind - Small scale (50 MW) $3,178 $2,543 Wind - Medium scale (200 MW) $2,886 $2,308 Wind - Large scale (500 MW) $2,744 $2,195 Geothermal - Enhanced Geothermal System (EGS) $6,899 $6,507 Geothermal - Hot Sedimentary Aquifers (HSA) $6,600 $5,715

8.3 Fuel Price Assumptions (Real 2010 dollars per GJ)

East Coast Gas

West Coast Gas

New QLD Coal

New NSW Coal

New VIC Coal

2010 5.30 8.10 1.46 1.34 0.57

2011 5.30 8.10 1.45 1.20 0.57

2012 5.20 8.10 1.45 1.19 0.57

2013 5.20 8.00 1.44 1.18 0.57

2014 5.30 7.50 1.43 1.17 0.56

2015 5.40 7.10 1.43 1.16 0.56

2016 5.70 6.80 1.42 1.15 0.56

2017 6.00 7.00 1.42 1.14 0.56

2018 6.40 7.10 1.42 1.13 0.56

2019 6.50 7.10 1.41 1.12 0.56

2020 6.80 7.00 1.41 1.11 0.56

2021 6.90 6.40 1.40 1.10 0.55

2022 7.10 6.40 1.40 1.09 0.55

2023 7.10 6.30 1.40 1.08 0.55

2024 7.10 6.70 1.40 1.07 0.55

2025 7.20 7.20 1.39 1.06 0.55

2026 7.30 7.10 1.39 1.06 0.55

2027 7.60 7.70 1.39 1.04 0.55

2028 7.60 7.80 1.39 1.04 0.55

2029 7.80 7.80 1.38 1.03 0.54

2030 7.90 7.90 1.38 1.02 0.54


8.4 Detailed Generator Data

Generator Comm Day Comm Year Retr Day Retr Year Region Company IRLF Cap Aux FO Rate Maint Factor Type VOM FOM Heat Rate Emissions

Bayswater 1 1982

NSW MacquarieGeneration 0.9552 2740 0.060 0.0384 0.0767 Sub_Cr_BlkCoal 1.19 49000 10028 991.8

Blowering 1 1969

NSW SnowyHydroLimited 1.0130 40 0.000 0.0444 0.0000 Hydro 0.00 12500 1000 0.0

Colongra 336 2009

NSW DeltaElectricity 0.9811 696 0.030 0.1000 0.0137 CCGT 10.10 13000 11250 736.9

Eraring 1 1982

NSW EraringEnergy 0.9866 2880 0.065 0.0384 0.0767 Sub_Cr_BlkCoal 1.19 49000 10169 998.6

GunningWind 182 2011

NSW Acciona 0.9835 47 0.000 0.0160 0.0500 Wind 0.00 48300 1000 0.0

Guthega 1 1955


HumeNSW 1 1957

NSW EraringEnergy 1.0583 14.5 0.000 0.0444 0.0000 Hydro 0.00 12500 1000 0.0

HVGTS 1 1988

NSW MacquarieGeneration 0.9591 47 0.030 0.1000 0.0137 OCGT_Oil 9.61 13000 12857 964.3

LeafsGully 1 2012

NSW AGLHydroPartnership 0.9835 360 0.010 0.1000 0.0110 OCGT 7.70 13000 11238 760.6

Liddell 1 1971

NSW MacquarieGeneration 0.9541 2082.5 0.050 0.0384 0.0767 Sub_Cr_BlkCoal 1.19 52000 10651 1081.1

MtPiper 1 1992

NSW DeltaElectricity 0.9703 1400 0.050 0.0384 0.0767 Sub_Cr_BlkCoal 1.32 49000 9730 935.0

Munmorah 1 1969 152 2014 NSW DeltaElectricity 0.9864 600 0.073 0.0384 0.0767 Sub_Cr_BlkCoal 1.19 55000 11688 1157.1

NSWWind 1 2008

NSW GenericWind 0.9835 16.62 0.000 0.0160 0.0500 Wind 0.00 27590 1000 0.0

Redbank 91 2001

NSW RedbankProjectPtyLtd 0.9571 148 0.080 0.0384 0.0767 Sub_Cr_BlkCoal 1.19 49500 12287 1212.7

Shoalhaven 1 1977

NSW EraringEnergy 1.0134 240 0.000 0.0444 0.0000 Hydro 0.00 12500 1000 0.0

Smithfield 1 1997

NSW SitheAustraliaPower 1.0021 160 0.050 0.0243 0.0658 Cogeneration 2.40 25000 8780 575.1

Tallawarra 1 2009

NSW TRUenergySAGenerationPtyLtd 0.9946 441 0.030 0.0463 0.0658 CCGT 1.05 31000 7200 471.6

Tumut3 1 1973


Upptumut 1 1959


Uranquinty 1 2009

NSW WamboPower 0.9406 652 0.030 0.1000 0.0137 OCGT 10.10 13000 11250 736.9

ValesPt 1 1978


Wallerawang 1 1976


WoodlandWind 1 2011

NSW InfigenEnergy 0.9835 42 0.000 0.0160 0.0500 Wind 0.00 48300 1000 0.0

Berrimah 1 1979

NT PWC 1 30 0.020 0.0985 0.0118 OCGT 8.10 13000 13000 537



Brewer 275 2010

NT PWC 1 9 0.038 0.1000 0.0151 OCGT_Oil 8.67 16000 15000 681

ChannelIsland 1 1986

NT PWC 1 232 0.022 0.0503 0.0484 CCGT 2.31 28692 10000 451

Katherine 1 1987

NT PWC 1 21 0.020 0.0985 0.0118 OCGT 8.10 13000 13000 537

LMSShoalBay 213 2005

NT IPP 1 1 0.000 0.0000 0.0000 LandfillGas 15.00 9590 14400 0

PineCreek 1 1989

NT IPP 1 35 0.038 0.1000 0.0151 OCGT_Oil 8.67 16000 16000 681

RonGoodin 1 1988

NT PWC 1 63 0.038 0.1000 0.0151 OCGT_Oil 8.67 16000 16000 681

TennantCreek 1 1987

NT PWC 1 17 0.038 0.1000 0.0151 OCGT_Oil 8.67 16000 18000 681

Weddell 1 2008

NT PWC 1 86 0.020 0.0985 0.0118 OCGT 8.10 13000 10432 542

Barcaldine 1 1996

QLD QPTC 0.9963 49 0.030 0.0463 0.0822 CCGT 2.40 25000 9000 510.3

BarronGorge 1 1963

QLD StanwellCorporation 1.0922 60 0.000 0.0444 0.0466 Hydro 0.00 10000 1000 0.0

Braemar 213 2006

QLD BraemarPowerProjectPtyLtd 0.9429 470 0.025 0.1000 0.0164 OCGT 7.93 13000 12000 680.4

Braemar2 152 2009

QLD ERMPower 0.9429 507 0.025 0.1000 0.0164 CCGT 7.93 13000 12000 680.4

CallideA 1 1965 1 2001 QLD CS_Energy 0.9682 120 0.070 0.0384 0.0438 Sub_Cr_BlkCoal 1.20 49500 9972 947.4

CallideAOxyFiring 183 2011 152 2015 QLD CS_Energy 0.9682 30 0.070 0.0384 0.0438 Sub_Cr_BlkCoal 1.20 49500 9972 0.0

CallideB 1 1988

QLD CS_Energy 0.9434 700 0.070 0.0384 0.0438 Sub_Cr_BlkCoal 1.20 49500 9972 947.4

CallidePP 1 2001

QLD CallidePowerTradingPtyLtd 0.9452 900 0.048 0.0384 0.0438 Sub_Cr_BlkCoal 1.20 49500 9474 918.9

Collinsville 1 1998

QLD QPTC 1.0360 187 0.080 0.0384 0.0438 Sub_Cr_BlkCoal 1.32 65000 12996 1187.9

Condamine 336 2009

QLD QLDGasCo 0.9651 135 0.030 0.0450 0.0822 CCGT 9.61 31000 7500 581.5

DarlingDowns 183 2010

QLD OriginEnergyElectricityLimited 0.9429 618 0.060 0.0450 0.0822 CCGT 1.05 31000 7826 564.4

Gladstone 1 1976

QLD QPTC 0.9818 1680 0.050 0.0384 0.0438 Sub_Cr_BlkCoal 1.19 52000 10227 962.4

Kareeya 1 1957

QLD StanwellCorporation 1.0802 86 0.000 0.0444 0.0466 Hydro 0.00 10000 1000 0.0

KoganCreek 244 2007

QLD CS_Energy 0.9429 734 0.080 0.0384 0.0438 Sup_Cr_BlkCoal 1.25 48000 9600 921.6

MackayGT 1 1976 1 2016 QLD StanwellCorporation 1.0327 30 0.030 0.1000 0.0164 OCGT_Oil 9.05 13000 12857 964.3

MillmerranPP 1 2003

QLD MillmerranEnergyTraderPtyLtd 0.9620 852 0.045 0.0384 0.0438 Sub_Cr_BlkCoal 1.19 48000 9600 902.4

MtStuart 1 1998

QLD QPTC 1.0229 401 0.030 0.1000 0.0164 OCGT_Oil 9.05 13000 12000 900.0

Oakey 336 1999

QLD QPTC 0.9433 304 0.030 0.1000 0.0164 OCGT_Oil 9.61 13000 11043 626.1

QLDWind 1 2008

QLD GenericWind 0.9853 12.45 0.000 0.0160 0.0500 Wind 0.00 27590 1000 0.0

RomaGT 1 1999

QLD OriginEnergyElectricityLimited 0.9654 61 0.030 0.1000 0.0164 OCGT 9.61 13000 12000 680.4



SpringGully 1 2013

QLD OriginEnergyElectricityLimited 0.9853 1000 0.030 0.0463 0.0438 CCGT 1.05 31000 7500 376.6

Stanwell 1 1993

QLD StanwellCorporation 0.9815 1429 0.070 0.0384 0.0438 Sub_Cr_BlkCoal 1.19 49000 9890 913.8

SwanbankA 1 1967 1 2002 QLD CS_Energy 0.9930 833 0.080 0.0384 0.0438 Sub_Cr_BlkCoal 1.19 55000 11803 1090.6

SwanbankB 1 1970 91 2012 QLD CS_Energy 0.9930 180 0.080 0.0384 0.0438 Sub_Cr_BlkCoal 1.19 55000 11803 1090.6

SwanbankE 1 2002

QLD CS_Energy 0.9930 360 0.030 0.0463 0.0822 CCGT 1.05 31000 7660 434.3

Tarong 1 1984

QLD TarongEnergy 0.9679 1400 0.080 0.0384 0.0438 Sub_Cr_BlkCoal 1.43 49500 9945 935.8

TNPS1 1 2002

QLD TarongEnergy 0.9680 443 0.050 0.0384 0.0438 Sub_Cr_BlkCoal 1.43 48000 9184 864.2

Wivenhoe 1 1984

QLD TarongEnergy 0.9883 500 0.000 0.0444 0.0466 Hydro 0.00 10000 1000 0.0

Yabulu 1 2005

QLD QPTC 1.0406 240 0.030 0.1000 0.0164 OCGT_Oil 5.09 31000 7826 443.7

YarwunCoGen 152 2010

QLD RioTinto 0.9883 156 0.020 0.0239 0.0822 Cogeneration 0.00 25000 10588 600.4

AGLHal 1 2002

SA AGLHydroPartnership 0.9746 201 0.025 0.1000 0.0110 OCGT 9.61 13000 15000 1048.5

Angaston 1 2005

SA InfratilEnergyAustraliaPtyLtd 0.9505 49 0.025 0.1000 0.0110 OCGT 9.61 13000 13846 1013.5

ClementsGap 213 2009

SA PacificHydro 0.9644 57 0.000 0.0160 0.0500 Wind 0.00 40000 1000 0.0

DryCreek 1 1973

SA SynergenPowerPtyLtd 1.0072 131 0.030 0.1000 0.0110 OCGT 9.61 13000 13846 967.8

HalletWind 91 2008

SA AGLHydroPartnership 0.9746 77 0.000 0.0160 0.0500 Wind 0.00 40000 1000 0.0

HalletWind2 275 2009






Ladbroke 1 2000

SA OriginEnergyElectricityLimited 0.9741 78 0.030 0.1000 0.0110 OCGT 3.60 13000 12000 838.8

LakeBonneyWind 152 2008

SA NPPower 0.9388 159 0.000 0.0160 0.0500 Wind 0.00 40000 1000 0.0

LakeBonneyWind3 182 2011

SA InfigenEnergy 0.9693 39 0.000 0.0160 0.0500 Wind 0.00 48300 1000 0.0

Mintaro 1 1984


NorthernPS 1 1985

SA NRGFlindersOpServicePtyLtd 0.9655 544 0.050 0.0436 0.0767 Sub_Cr_brownCoal 1.19 55000 10315 948.0

Osborne 1 1998

SA NRGFlindersOpServicePtyLtd 0.9998 184 0.050 0.0243 0.0438 Cogeneration 5.09 25000 8571 599.1

PlayfordB 1 1960

SA NRGFlindersOpServicePtyLtd 0.9677 220 0.080 0.0436 0.0767 Sub_Cr_brownCoal 3.00 70000 16438 1510.7

PortLincoln 1 1998

SA SynergenPowerPtyLtd 0.8654 65 0.080 0.1000 0.0110 OCGT_Oil 9.61 13000 13846 1013.5

PPCCGT 1 2000

SA PelicanPointPowerLimited 0.9988 461 0.020 0.0463 0.0438 CCGT 1.05 31000 7500 524.3

Quarantine 1 2002




Quarantine6 1 2012


SAWind 1 2008

SA GenericWind 0.9693 811.4 0.000 0.0160 0.0500 Wind 0.00 27590 1000 0.0

SnowtownWind 152 2008

SA TrustPower 0.9283 99 0.000 0.0160 0.0500 Wind 0.00 40000 1000 0.0

SnowtownWind2 1 2011

SA TrustPower 0.9693 206 0.000 0.0160 0.0500 Wind 0.00 48300 1000 0.0

Snuggery 1 1978


TorrensA 1 1967

SA TRUenergySAGenerationPtyLtd 0.9998 492 0.050 0.0243 0.0438 Steam_Gas 2.26 40000 13043 911.7

TorrensB 1 1977

SA TRUenergySAGenerationPtyLtd 0.9998 810 0.050 0.0243 0.0438 Steam_Gas 2.26 40000 12000 838.8

WaterlooWind 1 2011

SA Roaring40s 0.9693 111 0.000 0.0160 0.0500 Wind 0.00 48300 1000 0.0

BellBay 1 1997

TAS BellBayPowerPtyLtd 0.9985 240 0.050 0.0243 0.0438 Steam_Gas 7.93 40000 11250 642.4

BellBayThree 275 2006

TAS BellBayPowerPtyLtd 0.9996 120 0.025 0.1000 0.0123 OCGT 7.93 13000 12414 708.8

MusselroeWind 1 2013

TAS Roaring40s 0.9956 168 0.000 0.0160 0.0500 Wind 0.00 48300 1000 0.0

PulpMill 1 2013

TAS Gunns 0.9956 180 0.000 0.0500 0.1000 Biomass 5.00 48560 9022 0.0

TamarValley 244 2009

TAS AuroraEnergy 0.9993 208 0.030 0.0463 0.0438 CCGT 1.05 31000 7500 428.3

TamarValleyOCGT 91 2009

TAS AuroraEnergy 0.9996 58 0.025 0.0463 0.0438 OCGT 7.93 13000 12414 708.8

TASHydro 1 1950

TAS Hydro-ElectricCorporation 0.9853 2150 0.000 0.0444 0.0466 Hydro 0.00 14228 1000 0.0

TasWind 1 2008

TAS GenericWind 0.9956 142.5 0.000 0.0160 0.0500 Wind 0.00 27590 1000 0.0

AGLSom 1 2002

VIC AGLHydroPartnership 0.9943 148 0.025 0.1000 0.0082 OCGT 9.61 13000 15000 856.5

Anglesea 1 1969

VIC SECV 1.0114 156 0.100 0.0436 0.0521 Sub_Cr_brownCoal 1.19 81000 13235 1208.4

Bairnsdale 1 2001

VIC AlintaSalesPtyLtd 0.9683 80 0.030 0.1000 0.0082 OCGT 2.26 13000 10588 604.6

Bogong 1 2010

VIC AGLHydroPartnership 0.9912 140 0.000 0.0409 0.0466 Smallhydro 0.00 50000 1000 0.0

DartMouth 1 1960

VIC AGLHydroPartnership 1.0271 120 0.000 0.0444 0.0447 Hydro 0.00 14227 1000 0.0

Eildon 1 1956


Hazelwood 1 1964

VIC HazelwoodPower 0.9691 1600 0.100 0.0436 0.0521 Sub_Cr_brownCoal 1.19 84030 16364 1526.7

HumeV 1 1957

VIC EraringEnergy 1.0127 15 0.000 0.0444 0.0447 Hydro 0.00 14227 1000 0.0

JeeralangA 1 1979

VIC EcogenEnergyPtyLtd 0.9659 216 0.030 0.1000 0.0082 OCGT 9.05 13000 15721 897.6

JeeralangB 1 1980

VIC EcogenEnergyPtyLtd 0.9659 236 0.030 0.1000 0.0082 OCGT 9.05 13000 15721 897.6

LavertonNorth 336 2006

VIC SnowyHydroLimited 0.9961 320 0.025 0.1000 0.0082 OCGT 7.93 13000 11842 676.2

LoyYangA 1 1984

VIC LoyYangMMCPtyLtd 0.9715 2230 0.090 0.0436 0.0521 Sub_Cr_brownCoal 1.19 79000 13235 1215.0



LoyYangB 1 1993

VIC IPMAustraliaLimited 0.9715 1008 0.075 0.0436 0.0521 Sub_Cr_brownCoal 1.19 51200 13534 1242.4

MacarthurWind 91 2013

VIC AGLHydroPartnership 0.9845 365 0.000 0.0160 0.0500 Wind 0.00 48300 1000 0.0

McKay 1 1980


Mortlake2 1 2013

VIC OriginEnergyElectricityLimited 0.9845 450 0.025 0.0463 0.0438 CCGT 7.93 13000 12414 411.1

MortlakeOCGT 305 2010

VIC OriginEnergyElectricityLimited 0.9845 536 0.030 0.1000 0.0110 OCGT 8.33 13000 11250 642.4

Morwell 1 1958

VIC EnergyBrixAustralia 0.9674 164 0.150 0.0436 0.0521 Sub_Cr_brownCoal 1.19 60000 15000 1489.5

Murray 1 1967

VIC SnowyHydroLimited 0.9800 1528 0.000 0.0444 0.0411 Hydro 0.00 27137 1000 0.0

Newport 1 1980

VIC EcogenEnergyPtyLtd 0.9939 493 0.050 0.0243 0.0712 Steam_Gas 2.26 40000 10811 617.3

OaklandsWind 213 2011

VIC AGLHydroPartnership 0.9845 55 0.000 0.0160 0.0500 Wind 0.00 48300 1000 0.0

ValleyPower 1 2002

VIC ValleyPowerPtyLtd 0.9715 303 0.030 0.1000 0.0082 OCGT 9.61 13000 15000 856.5

VICWind 1 2008

VIC GenericWind 0.9845 376.7 0.000 0.0160 0.0500 Wind 0.00 27590 1000 0.0

WestKiewa 1 1955


Yallourn 1 1973

VIC TRUenergyYallournPtyLtd 0.9471 1454 0.089 0.0436 0.0521 Sub_Cr_brownCoal 1.19 82400 15319 1421.6

AlbanyWindfarm 1 2001

WA Verve 1 21.6 0.000 0.0160 0.0500 Wind 0.00 44674 0 0

AlintaDSM 1 2010

WA Alinta 1 17 0.000 0.0000 0.0000 DSR 0.00 7500 0 0

AlintaWF 1 2006

WA Alinta 1 89.1 0.000 0.0160 0.0500 Wind 0.00 44674 0 0

Atlas 1 1900

WA PerthEnergy 1 0.934 0.000 0.0000 0.0000 LandfillGas 15.00 9590 14400 0

BarrickDSM 1 2010

WA Barrick_Kanowna 1 9 0.000 0.0000 0.0000 DSR 0.00 7500 0 0

Bluewaters1 274 2008

WA GriffinPower 1 215.9 0.076 0.0384 0.0438 Sup_Cr_BlkCoal 1.25 48000 9000 923.9

Bluewaters2 244 2009

WA GriffinPower 1 215.9 0.076 0.0384 0.0438 Sup_Cr_BlkCoal 1.25 48000 9000 923.9

BremerBayWF 91 2005

WA Verve 1 0.66 0.000 0.0160 0.0500 Wind 0.00 44674 0 0

BridgetownBiomass 1 2009

WA WABiomass 1 40 0.000 0.0500 0.1000 Biomass 5.00 48560 9400 0

Canning 63 1995

WA LandfillGasAndPower 1 1.188 0.000 0.0000 0.0000 LandfillGas 15.00 9590 14400 0

Cockburn 1 2003

WA Verve 1 231.8 0.022 0.0503 0.0484 CCGT 2.31 28692 11600 451

CollgarWF 91 2012

WA CollgarWindFarm 1 206 0.000 0.0160 0.0500 Wind 0.00 44674 0 0

Collie 1 1999

WA Verve 1 318 0.063 0.0384 0.0584 Sub_Cr_BlkCoal 1.24 51083 10000 925.8

DMTEnergyDSM 1 2010

WA DMTEnergy 1 17 0.000 0.0000 0.0000 DSR 0.00 7500 0 0

EmuDownsWF 274 2006

WA EmuDowns 1 80 0.000 0.0160 0.0500 Wind 0.00 44674 0 0



EnergyResponseDSM 1 2010

WA EnergyResponse 1 73 0.000 0.0000 0.0000 DSR 0.00 7500 0 0

Geraldton 1 1973

WA Verve 1 15.516 0.020 0.0985 0.0118 OCGT 8.10 13000 15500 537

Gosnells 275 2003

WA AGL 1 0.656 0.000 0.0000 0.0000 LandfillGas 15.00 9590 14400 0

GriffinDSM 1 2010

WA GriffinPower 1 20 0.000 0.0000 0.0000 DSR 0.00 7500 0 0

Henderson 1 2006

WA WasteGasResources 1 2.66 0.000 0.0500 0.1000 Biomass 5.00 48560 9400 0

Kalamunda 123 1996


KalbarriWF 208 2008

WA Verve 1 1.7 0.000 0.0160 0.0500 Wind 0.00 44674 0 0

Kambalda 1 1996

WA SouthernCrossEnergy 1 11.995 0.020 0.0985 0.0118 OCGT 8.10 13000 11600 537

KemertonGT11 1 2005

WA Verve 1 143 0.000 0.0243 0.0123 Gas_Diesel 2.90 9590 11600 537

KemertonGT12 1 2005

WA Verve 1 141.7 0.000 0.0243 0.0123 Gas_Diesel 2.90 9590 11600 537

Kwinana_1 1 1970 1 2012 WA Verve 1 120 0.022 0.0503 0.0484 Steam Turbine 2.31 28692 11000 537

Kwinana_2 1 1970 1 2012 WA Verve 1 120 0.022 0.0503 0.0484 Steam Turbine

2.31 28692 11000 537


2.31 28692 11000 537


2.31 28692 11000 537

Kwinana_5 1 1976 1 2016 WA Verve 1 174 0.022 0.0503 0.0484 Steam Turbine (Gas/Coal/Liquid) 2.31 28692 10800 537

Kwinana_6 1 1976 1 2016 WA Verve 1 177 0.022 0.0503 0.0484 Steam Turbine (Gas/Coal/Liquid) 2.31 28692 10800 537

Kwinana_GT1 1 1972


Kwinana_GT2 1 2011


Kwinana_GT3 1 2011


Kwinana_WE 1 2012

WA WesternEnergy 1 105 0.000 0.0243 0.0123 Gas_Diesel 2.90 9590 15500 537

KwinanaCogen 1 1999

WA Verve 1 76.9 0.040 0.0241 0.0639 Cogeneration 2.49 25000 7200 591

MountHerronPS 1 2010

WA MountHerronEngineering 1 0.223 0.000 0.0500 0.1000 Biomass 5.00 48560 9400 0

MtBarkerWF 32 2011

WA SkyFarming 1 2.4 0.000 0.0160 0.0500 Wind 0.00 44674 0 0

Muja_1 1 1966 91 2007 WA Verve 1 60 0.063 0.0384 0.0584 Sub_Cr_BlkCoal 1.24 51083 11000 925.8




Muja_5 1 1981




Muja_6 1 1981


Muja_7 1 1981


Muja_8 1 1981


Mungarra_1 1 1990

WA Verve 1 32.15 0.020 0.0985 0.0118 OCGT 8.10 13000 13300 537

Mungarra_2 1 1990

WA Verve 1 32.15 0.020 0.0985 0.0118 OCGT 8.10 13000 13300 537

Mungarra_3 1 1990

WA Verve 1 31.999 0.020 0.0985 0.0118 OCGT 8.10 13000 13300 537

NewGenKwinana 305 2008

WA NewGen 1 320 0.022 0.0503 0.0484 CCGT 2.31 28692 7200 400

NewGenNeerabup 274 2010

WA NewGen 1 330.6 0.010 0.0985 0.0118 OCGT 7.70 13000 11238 711

ParkestonPS 1 1981

WA GoldfieldsPower 1 61.4 0.020 0.0985 0.0118 OCGT 8.10 13000 11600 537

Picton 1 2006

WA TeslaCorp 1 9.9 0.010 0.0985 0.0118 OCGT 7.70 13000 11238 711

Pinjar_1 1 1990


Pinjar_10 1 1990

WA Verve 1 107 0.020 0.0985 0.0118 OCGT 8.10 13000 12500 537

Pinjar_11 1 1990

WA Verve 1 115 0.020 0.0985 0.0118 OCGT 8.10 13000 12500 537

Pinjar_2 1 1990


Pinjar_3 1 1990


Pinjar_4 1 1990


Pinjar_5 1 1990


Pinjar_7 1 1990


Pinjar_9 1 1990

WA Verve 1 107 0.020 0.0985 0.0118 OCGT 8.10 13000 12500 537

PinjarraCogen 91 2006

WA Alinta 1 261 0.040 0.0241 0.0639 Cogeneration 2.49 25000 7200 537

PremierPowerDSM 1 2010

WA PremierPowerSales 1 31.6 0.000 0.0000 0.0000 DSR 0.00 7500 0 0

RedHill 183 1993


Rockingham 1 1900

WA AGL 1 1.607 0.000 0.0000 0.0000 LandfillGas 15.00 9590 14400 0

SouthCardup 1 1900

WA PerthEnergy 1 2.839 0.000 0.0000 0.0000 LandfillGas 15.00 9590 14400 0

SynergyDSM 1 2010

WA Synergy 1 40 0.000 0.0000 0.0000 DSR 0.00 7500 0 0

TamalaPark 1 2004


TIWEST_Cogen 63 1999

WA Verve 1 33 0.040 0.0241 0.0639 Cogeneration 2.49 25000 11600 537

Wagerup 1 1999

WA Alcoa 1 24 0.040 0.0241 0.0639 OCGT 2.49 25000 7200 591



WagerupCogen 213 2007

WA Alinta 1 352 0.040 0.0241 0.0639 Cogeneration 2.49 25000 7200 591

WaterCorpDSM 1 2010

WA WaterCorporation 1 52.5 0.000 0.0000 0.0000 DSR 0.00 7500 0 0

WestKalgoorlie 1 2006

WA Verve 1 53.175 0.010 0.0985 0.0118 OCGT 7.70 13000 11238 711

WorsleyCogen 1 2000

WA Verve 1 106 0.040 0.0241 0.0639 Cogeneration 2.49 25000 8000 537

Sources:

1. Generator capacities: NEM SOO 2010, WA IMO SOO 2010 and NT 2008-09 Power System Review

2. Capital Costs and Learning Curves: Capital costs and technology learning curves have been taken from the report „Preparation of energy market modelling data for the Energy White Paper,

supply assumptions report‟ (Sep 2010), prepared by the ACIL Tasman for AEMO/DRET.

3. Biomass and small hydro capital cost assumptions are based on our own estimates, including those that were used for prior NEM studies such as the study conducted for the National

Generators Forum.47

4. The wind cost estimate is based on the average of the small, medium and large forecasts provided in the ACIL report. The geothermal cost estimate is based on the average of the EGS and

HSA estimates.

5. Fuel Price Assumptions: Fuel prices for NEM and WA generators are derived from the ACIL Tasman report „Preparation of energy market modelling data for the Energy White Paper, supply

assumptions report‟ (Sep 2010). Average of scenarios 1 & 2 are used.. NT fuel prices are based on our own assumptions.

6. Variable and fixed operations and maintenance costs are from the ACIL Tasman report "Fuel Resource, New Entry and Generation Costs in the NEM" (Feb 09). For WA and NT the costs are

based on the average of the NEM data for similar generator type.

7. Auxiliary, heat rate and emissions rates are from the ACIL Tasman report "Fuel Resource, New Entry and Generation Costs in the NEM" (Feb 09). For WA and NT the rates are based on the

average of the NEM data for the generator type.

8. Forced outage and maintenance rates are from the NEMMCO report "2008 ANTS Consultation: Issues Paper". For WA and NT the rates are based on the average of the NEM data for the

generator type.

Notes:

1. CommDay and CommYear: Commissioning day and year;

2. RetrDay and RetrYear: Planned retirement day and year;

3. Region: NEM region;

4. Company: Genco owning the plant;

47

Charles River Associates, Analysis of Greenhouse Gas Policies for the Australian Electricity Sector, Report prepared for the National Generators Forum, 2007.


5. IRLF: Intra-regional loss factor;

6. Cap: Capacity in MW;

7. Aux: Auxiliary consumption;

8. FO Rate: Forced outage rate;

9. MaintFactor: planned maintenance factor;

10. Type: Generation and demand response technology type including sub-critical (“Sub_Cr”) and super-critical (“Sup_Cr”) coal and demand side response (“DSR”), Cogeneration, Wind, Biomass,

Landfill gas, Combined Cycle and Open Cycle Gas Turbines, Cogeneration, etc

11. VOM and FOM: Variable operation and maintenance costs (in $/MWh) and fixed operation and maintenance cost (in $/MW/year)

12. HeatRate: Plant average heat rate in MJ per MWh

13. Emissions: CO2 emission intensity in kg per MWh.