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Electricity Generation Investment Analysis
Final Report
14 April 2011
Deloitte Touche Tohmatsu
550 Bourke Street
Melbourne VIC 3000
GPO Box 78
Melbourne VIC 3001
Australia
Tel: +61 (0) 3 9671 7000
Fax: +61 (0) 3 9671 7700
www.deloitte.com.au
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
otherwise verified the accuracy or completeness of the information. We have not
contemplated the requirements or circumstances of anyone other than DRET.
The information contained in this Report is general in nature and is not intended to be applied
to anyone‟s particular circumstances. This Report may not be sufficient or appropriate for your
purposes. It may not address or reflect matters in which you may be interested or which may
be material to you.
Events may have occurred since we prepared this Report which may impact on it and its
conclusions.
No one else, apart from DRET, is entitled to rely on this Report for any purpose. We do not
accept or assume any responsibility to anyone other than DRET in respect of our work or this
Report.
Liability limited by a scheme approved under Professional Standards Legislation.
Deloitte Touche Tohmatsu
550 Bourke Street
Melbourne VIC 3000
GPO Box 78
Melbourne VIC 3001
Australia
Tel: +61 (0) 3 9671 7000
Fax: +61 (0) 3 9671 7700
www.deloitte.com.au
Deloitte: Electricity Generation Investment Analysis Page 2
© 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
Deloitte: Electricity Generation Investment Analysis Page 2
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
Deloitte: Electricity Generation Investment Analysis Page 3
Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its network of
member firms, each of which is a legally separate and independent entity. Please see www.deloitte.com/au/about for a detailed
description of the legal structure of Deloitte Touche Tohmatsu Limited and its member firms.
Liability limited by a scheme approved under Professional Standards Legislation.
© 2011 Deloitte Touche Tohmatsu
Deloitte: Electricity Generation Investment Analysis Page 4
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
Deloitte: Electricity Generation Investment Analysis Page 5
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
Deloitte: Electricity Generation Investment Analysis Page 6
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
Deloitte: Electricity Generation Investment Analysis Page 7
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.
Deloitte: Electricity Generation Investment Analysis Page 8
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.
Deloitte: Electricity Generation Investment Analysis Page 9
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.
Deloitte: Electricity Generation Investment Analysis Page 10
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
Deloitte: Electricity Generation Investment Analysis Page 11
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.
Deloitte: Electricity Generation Investment Analysis Page 12
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
Deloitte: Electricity Generation Investment Analysis Page 13
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
Co
st o
f P
olicy U
ncert
ain
ty (
$ m
illio
n) Scenario 1: Baseload 2017 - 100% RET
Scenario 2: Baseload 2020 - 100% RET
Scenario 3: Baseload 2025 - 100% RET
Deloitte: Electricity Generation Investment Analysis Page 14
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
Deloitte: Electricity Generation Investment Analysis Page 15
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
Deloitte: Electricity Generation Investment Analysis Page 16
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
Deloitte: Electricity Generation Investment Analysis Page 17
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
Deloitte: Electricity Generation Investment Analysis Page 18
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
Deloitte: Electricity Generation Investment Analysis Page 19
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.
Deloitte: Electricity Generation Investment Analysis Page 20
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
Deloitte: Electricity Generation Investment Analysis Page 21
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.
Deloitte: Electricity Generation Investment Analysis Page 22
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.
Deloitte: Electricity Generation Investment Analysis Page 23
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.
Deloitte: Electricity Generation Investment Analysis Page 24
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.
Deloitte: Electricity Generation Investment Analysis Page 25
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.
Deloitte: Electricity Generation Investment Analysis Page 26
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.
Deloitte: Electricity Generation Investment Analysis Page 27
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
Deloitte: Electricity Generation Investment Analysis Page 28
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.
Deloitte: Electricity Generation Investment Analysis Page 29
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.
Deloitte: Electricity Generation Investment Analysis Page 30
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.
Deloitte: Electricity Generation Investment Analysis Page 31
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.
Deloitte: Electricity Generation Investment Analysis Page 32
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
Deloitte: Electricity Generation Investment Analysis Page 33
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.
Deloitte: Electricity Generation Investment Analysis Page 34
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.
Deloitte: Electricity Generation Investment Analysis Page 35
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.
Deloitte: Electricity Generation Investment Analysis Page 36
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)
Deloitte: Electricity Generation Investment Analysis Page 37
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
Deloitte: Electricity Generation Investment Analysis Page 38
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?
Deloitte: Electricity Generation Investment Analysis Page 39
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.
Deloitte: Electricity Generation Investment Analysis Page 40
(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.
Deloitte: Electricity Generation Investment Analysis Page 41
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?
Deloitte: Electricity Generation Investment Analysis Page 42
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.
Deloitte: Electricity Generation Investment Analysis Page 43
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
Deloitte: Electricity Generation Investment Analysis Page 44
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
Deloitte: Electricity Generation Investment Analysis Page 45
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
Deloitte: Electricity Generation Investment Analysis Page 46
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.
Deloitte: Electricity Generation Investment Analysis Page 47
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.
Deloitte: Electricity Generation Investment Analysis Page 48
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.
0
1000
2000
3000
4000
5000
6000
Co
st o
f P
olicy U
ncert
ain
ty (
$ m
illio
n) Scenario 1: Baseload 2017 - 100% RET
Scenario 2: Baseload 2020 - 100% RET
Scenario 3: Baseload 2025 - 100% RET
Deloitte: Electricity Generation Investment Analysis Page 49
Figure 10: Cost of Policy Uncertainty: Average cost in (Real 2010) $ per MWh
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
Co
st o
f P
olicy U
ncert
ain
ty (
$/M
Wh
) Scenario 1: Baseload 2017 - 100% RET
Scenario 2: Baseload 2020 - 100% RET
Scenario 3: Baseload 2025 - 100% RET
Deloitte: Electricity Generation Investment Analysis Page 50
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
Elicit views on financing baseload generation investment
10 Westpac National Lender institution
Elicit views on financing baseload generation investment
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
Deloitte: Electricity Generation Investment Analysis Page 51
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?
Deloitte: Electricity Generation Investment Analysis Page 52
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?
Deloitte: Electricity Generation Investment Analysis Page 53
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 $90 per MWh;
We do not consider any reserve requirement or any other side constraint such as RET etc in
this example.
Table 7 shows the optimal generation, total system cost (or, resource cost) and prices for the
system for two cases with and without considering the CCGT investment.
Deloitte: Electricity Generation Investment Analysis Page 54
Table 7: Optimal Generation (MW), Resource Costs ($ 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:
Deloitte: Electricity Generation Investment Analysis Page 55
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.
Deloitte: Electricity Generation Investment Analysis Page 56
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
8
9
10
11
12
13
14
0 5000 10000 15000
De
man
d (
GW
)
Half Hour
LDC
Blocks
Deloitte: Electricity Generation Investment Analysis Page 57
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
0
20000
40000
60000
80000
100000
120000
140000
2000 2005 2010 2015 2020 2025 2030
De
man
d (
GW
h)
NSW
QLD
SA
TAS
VIC
WA
NT
Historic SOO Forecast ABARE Forecast
0
5000
10000
15000
20000
25000
2000 2005 2010 2015 2020 2025 2030
Pe
ak D
em
and
(M
W) NSW
QLD
SA
TAS
VIC
WA
NT
Historic SOO Forecast ABARE Forecast
Deloitte: Electricity Generation Investment Analysis Page 58
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.
0
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) IDGCC
IDGCC CCS
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IGCC CCS
Brown Coal
Black Coal
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OCGT
Deloitte: Electricity Generation Investment Analysis Page 59
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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Deloitte: Electricity Generation Investment Analysis Page 60
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
Deloitte: Electricity Generation Investment Analysis Page 61
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
NSW SnowyHydroLimited 0.9716 60 0.000 0.0444 0.0411 Hydro 0.00 27137 1000 0.0
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
NSW SnowyHydroLimited 1.0092 1800 0.000 0.0444 0.0411 Hydro 0.00 27137 1000 0.0
Upptumut 1 1959
NSW SnowyHydroLimited 0.9768 616 0.000 0.0444 0.0411 Hydro 0.00 27137 1000 0.0
Uranquinty 1 2009
NSW WamboPower 0.9406 652 0.030 0.1000 0.0137 OCGT 10.10 13000 11250 736.9
ValesPt 1 1978
NSW DeltaElectricity 0.9854 1320 0.046 0.0384 0.0767 Sub_Cr_BlkCoal 1.19 49000 10169 1001.7
Wallerawang 1 1976
NSW DeltaElectricity 0.9718 1000 0.073 0.0384 0.0767 Sub_Cr_BlkCoal 1.32 52000 10876 1045.2
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
Deloitte: Electricity Generation Investment Analysis Page 62
Generator Comm Day Comm Year Retr Day Retr Year Region Company IRLF Cap Aux FO Rate Maint Factor Type VOM FOM Heat Rate Emissions
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
Deloitte: Electricity Generation Investment Analysis Page 63
Generator Comm Day Comm Year Retr Day Retr Year Region Company IRLF Cap Aux FO Rate Maint Factor Type VOM FOM Heat Rate Emissions
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
SA AGLHydroPartnership 0.9763 58 0.000 0.0160 0.0500 Wind 0.00 40000 1000 0.0
HalletWind4 121 2011
SA AGLHydroPartnership 0.9693 107 0.000 0.0160 0.0500 Wind 0.00 48300 1000 0.0
HalletWind5 336 2011
SA AGLHydroPartnership 0.9693 43 0.000 0.0160 0.0500 Wind 0.00 48300 1000 0.0
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
SA SynergenPowerPtyLtd 0.9819 79 0.030 0.1000 0.0110 OCGT 9.61 13000 12857 898.7
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
SA OriginEnergyElectricityLimited 1.0000 207 0.050 0.1000 0.0110 OCGT 9.61 13000 11250 786.4
Deloitte: Electricity Generation Investment Analysis Page 64
Generator Comm Day Comm Year Retr Day Retr Year Region Company IRLF Cap Aux FO Rate Maint Factor Type VOM FOM Heat Rate Emissions
Quarantine6 1 2012
SA OriginEnergyElectricityLimited 0.9693 125 0.010 0.1000 0.0110 OCGT 7.70 13000 11238 811.7
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
SA SynergenPowerPtyLtd 0.9497 59 0.030 0.1000 0.0110 OCGT 9.61 13000 13846 1013.5
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
VIC AGLHydroPartnership 0.9964 76 0.000 0.0444 0.0447 Hydro 0.00 14227 1000 0.0
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
Deloitte: Electricity Generation Investment Analysis Page 65
Generator Comm Day Comm Year Retr Day Retr Year Region Company IRLF Cap Aux FO Rate Maint Factor Type VOM FOM Heat Rate Emissions
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
VIC AGLHydroPartnership 0.9912 160 0.000 0.0444 0.0447 Hydro 0.00 14227 1000 0.0
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
VIC AGLHydroPartnership 1.0073 69 0.000 0.0444 0.0447 Hydro 0.00 14228 1000 0.0
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
Deloitte: Electricity Generation Investment Analysis Page 66
Generator Comm Day Comm Year Retr Day Retr Year Region Company IRLF Cap Aux FO Rate Maint Factor Type VOM FOM Heat Rate Emissions
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
WA LandfillGasAndPower 1 1.3 0.000 0.0000 0.0000 LandfillGas 15.00 9590 14400 0
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
Kwinana_3 1 1972 336 2008 WA Verve 1 120 0.022 0.0503 0.0484 Steam Turbine
2.31 28692 11000 537
Kwinana_4 1 1972 336 2008 WA Verve 1 120 0.022 0.0503 0.0484 Steam Turbine
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
WA Verve 1 16.925 0.000 0.0243 0.0123 Gas_Diesel 2.90 9590 15500 537
Kwinana_GT2 1 2011
WA Verve 1 92.156 0.000 0.0243 0.0123 Gas_Diesel 2.90 9590 15500 537
Kwinana_GT3 1 2011
WA Verve 1 92.156 0.000 0.0243 0.0123 Gas_Diesel 2.90 9590 15500 537
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_2 1 1966 91 2007 WA Verve 1 60 0.063 0.0384 0.0584 Sub_Cr_BlkCoal 1.24 51083 11000 925.8
Muja_3 1 1966 91 2007 WA Verve 1 60 0.063 0.0384 0.0584 Sub_Cr_BlkCoal 1.24 51083 10400 925.8
Muja_4 1 1966 91 2007 WA Verve 1 60 0.063 0.0384 0.0584 Sub_Cr_BlkCoal 1.24 51083 10400 925.8
Muja_5 1 1981
WA Verve 1 185 0.063 0.0384 0.0584 Sub_Cr_BlkCoal 1.24 51083 11000 925.8
Deloitte: Electricity Generation Investment Analysis Page 67
Generator Comm Day Comm Year Retr Day Retr Year Region Company IRLF Cap Aux FO Rate Maint Factor Type VOM FOM Heat Rate Emissions
Muja_6 1 1981
WA Verve 1 185 0.063 0.0384 0.0584 Sub_Cr_BlkCoal 1.24 51083 11000 925.8
Muja_7 1 1981
WA Verve 1 211 0.063 0.0384 0.0584 Sub_Cr_BlkCoal 1.24 51083 10400 925.8
Muja_8 1 1981
WA Verve 1 211 0.063 0.0384 0.0584 Sub_Cr_BlkCoal 1.24 51083 10400 925.8
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
WA Verve 1 32.15 0.000 0.0243 0.0123 Gas_Diesel 2.90 9590 13300 537
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
WA Verve 1 31.703 0.000 0.0243 0.0123 Gas_Diesel 2.90 9590 13300 537
Pinjar_3 1 1990
WA Verve 1 37 0.000 0.0243 0.0123 Gas_Diesel 2.90 9590 13300 537
Pinjar_4 1 1990
WA Verve 1 37 0.000 0.0243 0.0123 Gas_Diesel 2.90 9590 13300 537
Pinjar_5 1 1990
WA Verve 1 37 0.000 0.0243 0.0123 Gas_Diesel 2.90 9590 13300 537
Pinjar_7 1 1990
WA Verve 1 37 0.000 0.0243 0.0123 Gas_Diesel 2.90 9590 13300 537
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
WA LandfillGasAndPower 1 2.399 0.000 0.0000 0.0000 LandfillGas 15.00 9590 14400 0
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
WA LandfillGasAndPower 1 3.386 0.000 0.0000 0.0000 LandfillGas 15.00 9590 14400 0
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
Deloitte: Electricity Generation Investment Analysis Page 68
Generator Comm Day Comm Year Retr Day Retr Year Region Company IRLF Cap Aux FO Rate Maint Factor Type VOM FOM Heat Rate Emissions
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.
Deloitte: Electricity Generation Investment Analysis Page 69
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.