Empirical Analysis of Market Power and Wind Power in the ... · THE UNIVERSITY OF NEW SOUTH WALES...

THE UNIVERSITY OF NEW SOUTH WALES

SCHOOL OF ECONOMICS

Empirical Analysis of MarketPower and Wind Power in theAustralian National Electricity

Market

Samuel Robert Forrest

z3185734

Bachelor of Engineering (Mechanical) / Bachelor of Commerce

(Financial Economics)

22 October 2012

Dr V. Panchenko

Originality Statement

I, Samuel Forrest, hereby declare that this submission is my own work, which, to

the best of my knowledge, does not contain any material that has been published

previously by another author, exept where cited and acknowledged in this thesis.

Signed...........................................................................Date..........................

2

Acknowledgments

I would like to thank Dr Valentyn Panchenko for his excellent guidance in

undertaking this ambitious project.

I would also like to thank Dr Iain MacGill for his fantastic insights and input.

A thank you goes to Brett Watson, Michelle Hillier and Andy Varvel for being great

�atmates. An additional thanks goes to Brett for his editing expertise.

3

Contents

1 Introduction 11

2 Background and Literature Review 14

2.1 Introduction to Competition in Wholesale Electricity Markets . . . . 15

2.2 De�ning Market Power in Electricity Markets . . . . . . . . . . . . . 18

2.3 The Measurement of Market Power . . . . . . . . . . . . . . . . . . . 20

2.4 Market Power in the Australian National Electricity Market . . . . . 23

2.5 Wind Power and Market Power . . . . . . . . . . . . . . . . . . . . . 25

3 The Australian National Electricity Market 27

3.1 Market Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.2 Current Assets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.3 Market Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.3.1 AGL Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.3.2 Origin Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.3.3 Infratil Energy . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.3.4 International Power . . . . . . . . . . . . . . . . . . . . . . . . 35

3.3.5 Alinta Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.3.6 TRUenergy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4

4 Model and Estimation Methodology 37

4.1 Residual demand framework for an electricity generating �rm . . . . 37

4.1.1 De�ning the residual demand curve . . . . . . . . . . . . . . . 41

4.1.2 Demand and Bid Curve Smoothing . . . . . . . . . . . . . . . 44

4.1.3 Estimating the Residual Demand Derivative . . . . . . . . . . 44

4.2 Forward contract positions and retail demand . . . . . . . . . . . . . 45

4.3 Estimating marginal costs . . . . . . . . . . . . . . . . . . . . . . . . 47

4.4 The Exercise of Market Power at the Firm Level . . . . . . . . . . . . 50

4.5 The Exercise of Market Power at the Market Level . . . . . . . . . . 52

5 Results 58

5.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

5.2 Forward Contract Pro�les . . . . . . . . . . . . . . . . . . . . . . . . 59

5.3 Marginal Cost Functions . . . . . . . . . . . . . . . . . . . . . . . . . 62

5.4 The Exercise of Market Power at the Firm Level . . . . . . . . . . . . 65

5.4.1 Daily Pro�les . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

5.4.2 The Lerner Index and Market Demand . . . . . . . . . . . . . 67

5.4.3 The Lerner Index and Wind Output . . . . . . . . . . . . . . 69

5.4.4 Wind, Demand and the Lerner Index . . . . . . . . . . . . . . 70

5.5 The Exercise of Market Power at the Market Level . . . . . . . . . . 74

5.5.1 Market power rent over time . . . . . . . . . . . . . . . . . . . 74

5.5.2 Market power, demand and wind output . . . . . . . . . . . . 76

6 Conclusion 82

6.1 Further Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

5

A Data 85

A.1 Firm generation data . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

A.2 Wind generation data . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

B Analysis Implementation 90

C Forward Contract Pro�les 92

References 98

6

List of Figures

2.1 The de�nition of Market Power Rent . . . . . . . . . . . . . . . . . . 20

3.1 The National Electricity Market (MacGill, 2010; AEMO, 2010) . . . . 28

3.2 Frequency of extreme price events across NEM regions(AER, 2011) . 30

3.3 Registered capacity by type and state in the National Electricity

Market(AER, 2011) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.4 Installed wind capacity in the NEM(Clean Energy Council, 2011) . . 32

3.5 Market shares for each region(AER, 2011) . . . . . . . . . . . . . . . 34

4.1 Market supply and demand curves . . . . . . . . . . . . . . . . . . . . 43

4.2 Supply curve broken down into �rm supply and residual supply . . . 43

4.3 Firm supply and residual demand . . . . . . . . . . . . . . . . . . . . 43

4.4 Intersection of �rm supply and residual demand with Gaussian kernel

smoothing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

4.5 Market power rent, competitive rent and aggregate marginal costs . . 54

5.1 Forward Contract Pro�les - AGL Energy . . . . . . . . . . . . . . . . 60

5.2 Forward Contract Pro�les - Alinta Energy . . . . . . . . . . . . . . . 61

5.3 Forward Contract Pro�les - International Power . . . . . . . . . . . . 61

5.4 Forward Contract Pro�les - Origin Energy . . . . . . . . . . . . . . . 62

5.5 Forward Contract Pro�les - TRUenergy . . . . . . . . . . . . . . . . . 62

7

5.6 Marginal Cost Curve - AGL Energy . . . . . . . . . . . . . . . . . . . 63

5.7 Marginal Cost Curve - Alinta . . . . . . . . . . . . . . . . . . . . . . 64

5.8 Marginal Cost Curve - International Power . . . . . . . . . . . . . . . 64

5.9 Marginal Cost Curve - Origin Energy . . . . . . . . . . . . . . . . . . 64

5.10 Marginal Cost Curve - TRUenergy . . . . . . . . . . . . . . . . . . . 65

5.11 Average Lerner Index across the day . . . . . . . . . . . . . . . . . . 67

5.12 Lerner Index and Demand . . . . . . . . . . . . . . . . . . . . . . . . 68

5.13 Lerner Index and Wind Output . . . . . . . . . . . . . . . . . . . . . 70

5.14 Revenue breakdown across the year . . . . . . . . . . . . . . . . . . . 75

5.15 Average revenue breakdown across the day . . . . . . . . . . . . . . . 76

5.16 Average revenue breakdown versus dispatch . . . . . . . . . . . . . . 77

5.17 Average revenue breakdown versus wind output . . . . . . . . . . . . 78

B.1 Analysis schematic . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

C.1 Forward Contract Pro�les - Saturday - AGL Energy . . . . . . . . . . 92

C.2 Forward Contract Pro�les - Sunday - AGL Energy . . . . . . . . . . . 93

C.3 Forward Contract Pro�les - Saturday - Alinta Energy . . . . . . . . . 93

C.4 Forward Contract Pro�les - Sunday - Alinta Energy . . . . . . . . . . 94

C.5 Forward Contract Pro�les - Saturday - International Power . . . . . . 94

C.6 Forward Contract Pro�les - Sunday - International Power . . . . . . . 95

C.7 Forward Contract Pro�les - Saturday - Origin Energy . . . . . . . . . 95

C.8 Forward Contract Pro�les - Sunday - Origin Energy . . . . . . . . . . 96

C.9 Forward Contract Pro�les - Saturday - TRUenergy . . . . . . . . . . 96

C.10 Forward Contract Pro�les - Sunday - TRUenergy . . . . . . . . . . . 97

8

List of Tables

2.1 Methods for Analysing Market Power Twomey et al. (2005, page 10) . 21

4.1 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.2 Maximum generator sizes and peaking capacity . . . . . . . . . . . . 47

5.1 Wind, Demand and Lerner Index regression analysis . . . . . . . . . . 72

5.2 Wind and dispatch regression results . . . . . . . . . . . . . . . . . . 79

A.1 AGL Energy Generators . . . . . . . . . . . . . . . . . . . . . . . . . 86

A.2 Alinta Energy Generators . . . . . . . . . . . . . . . . . . . . . . . . 86

A.3 Infratil Energy Generators . . . . . . . . . . . . . . . . . . . . . . . . 86

A.4 International Power Generators . . . . . . . . . . . . . . . . . . . . . 87

A.5 Origin Energy Generators . . . . . . . . . . . . . . . . . . . . . . . . 87

A.6 TRUenergy Generators . . . . . . . . . . . . . . . . . . . . . . . . . . 88

A.7 Wind Generators by Firm . . . . . . . . . . . . . . . . . . . . . . . . 89

A.8 SA Wind Generators . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

9

Abstract

Market power is wholesale electricity markets has been frequently blamed for

in�ating electricity prices paid by consumers in restructured electricity markets

around the world. In the transition towards the use of renewable sources for

electricity generation and the unique challenges this brings, the in�uence of

unpredictable, low marginal cost renewable sources, in particular wind, on the

exercise of market power requires a much deeper understanding. The inverse

relationship between wind output and the exercise of transient market power

exaggerates the price di�erential between wind and conventional generators and

thus creates incentives that may undermine the growth of wind and other low

marginal cost renewable fuels as well as negatively impact the pro�tability of

conventional generators. In light of this, this study utilises residual demand

analysis of actual bidding data to estimate contract positions and marginal costs

and to investigate the relationship between wind output and the exercise of market

power at both a �rm and market level. This study analyses data from the South

Australian region of the Australian National Electricity Market (NEM), a region of

particular interest due to its high level of wind output and susceptability to the

exercise of market power. The study concludes that wind output has a signi�ciant

e�ect on the exercise of market power at both a �rm and market level, however, a

reduction in the exercise of market power provides little scope for reducing the

price di�erential between wind and conventional generation.

10

Chapter 1

Introduction

The electricity production industry is facing a set of unprecedented challenges

posed by the shift towards renewable energy. As conventional fuel types, such as

coal and gas, receive less new investment and play a diminished role in meeting

electricity demand, regulatory policies and the markets designed to incentivise

investment and e�ciently allocate generation need to be constantly evaluated and

adjusted. Renewable sources have more volatile output, low marginal costs, high

�xed costs and unique technical characteristics that mean there is much work to be

done in understanding how large penetration of these technologies will e�ect

market outcomes. Of these sources, wind power in particular is experiencing

remarkable growth and is the �rst such technology to reach penetration levels that

are cause for concern for market operators and regulators.

Market power is an ongoing issue in electricity markets around the world.

Electricity markets are susceptible to the exercise of market power for a number of

reasons, e.g. inelastic demand for electricity, short run capacity constraints and

load pockets caused by transmission constraints, and therefore, the exercise of

market power is frequently blamed for in�ating wholesale prices. Traditionally,

active forward markets have provided a means for hedging risk and simultaneously

reducing the incentive to exercise market power by reducing the amount of

electricity that is bid competitively into the market. However, the increase in

vertical integration of �rms across electricity production and retailing reduces the

need for forward contracting, as these �rms are essentially hedged through their

11

Introduction

retail demand obligations. In Australia, especially in the South Australian region

of the National Electricity Market (NEM), this is reducing liquidity in contract

markets and thereby making it harder for new entrants without retail exposure to

e�ectively hedge their spot market exposure.

This paper brings together techniques for analysing market power to conduct one

of the �rst empirical studies on the impact of renewables on competition in

wholesale electricity markets. It extends previous work by incorporating the e�ect

of exogenous, low marginal cost sources of generation into market power

calculations and applies this framework to analysing the NEM. The South

Australian region of the NEM and the �rms operating in it are used as a case

study since this region has the highest penetration of wind power and the most

pressing market power concerns. Both the exercise of market power across the

NEM by �rms that operate in South Australia and the exercise of market power in

the region as a whole are analysed to provide both a �rm level and market level

perspective on the issue.

A methodology is proposed for the estimation of the hedge position of a �rm that

does not rely on knowledge of actual contract or cost data, �gures that are

notoriously hard to access due to their highly con�dential nature. The method uses

observations of output to approximate the daily hedge pro�le for a given �rm at

each point in time. From these contract estimates, the study then uses a structural

econometric approach to derive marginal cost curves for each �rm from the

assumption of pro�t maximising bidding strategies given the behaviour of other

�rms and market conditions. With these characteristics of the �rm established,

two analyses of market power are conducted. The �rst looks at the exercise of

market power of individual �rms by analysing their Lerner Index and the factors

in�uencing it, and the second uses the marginal cost estimates to estimate the

degree to which market power is exercised at a market level. The results indicate

that wind power has a signi�cant in�uence on the exercise of market power at both

a �rm and market level. In particular, the exercise of market power at a �rm level

is driven largely by wind, more so than demand, due to the di�culty �rms face in

predicting wind output at a point in time when they are making medium to long

12

Introduction

term contracting decisions. The analysis of market power at a market level �nds

that, as expected, wind output has a noticable depressing e�ect on both the

market power exercised and the level of aggregate cost the market incurs in

meeting demand. In particular, the analysis �nds that the scope for reducing the

`merit order e�ect' of wind generation, i.e. the di�erence in price received by wind

relative to the market average, through mitigation of market power is limited.

It is important to note that the identi�cation of market power rent in this analysis is

not necessarily an argument for the need for regulation in the NEM nor does it imply

that there is su�cient incentive for market entry. Due to the nature of the question at

hand, this analysis measures market power against short run marginal cost (SRMC)

rather than long run marginal cost (LRMC). LRMC is more traditionally used in

regulatory decision making processes since long run e�ciency is of most concern

to a regulator. In the long run, �rms need to earn prices above their marginal

costs in order to recoup �xed costs. Market power rent as measured against SRMC

contributes towards �rms recouping their �xed costs and therefore may not represent

ine�cient market outcomes. There is, however, a relationship between the short and

the long run which means that persistent short run e�ects will ultimately translate

into long run e�ects. Therefore, any observed e�ects on market power as de�ned

against SRMC measure will ultimately translate into long run e�ects and, equally,

a driver of market power in a short run context, if persistent, will ultimately be a

driver of market power in a long run context. Wind generation represents one such

driver of market power.

Chapter 2 will provide background on the de�nition and measurement of market

power and review the literature in regards to market power and the impact of wind

on wholesale electricity markets. Chapter 3 will provide a brief description of the

NEM. Chapter 4 will detail the theoretical underpinnings of the analysis and outline

the methodology used throughout the study. Chapter 5 will outline the results of

the analysis and Chapter 6 will conclude the study and outline possible avenues for

further work.

13

Chapter 2

Background and Literature Review

Over the past few decades, the restructuring of the electricity industry from state

owned monopolies towards deregulated and competitive markets has brought about

a number of challenges. The production of electricity in particular has been a area of

dramatic reform with the development of competitive wholesale electricity markets

and the privatisation of generation assets. The unique nature of electricity, complex

technical considerations and the need to manage environmental and social concerns

has made design, operation and regulation of wholesale electricity markets a very

complex task. Consequently, it is an area of deep interest for academic and private

sector researchers.

This chapter will provide background on wholesale electricity markets and

competition, an overview of the measurement of market power, how this relates to

Australian context and wind output. Section 2.1 will provide an introduction to

competition in wholesale electricity markets and what this means for the exercise

of market power. Section 2.2 will focus in on the de�nition of market power in a

wholesale electricity market context and some key issues in reaching a clear

de�nition. Section 2.3 outlines the various methods for measuring market power.

Section 2.4 outlines the literature around market power in the Australian context

and Section 2.5 concludes by looking at the in�uence on wind generation on

market power.

14


2.1 Introduction to Competition in Wholesale

Electricity Markets

Wholesale electricity markets are the market where electricity producers sell their

electricity to retailers and large users. Due to the non-storable nature of electricity,

the highly inelastic demand for electricity and complex transmission networks,

wholesale electricity markets are often exhibit extremely volatile prices and are

often criticised for being susceptible to the exercise of market power. In order to

manage these risks, retailers and large users enter into forward contracts to

pre-purchase their electricity from generators. Related to this are a number of

di�erent structures for wholesale electricity markets that a�ect the way �rms

contract and manage risk. Firstly, a wholesale electricity market can either have a

gross pool or a net pool for electricity. In a gross pool market, all electricity must

�ow notionally through the spot market and so o� market �nancial transfers are

made for the di�erence between the spot price and contract price. In a net pool,

however, the spot market is only for the residual electricity left over after all

bilateral trades have taken place. Another key di�erence is between energy only

and capacity payment markets. Some electricity markets only provide revenue for

generators on the actual electricity they produce, i.e. an energy only market,

whereas some markets provide both capacity payments, for having capacity that is

able to generate as well as actual electricity produced. Capacity payments re�ect

the fact that capacity has an inherent value to the market in terms of security but

economic ine�ciencies can arise when capacity payments are not correctly applied.

In order to manage the complex technical and economic factors that go into

determining which generators should be give the right to generate at any point in

time, a centralised dispatch process dictates the exact quantity and timing of the

dispatch from each generator in the market. In general, the optimal dispatch

allocation is what is known as `security constrained', meaning that generators are

dispatched in such a way that if a major transmission outage was to occur then

under the new market scenario demand would still be able to be met without

requiring the violation of any other transmission constraints.

15


In theory, with high data transparency and publicly available knowledge of market

participants, electricity markets provide an excellent environment for analysis of

competition. However, the complexity of transmission networks, the technical

characteristics of generators and the extensive use of complex derivatives makes

full analysis of market power exceptionally di�cult. As Biggar (2011) discusses,

electricity markets involve a unique form of competition and are considered to be

susceptible to market power due to a number of unique characteristics:

• Firstly, short run demand for wholesale electricity is highly price inelastic in

the short run. This is due to a number of factors. First and foremost, the

vast majority of consumers are not exposed to wholesale prices. The

majority of consumers purchase their electricity through contracts with

electricity retailers thereby eliminating their exposure to wholesale prices.

Also, and more generally, there are limited opportunities for the substitution

of electricity;

• Secondly, capacity is �xed in the short run. Generators generally have limited

ability to increase output when price is high;

• Third, generators interact with each other in a regular and ongoing way

through the dispatch process. These interactions mean opportunities exist

for tacit collusion by �rms; and

• Finally, transmission constraints mean that e�ective competition that a �rm

faces at a given point in time can be highly limited should the �rm possess

the only supply that meet demand in a certain region, or `load pocket'.

These reasons also mean that typical measures of market power that are used in

anti-trust and competition analysis, such as the HHI index, Residual Supply Index

and others, are generally less able to capture the full dynamics of the exercise of

market power in wholesale electricity markets.

Market power can be exercised through a number of means. As Twomey et al.

(2005) outlines, there are two main methods for exercising market power, economic

withholding and physical withholding. Economic withholding is where output is bid

16


into the market at a higher price than usual, meaning it will not get dispatched.

Physical withholding is limiting the amount of output bid into the market. In reality,

withholding of capacity is very hard to distinguish from a generator outage; a key

problem in the identi�cation of the exercise of market power.

The ability for a �rm to exercise market power is also heavily linked to the presence

of transmissions constraints. Transmission constraints are limits placed on electricity

transmission lines in order to ensure that the line is operating within capacity and

to ensure that security of supply is maintained if it or another line were to fail. Due

to these transmissions constraints, the competition that a given �rm faces changes

dynamically. For example, in the Australian context, the interconnectors between

regions allow for energy to �ow between di�erent demand centres to create increased

uniformity of prices across the network. However, these interconnectors have �nite

and dynamic limits that, when binding, mean that no additional electricity can

�ow between regions. Under these circumstances, regions can become isolated, thus

meaning demand must be met by supply from within the region. The competition

facing a particular �rm may be drastically reduced under these conditions, allowing

that to have unrestrained in�uence over price.

Forward contracts and hedging behaviour are also very important elements of

competition in electricity markets. In general, �rms are hedged from the spot

market in two ways; through forward contracts or other derivatives, and through

retail market participation. Retail market participation insulates the �rm from the

spot market since the same �rm is selling and buying the electricity. From a

market perspective, one advantage of hedging is that it tends to lower the spot

price for electricity. Firms will typically bid their output, especially below their

contract position, lower than they otherwise would have. This is a result that has

been proven under varying sets of assumptions; Allaz and Vila (1993) proved this

assuming Cournot competition and Green (1999) proved this assuming linear

supply functions.

Further, in times of uncertainty about the future obligations of electricity generators

forward contracting tends to diminish as retailers are less inclined to set prices when

there is expectation of price rises. For example, in Australia as noted by Biggar

17


(2011) and ACIL Tasman (2011), the current uncertainty created by the carbon

price and Carbon Pollution Reduction Scheme, as it is currently known, is reported

to be reducing the ability of generators to sell contracts into the forward markets,

thereby increasing their aggressiveness in the spot market. As ACIL Tasman (2011)

concluded this uncertainty can act to raise the incentives to �rms to exercise market

power, thus raising spot prices.

2.2 De�ning Market Power in Electricity Markets

Before attempting to analyse the exercise of market power we �rst must reach an

exact de�nition of what �market power� is in the context of electricity markets.

While this appears to be a simple task, de�ning market power in wholesale

electricity markets has historically been a source of contention. Stoft (2002) notes

the often di�erent interpretations of market power by economists and regulators

and proposes that this contributes to the somewhat vague understanding of it.

The fundamental di�erence is approaches is that economists tend to view any

deviation from the e�cient market outcomes due to price manipulation as market

power whereas regulators tend to add the additional requirement of medium to

long term pro�tability in de�ning it. This ambiguity manifests itself in the

question of whether market power should be measured against short run marginal

cost (SRMC) or long run marginal cost (LRMC). Standard economic theory says

that deviation of price from SRMC is considered an ine�cient outcome for the

market as it indicates that �rms are earning an economic pro�t. However, this

does not take into account two key elements for considering the e�ciency of energy

only wholesale markets. Firstly, �rms will not enter the market without the

expectation that they will recoup their �xed costs; which are extremely high in

electricity production. Secondly, in a capacity constrained market, prices must

deviate above SRMC for short transient periods when capacity is insu�cient to

meet demand. The pro�ts in these periods are legitimate signals of the need for

additional capacity investment. As it result of these it is often argued that a longer

term analysis of market outcomes measured against LRMC is a very important

18


element for driving policy decision making.

These two competing concepts of short term and long term views of market power

means that, frequently, a situation can exist where market power can be said to

exist and not exist simultaneously under the two de�nitions. A �rm may be having

a signi�cant short term in�uence on price levels but not be earning pro�ts in excess

of its long run costs.

While it is clear that longer term measures are more accurate in determining whether

the exercise of market power is cause for regulatory concern, an empirical problem

arises when applying these de�nitions to look into speci�c short run drivers of market

power, i.e. the level of wind generation, due to the fact that LRMC is a long term

concept it is not well de�ned in the short run. If one wants to look at the short run

impacts on market power, one must use a short run measure.

Adopted from Wolak (2009), Figure 2.1 shows one such short run measure of

market power, the concept of `market power rent'. Market power rent can be

interpreted as the di�erence in market revenue under actual circumstances and

under circumstances where all �rms bid at their SRMC. Incorporated in this

measure will be the degree to which �rms exercise market power, but it will also

capture genuine signals of a requirement for capacity investment that the market

makes. In practice it is very di�cult to distinguish between high prices due to

capacity shortages and high prices due to the withholding of capacity with the

intention to exercise market power, and both of these instances will tend to be

captured here in the measure of 'market power rent'. In this study the term

market power will be synonymous with this de�nition of market power rent and

the phrase persistent market power will be used to signify market power as

measured against LRMC that may warrant long term regulatory intervention.

Hird and Reynolds (2012) summarises this concept well,

�...overall e�cient operation actually requires generators to bid above

their short run marginal costs at least some of the time in order to cover

LRMC and thereby maintain incentives for e�cient entry. While such

conduct does involve the exercise of transitory market power, it is not, on

its own, evidence of market power that is harmful to e�ciency nor in need

19


Figure 2.1: The de�nition of Market Power Rent

of regulatory intervention given the way the NEM pricing arrangements

are structured.�

2.3 The Measurement of Market Power

Given the propensity for market power to be exercised in electricity markets,

methods for detecting it, ex post, and predicting it, ex ante, are important for

guiding policy and regulatory decision making. Due to the unique characteristics of

electricity markets it is generally considered that many of the traditional measures

of market power, such as HHI, without speci�c adjustments re�ecting market and

transmission conditions, do not yield useful results. Consequently, there are a vast

range of approaches to understanding market power. As Twomey et al. (2005)

notes, these can be broadly broken down into long term and short term analyses.

Long term approaches tend to rely on long run simulation models, structural indices

and comparisons to LRMC and are typically used to answer questions around the

existence of persistent market power in the long run. Short term analyses rely more

on analysis of intraday data to understand the more speci�c causes of market power,

20


Table 2.1: Methods for Analysing Market Power Twomey et al. (2005, page 10)

for example, the speci�c market conditions under which a particular �rm may be

overly in�uencing price. Typically, a combination of both these approaches are

required to fully understand the existence and causes of market power. The focus

of this study is the measurement of market power from a short term perspective in

order to better understand the speci�c short term drivers of it.

Residual demand analysis is a broad term used to describe analysis that involves

using market bidding data to look at the decision facing a strategic �rm

participating in a wholesale electricity market. In general, the approach can be

used to estimate contracting behaviour and cost functions and in recent times has

been used to understand the exercise of market power. The approach, or the

theory underpinning it, has been used in a number of studies to understand

competition and �rm behaviour in wholesale electricity markets.

Residual demand analysis draws from a number of areas of economic and econometric

theory. The theoretical underpinnings can be traced back to Klemperer and Meyer

(1989) who �rst characterised market equilibria with supply function bidding in

an oligopolistic market with uncertainty and Rosse (1970) who �rst looked at the

estimation of cost functions from market clearing conditions. Green and Newbery

(1992) conducted a landmark study which analysed the British Electricity Market

during its early operation and showed, using a theoretical model of a competitive �rm

bidding with supply functions, that the Nash equilibrium in this scenario implied

a high markup over marginal costs and signi�cant deadweight loss. Borenstein

and Bushnell (1998) analysed the deregulated Californian Electricity Market using

21


a static Cournot model with a competitive fringe and concluded that the level of

available hydroelectric output, as well as the elasticity of demand, had a signi�cant

in�uence on the degree to which �rms could exercise market power.

Wolak (2001, 2002, 2007) used data from the early operation of the Australian

National Electricity Market, from 1997, to answer a range of questions around cost

functions and contracting in wholesale electricity markets. The analyses all utilised

the same data set due to the di�culty in obtaining commercially sensitive forward

contract data. Wolak (2001) looks at the role of forward contracts on the bidding

behaviour of generators and empirically veri�es the result that the amount of

hedging through contracting that a �rm conducts signi�cantly a�ects their

incentive to exercise market power. Wolak (2002) outlines a methodology for

estimating the cost function of a speci�c market participant and outlines how this

process can be used to measure the exercise of market power. The main

contribution of the work was presenting the methodology and structural

econometric framework for conducting such an analysis. Wolak (2007) extends the

previous analyses to look at the the impact of contract behaviour on broader

market outcomes.

Hortascu and Puller (2008) extends the literature by providing a rigorous theoretical

framework for the bidding behaviour of electricity generators and applies this to the

Texas market. Importantly, it shows that the equivalent to the �rst order condition

from pro�t maximisation can be reached using a set of less strict assumptions,

namely that �rms aim to push price up when they are above their contract positions

and push price down when they are below and that uncertainties faced by generators

can be reduced to parallel shifts of the residual demand curve. This framework is

then applied to analysing the optimality of bidding behaviour in the Texas electricity

spot market and con�rms that �rms executed bidding strategies that were broadly

consistent with static pro�t maximisation, even more so for price setting �rms.

Turning more speci�cally to the analysis of market power, Wolfram (1999) conducted

an analysis of the duopoly market power present in the British Electricity Market

and concluded that price-cost margins were low compared to theoretical predictions,

and thus there was limited evidence for the exercise of market power. One of the

22


most extensive investigations of market power using the residual demand approach

was done by Wolak (2009). The report provides a thorough investigation of both

the incentive to exercise and exertion of market power in the New Zealand wholesale

electricity market and provides quanti�cations of the e�ects.

More recently, Bosco (2011) conducted an analysis of the Italian wholesale electricity

market by estimating cost functions for a range of �rms and used these estimates

to look at the exercise of market power by looking at the Lerner index of �rms. It

concludes that the dominant price setting �rms have the most sign�cant signi�cant

market power. The analysis is conducted on a net pool market and so care must be

taking in directly comparing results with those in the NEM.

One weakness and critique of residual demand analysis, as it has traditionally been

applied, is the fact that the impact of transmission constraints on the bidding

strategies of �rms are not explicitly considered. Xu and Baldick (2008) and Lee

and Baldick (2011) have made advances in including transmission constraints in

market power calculations, by developing a method for calculating the residual

demand derivative in the presence of transmission constraints.

2.4 Market Power in the Australian National

Electricity Market

As with many wholesale electricity markets around the world, market power has

been a signi�cant issue in the NEM since its deregulation in the early 2000's. Short

and Swan (2002) provides a general introduction and discussion of the competitive

environment in the Australian National Electricity and provides a methodology for

estimating the marginal costs and thereby analysing the presence of market power.

Marginal costs are estimated by assuming that the marginal cost function can be

approximated by the lower envelope of all supply o�ers and the study concludes that

there is behaviour consistent with the transient exercise of market power.

Residual demand analysis was �rst applied to the NEM by Wolak (2002). The

study only focused on applying the techniques to one generator rather than the entire

23


market and looked at the estimation of cost functions using a structural econometric

framework. Hu et al. (2004) conducted a descriptive analysis of market power in

the NEM and found that bidding patterns varied in a way that was consistent with

�rms restricting output and thus exercising transient market power.

More recently, Kemp et al. (2012), as part of a regulatory review of the NEM,

investigated the question of market power by looking at the relationship between

LRMC and spot price outcomes. The study estimated the LRMC for each state for

on an annual basis using a model of market entry and investment. It concluded that

there was no evidence for any long term persistence of prices above the LRMC values

obtained and thus no evidence for the persistent exercise of market power. Biggar

(2011) uses a residual demand framework to analyse the potential presence of market

power in the NEM and speci�cally looks at the South Australian and Queensland

regions. The analysis focuses on the short run comparison of market outcomes with

SRMC and concludes that there is evidence for the exercise of transient market

power in these regions of the NEM.

Further, Hird and Reynolds (2012) looked at barriers to entry present in the NEM

and how these may a�ect new investment and expansion in electricity generation.

They concluded that there is no evidence that the three mainland states of

Queensland, New South Wales and Victoria have any signi�cant barriers to entry.

Hird and Reynolds (2012), as well as Biggar (2011), note that the region within

the NEM with the highest barriers for entry and largest potential for the exercise

of market power is South Australia. This is attributed to the presence of large

vertically integrated �rms, the consequently relatively illiquid contract and futures

markets (as vertically integrated �rms have less need for entering forward

contracts) and the presence of wind generation. Consequently, the South

Australian market presents an interesting case study due to the presence of both

high wind and high potential for the exercise of market power.

24


2.5 Wind Power and Market Power

The relationship between renewable technologies and competition is a developing

area of research. Twomey (2010) conducts a theoretical analysis of how stochastic,

low cost, exogenous wind generation will systematically miss out on the higher

prices that occur as a result of the exercise of transient market power by

conventional generators. The paper, importantly, concludes that relying on short

run market power pro�t margins to incentivise new investment in generation is not

a technologically neutral policy. It will tend to favour conventional generation as

those generators will reap a larger proportion of the market power rent present in

the market.

More broadly, related analysis of the impact of speci�c low marginal cost

generation types has been conducted looking at the impact of hydroelectric

generation on market power. In particular, Borenstein and Bushnell (1998) in its

analysis of the Californian Electricity Market and Wolak (2009) in its analysis of

the New Zealand Electricity Market. Both studies concluded that due to its low

marginal cost, the presence of hydroelectric generation had a signi�cant depressing

e�ect on the exercise of market power. While having similarly low marginal costs,

the controllability of the output hydroelectric generation means that its market

impacts are profoundly di�erent to that of wind generation.

A related, more developed area of research around wind generation is around the

relationship between wind and wholesale spot prices, known as the `merit order

e�ect' of wind generation, and the associated impact of conventional plant

dispatch. The merit order e�ect is the systematic displacement of higher cost

generation through the market dispatch process due to its very low marginal costs.

The result is that there is an inverse relationship between wind generation and

spot prices and dispatch of conventional generation types. This has a number of

implications, including increased price volatility, negative operational impacts on

conventional generation through the requirement for increased output �uctuations,

and a reduction in the average prices received by wind generators. In the short run

this reduction in price due to wind represents a saving to the market, however, in

the long run this is expected to be negated somewhat by costs associated with an

25


increased need for �exible high cost generation and the increased operational costs

of conventional generators as the need for their output decreases (Green and

Vasilakos, 2010). A number of studies have investigated and quanti�ed this e�ect.

Sensfus et al. (2007) provides a detailed description of the e�ect and looked at its

impact in the German Electricity Market. Woo et al. (2011) conducted an

econometric analysis of market data to quantify the e�ect in the ERCOT market

in Texas, USA. More broadly, Green and Vasilakos (2010) conducted market

simulations of the impact of wind generation on fossil fuel investment and IEA

Task 25 (2011) provides a detailed analysis of the interrelationship between

operational and market impacts of large amounts of wind generation.

Turning to the relationship of wind generation with market power, the same logic

behind the in�uence of wind on generator dispatch can be extended to the in�uence

of wind on market power. The displacement of conventional generation by wind is

merely a result of the reduced demand for conventional generation as a result of wind

output. In addition, a natural implication of the impact of wind on price is that the

ability for �rms to earn market power rent or super normal pro�t is reduced.

There is a more work to be done in looking at the e�ect of low marginal cost,

highly variable renewable sources on competition in wholesale electricity markets.

In particular, with the exercise of market power being a common problem in

wholesale markets and the ongoing implementation of policies promoting the use of

renewable sources, having a detailed understanding of the relationship between the

two is of paramount importance. As markets continue to be forced to

accommodate renewable resources it is vital that policy makers have a sound

understanding of how these factors will impact on, the following, sometimes

competing, policy objectives; maintaining low electricity prices, incentivising the

expansion of renewable sources and ensuring the ongoing pro�tability of existing

conventional plants.

26

Chapter 3

The Australian National Electricity

Market

As part of the deregulation of the Australian electricity industry, the National

Electricity Market (NEM) commenced formal operation in 1998. The NEM is the

largest electricity market in Australia, covering the states of New South Wales,

Queensland, Victoria, South Australia and as of 2005, Tasmania. Each of the

states are treated as separate regions connected by interconnectors. The network is

the longest in the world stretching from Port Lincoln in South Australia to Port

Douglas in Queensland, a distance of around 5000km. Figure 3.1 shows the

geographic coverage of the NEM as well as the �ve regions into which it is split.

The NEM is unique among the world's electricity markets for a number of reasons.

It has a large geographic coverage which means a requirement for large amounts of

transmission infrastructure. The maintenance and security of such vast amounts of

infrastructure means that transmission outages are a more common occurrence than

in other markets around the world. Another unique characteristic is the dominance

of coal �re generation. Due to the abundance of coal as a fuel source in Australia, coal

is the most used fuel in electricity generation. As a result of the in�exibility of coal

�red generation, this has a number of rami�cations, particularly in an environnment

of expanding renewable energy generation.

The NEM provides an excellent case study for the analysis of wholesale electricity

27

The Australian National Electricity Market

Figure 3.1: The National Electricity Market (MacGill, 2010; AEMO, 2010)

markets due to its structure and transparency. It is classi�ed as an energy only

market, in which generators are only paid for the electricity the produce and the

NEM has a gross pool, whereby all electricity must notionally �ow through the

market. The combination of these two characteristics means that a fuller picture

of the competitive behaviour of �rms is able to gained from the NEM compared to

other markets.

While as a wholesale market the NEM has been generally considered a success

(Outhred, 2000), it has faced its own share of problems. In particular, in the recent

past the e�ectiveness of competition in the South Australian region has been brought

into question due to historically high prices in this region, the in�uence of wind,

limited interconnection and the dominant market position of �rms in the region.

3.1 Market Design

The NEM is operated by the Australian Energy Market Operator (AEMO). The

AEMO was established in 2009 to manage the NEM and gas markets, replacing

28


the electricity functions of the National Electricity Market Management Company

(NEMMCO) and the planning responsibilities of the Electricity Supply Industry

Planning Council (ESIPC). The AEMO governs the operations of the NEM

according to a comprehensive set of market rules.

The NEM has undergone sign�ciant change over the last decade. In 2003 the

Ministerial Council for Energy made recommendations for reform of the electricity

market (Australian Ministerial Council on Energy, 2003). The Council recognised

that �e�ective operation of an open and competitive national energy market

contributes to improved economic and environmental performance, and delivers

bene�ts to households, small businesses and industry, including in regional areas�.

The Council made recommendations for the establishment of the Australian

Energy Market Commission (AEMC) and the Australian Energy Regulator (AER)

as independent bodies for developing market rules and regulating them e�ectively.

The wholesale spot market is where the trading of electricity occurs through the real

time matching of supply and demand by a centralised dispatch system. Generators

o�er their electricty supply by placing ten dispatch bids - an o�er to supply a

certain quantity of electricity at a particular price - every �ve minutes. From the

bids, the AEMO determines which generators will be required to dispatch. Broadly

speaking, dispatch o�ers are ordered in ascending order of price and are chosen

to dispatch following that order subject to transmission and security constraints.

This approach means that, subject to the aforementioned constraints, the least cost

generation is dispatched �rst. The spot market price at each �ve minute interval is

determined by the bid of the last generator to be dispatched in a particular region.

All generators generating at that time receive this price. The six �ve-minute prices

are then averaged to determine the price for each 30 minute interval, which is the

price that is then received by the generators.

Price spikes are an important characteristic of electricity markets. In the NEM,

prices are capped at a level of $12500/MWh and have a �oor at -$1000/MWh for

each �ve minute interval. This range is high relative to other markets around the

world and so prices in the NEM tend to have some of the highest price volatilities.

South Australia generally has the highest number of price intervals above $5000, as

29


Figure 3.2: Frequency of extreme price events across NEM regions(AER, 2011)

shown in Figure 3.2. This high price volatility is driven by a number of factors, one

of which is the high installed wind capacity.

In the NEM generators are either classi�ed by the AEMO as either scheduled, non-

scheduled or semi-scheduled. Scheduled Generators are market particpants that are

subject to the centralised bidding and dispatch process. Non-scheduled Generators

do not participate in the centralised dispatch process and are treated as a negative

demand. Semi-scheduled generation is the same as scheduled generation however

it is subject to additional conditions, i.e. output can be resticted under certain

circumstances.

3.2 Current Assets

The NEM utilises a diverse range of technologies in its generation portfolio. In

Australia, it is estimated that 36.9% of emissions come from electricity generation

(DoCC, 2009). Driving this high proportion is the prevalence of coal �red generation

in the NEM. According to AER (2011), 58% of registered generation capacity is

30


black or brown coal however this capacity accounts for approximately 81% of output.

Queensland and New South Wales are dominated by black coal due to the abundance

of the resource, particularly in the Bowen Basin in Qld and the Hunter Valley in

New South Wales. In Victoria and South Australia, the majority of coal comes in

the form of brown coal, a lower grade, more polluting form of the material. Victoria

derives a more sign�ciant portion of its generation from this source, largely from the

Latrobe Valley, east of Melbourne.

In addition, natural gas represents approximately 21% of generation capacity but

due to the peaking and intermediate nature of natural gas plants, it only supplies

approximately 10% of output. All regions have a sign�ciant portion of their

generation from peaking gas turbines. New South Wales and Victoria have a lower

share due to the �exible hydroelectric power coming from the Snowy Mountain

Hydro-electric scheme and South Australia has a higher share due to the

prevalance of intermediate gas-�red boiler type generators.

Wind has relatively low exposure in the NEM as a whole with approximately 3% of

capacity and 2% of output, though it is experiencing signi�cant growth across the

regions (see Figure 3.4). This capacity is concentrated in South Australia and to

a lesser extent Victoria meaning that wind penetration is having noticable e�ects

in these regions. In terms of installed capacity South Australia and Victoria have

the largest, with approximately 1000MW installed capacity in South Australia and

430MW in Victoria.

3.3 Market Participants

While there are approximately 70 registered particpants in the NEM, the market

is dominated by a number of large �rms. Figure 3.5 shows the market share for

the generators in each region. South Australia is dominated by AGL Energy, with

a market share of 34%. This has been a cause for concern for regulators due to

the occurence of signi�cant high prices in the region, which some attribute to the

bidding behaviour of the �rm. The following sections provide a brief overview of

each �rm operating in the South Australian region. In�gen is not covered in detail

31


Figure 3.3: Registered capacity by type and state in the National ElectricityMarket(AER, 2011)

Figure 3.4: Installed wind capacity in the NEM(Clean Energy Council, 2011)

32


in this study since the �rm only has wind assets which are assumed to not bid

competitively.

3.3.1 AGL Energy

AGL is large vertically integrated �rm operating across the gas and electricity

markets across Australia. AGL is notable for its sign�ciant presence in South

Australia, where it is generally acknowledged that their behaviour, particularly

from 2007 to 2009 resulted in in�ated prices in the region. In South Australia AGL

owns the Torrens Island �eet of large gas �red steam turbine which, with a

capacity of 1280MW, make up a sign�ciant portion of the total installed capacity

in South Australia. The �rm also has a notable presence in renewable energy,

owning the wind farms of Hallett 1, Hallett 2, North Brown Hill, The Blu� and

Wattle Point in South Australia and Macarthur and Oaklands Hill in Victoria.

The combined capacity of these is over 900MW. Macarthur Wind Farm, which has

a capacity of over 400MW, has only recently commenced operation and so did not

produce any output in the sample period for this study.

During the analysis period AGL had a 32.5% stake in Loy Yang A. It is assumed

that the �rm did not have bidding control over Loy Yang A during this time. In

June 2012 AGL made a full acquisition of Loy Yang A.

3.3.2 Origin Energy

Origin Energy has wholesale and retail electricity and gas interests across the

Australian market. Gas �red generation forms a large part of the �rms capacity

which is in line with the �rm's large presence in gas production. In terms of wind

generation, Origin Energy owns Cullerin Range Wind Farm (NSW), Lake Bonney

Wind Farm (SA) and Wonthaggi Wind Farm (VIC), which together amount to a

total capacity of approximately 120MW.

33


Figure 3.5: Market shares for each region(AER, 2011)

34


3.3.3 Infratil Energy

Infratil Energy owns two gas �red generators as part of Angaston Power Station with

a total capacity of around 100MW. These generators are primarily used as a hedge

for Infratil Energy's much larger retail arm, Lumo Energy. Due to the high cost

and the peaking nature of their generators, the �rm's particpation in the wholesale

market is limited.

3.3.4 International Power

International Power owns the coal �red generators Loy Yang B and Hazelwood, the

gas �red generator, Pelican Point, and the Synergen Gas �red turbines. International

Power has a presence in the retail market under the name of Simply Energy. In terms

of wind generation the �rm owns Canunda Wind Farm in South Australia though

the generator is a non-market generator (it is registered with the market operator

but does enter into the market dispatch process).

3.3.5 Alinta Energy

Alinta is a medium sized vertically integrated �rm. It owns the brown coal �red

power plants of Northern Power Station and Playford B Power Station, both based

in South Australia, and the gas �red Baermar Power Station located in Queensland.

Its retail business is primarily in Western Australia, but it has a number of electricity

customers in South Australia and Victoria.

3.3.6 TRUenergy

TRUenergy, as of October 2012, has acquired the retail interests of EnergyAustralia

in NSW and has adopted the brand, EnergyAustralia, across its entire business.

TRUenergy is the owner of a range of gas and coal �red plants, namely Yallourn W

Power Station (VIC) and Hallet Power Station (SA) and Tallawarra Power Station

(NSW). It also has gentrader rights (the control of bidding) for the Wallerwang �C�

Power Station. Regarding wind generation, the �rm owns Cathedral Rocks Wind

35


Farm and Waterloo Wind Farm, both located in South Australia representing a total

capacity of approximately 180MW.

36

Chapter 4

Model and Estimation Methodology

This section outlines the theoretical underpinnings for the analysis and the

methodology used. It is broken down into �ve sections. Section 4.1 provides a

formal introduction to the electricity production and contracting decisions faced by

an electricity generating �rm and residual demand analysis. Section 4.2 outlines

the methodology used for estimating forward contract position values. Section 4.3

outlines the method and theory behind the estimation of �rm level marginal cost

functions. Section 4.4 continues this by describing the use of the inverse elasticity

of residual demand as an index of a �rm's incentive to exercise market power.

Finally, Section 4.5 outlines the theory behind, and estimation methodology for, an

index measuring the exercise of market power and the expected in�uence of wind

power on the index.

4.1 Residual demand framework for an electricity

generating �rm

Modern energy companies are complex. Beyond the complicated set of technical,

regulatory and environmental issues, �rms often have interests in multiple

sub-sectors and along the supply chain. This means that decision making in

certain areas of the company is often driven by considerations from involvements

in many other areas. In this analysis we use a model which is an abstraction of the

37


reality facing an energy company to focus on the electricity generation decision.

We consider a vertically integrated �rm, that is, a �rm with interests in both

production and retail, that makes decisions around its wholesale electricity market

behaviour given a hedge position, a cost function and expectations about market

conditions. To understand the decision facing such a �rm we turn to a model of

pro�t from electricity generating, adapted from those used by Wolak (2001),

Wolak (2002), Hortascu and Puller (2008) and Bosco (2011). The �rm sells its

output through the spot market and forward contracts and, depending on whether

the �rm is vertically integrated, uses its output to meet retail demand obligations.

In this simpli�ed model a �rm earns revenue through its participation in these

three markets. A summary of the notation used is outlined in Table 4.1. The

pro�t equation for a �rm can be written as

π = DR(p)p− C(DR(p)) +QC(PC − p) +QR(PR − p)

where

DR(p) = DM − SR(p)

The residual supply curve, SR(p), is contructed from all bids submitted by �rms

other than the �rm of concern. The residual demand is created by subtracting the

residual supply curve from an assumed perfectly inelastic market demand curve.

Therefore, in simple terms the residual demand traces out the path of prices that

the �rm would recieve for each level of output. A more detailed description of the

residual demand curve and its de�nition is provided in Section 4.1.1.

Let us now assume that the �rm performs a static pro�t maximisation in each period

of market operation. The objective of the �rm is therefore to solve the following

optimisation problem

maxπ = maxDR(p)p− C(DR(p)) +QC(PC − p) +QR(PR − p)

38


Symbol Variableπ Pro�tDM Market demandDR(p) Residual demandSM(p) Market supplySR(p) Residual supplyp Priceq Quantity

C(·) Cost functionQC Quantity contractedPC Contract priceQR Retail obligationPR Retail priceQT Total hedge

Table 4.1: Notation

subject to the �rms capacity contraints. Taking the �rst order condition with respect

to price gives

∂π∂p

= D′R(p)(p−MC) +DR(p)−QC −QR (4.1)

WhereMC = C ′(DR(p)). Assuming pro�t maximisation and setting ∂π∂p

= 0, we get

D′R(p)(p−MC) +DR(p)− (QC +QR) = 0 (4.2)

which solving for the price-cost margin gives the equation

p−MC =QT −DR(p)

D′R(p)(4.3)

where

QT = QC +QR

From Equation 4.3 we can see that p =MC will occur under two conditions: if the

�rm contracts exactly its level of output, i.e. QT = DR(p); or if the �rms faces a

perfectly elastic residual demand curve, i.e. 1D′

R(p)= 0. Also, since D′R(p) < 0, for

39


a �xed slope of the residual demand curve, the price-cost margin increases as the

amount of contracted output decreases. A peculiar feature of electricity contracting

that the model highlights is the fact that if a �rm has contracted more than its

output then it has an incentive to drive price down below its marginal cost. This

is a result of the fact that under those circumstances the �rm becomes a net buyer

and must enter the market to purchase electricity to meet its contract.

At this point it is also prudent to discuss the assumption of pro�t maximisation as

made here. There are a number of factors that need to be addressed in order to

understand the inherent limitation in this approach. Firstly, the fact that �rms are

forced to bid in price-quantity pairs rather than a continuous supply function means

that it is highly unlikely that bid function of a �rm accurately re�ects their optimal

bid function, even if the �rm has perfect information around market conditions.

Also, the theoretical framework is unlikely to be capturing all elements of the �rms

revenues, costs and risks. The analysis assumes that the �rm is acting in the

electricity wholesale and retail markets alone and does not take into account

ancillary services revenues. Also, other markets a�ect the behaviour of the �rm in

these markets. The most signi�cant of these is natural gas, which in some regions

also has an active spot market that in�uences the decisions of �rms in the

electricity market. For example, if a �rm has an interest in gas production then

the occasion may arise whereby it is more pro�table to sell gas on the spot market

rather than using it to generate electricity. The interdependency among market in

the energy sector makes analysing the electricity markets in isolation a somewhat

problematic task.

Assuming we have the market clearing price and quantity values for a �rm, in the

absence of detailed knowledge of the �rm's contracting positions and costs, there

are three unknown quantities in Equation 4.2: the marginal cost value, the hedge

position and the residual demand derivative. The remainder of this Section focuses

on the calculation of the residual demand derivative, a central element of residual

demand analysis. Section 4.2 further describes the nature of hedging undertaken

by a �rm and the estimation methodology for calculating a hedge position that

incorporates both the forward contract position and retail demand obligation.

40


Section 4.3 then outlines how, with knowledge of bid data from all �rms in the

market, these two elements can be used to estimate the marginal cost function for

a given �rm.

4.1.1 De�ning the residual demand curve

The residual demand curve is a description of the competition facing a particular

�rm. In theory, the residual demand curve traces out the path of the price a �rm

would receive for its electricity at each level of output. In electricity markets, with

knowledge of bidding behaviour, getting an indication of this is far easier than in

other traditional markets where �rm behaviour is not so readily observed.

However, given the complexity of transmission networks and security constrained

power dispatch processes, having a perfect understanding of the market outcomes

associated with di�erent bidding strategies is still very di�cult. It is almost

impossible, or at least extremely computationally intensive, to fully characterise

the residual demand curve.

The largest in�uence on competition facing a �rm, aside from the level of demand,

are transmission constraints, and in the NEM, the regional interconnectors are the

most signi�cant of these transmission constraints. When a transmission constraint

is binding, a phenomenon called `regional separation' can occur, which means that

demand at a particular location can no longer be met by supply from anywhere in

the market. For example, a region that is only connected to the market by a single

interconnector can become fully separated should that interconnector reach capacity.

In particular, when the interconnector is constrained on import, meaning no more

electricity can �ow into the region, competition for an additional unit of demand

is drastically reduced as the demand must be met by supply from the region. An

additional layer of complexity is added when �rms have interests across multiple

regions where only a certain portion of the �rm's output is a�ected by a constraint.

In order to mitigate the e�ects of these complex characteristics, the approach used

in this analysis focuses on times when the competition faced by the �rm is the

most readily identi�able, i.e. when transmissions constraints are having a limited

in�uence. For approximately 80% of the time none of the interconnector constraints

41


in the NEM are binding, therefore in order to mitigate these e�ects we consider only

these time intervals. Under these conditions it is assumed that equating the supply

curve (constructed from all bids submitted to the market) with market demand, as

depicted in Figure 4.1, is a valid approximation for the dispatch process.

With the aforementioned restrictions in mind and following the methodology �rst

developed by Wolak (2001), the residual demand and bid curves are now

constructed as follows. First, bids are separated into those from the �rm of concern

and those from other �rms. The bids from the �rm of concern are then placed in

ascending order to create the �rm bid function. Similarly, a `residual supply' curve

is constructed from the bids from other �rms. This splitting of the market supply

curve is shown in Figure 4.2. The residual demand curve is then de�ned by

subtracting the residual supply curve up to the level of market demand from the

market demand curve, resulting in the curve shown in Figure 4.3. The price at the

point of intersection of these two curves is approximately equal to the price at

point of intersection of the market supply and demand curves in Figure 4.1.

Underlying this methodology is the assumption that in constructing its bid

function, a �rm has knowledge, or at least an expectation of market demand, and

the bids of other generators. Demand forecasting is a very important part of

electricity production. The market operator provides demand forecasts and �rms

produce their own internal forecasts, and so this is a reasonably valid assumption.

With regard to �rm's expectations of bidding, because the market is a repeated

interaction game, where the bidding of other �rms is observed, there is likely to be

some form of learning by a �rm about the behaviour of its rivals. This means that

the assumption that a �rm has knowledge of the bidding behaviour of competing

�rms is a valid one. There are, however, still a number of uncertainties in the

market, for example transmission constraints and generator outages that are

largely less able to be predicted, which will in�uence the shape of residual demand

curve. For simplicity, these are not explicitly incorporated into the analysis.

42


Figure 4.1: Market supply and demand curves

Figure 4.2: Supply curve broken down into �rm supply and residual supply

Figure 4.3: Firm supply and residual demand

43


4.1.2 Demand and Bid Curve Smoothing

Given the fact that the residual demand and bid curves are step functions

consisting of the accumulation of discrete bids, �nding the derivative of the curves

is problematic as it will either be zero or in�nity at all points. Therefore, some

form of procedure is required to alter the curves to ensure that a �nite gradient

can be observed. As is common practice in the analysis of electricity market

bidding (see Wolak (2002); Bosco (2011)), in this analysis Gaussian Kernel Density

Smoothing was applied to the residual demand and bid curves. For the bid curve

the formula applied was

SB(p) =K∑k=1

qkϕ(p− pkH

)

For the residual demand curve both the smoothed values and gradient are required.

The following formulae were used respectively

DR(p) =K∑k=1

qk

(1− ϕ(

p− pkH

))

D′R(p) = −K∑k=1

qkφ(p− pkH

)

where K is the number of unique prices, pk, at which bids were placed and qk is the

quantity bid at each price. ϕ is the standard normal cumulative density function

and φ is the standard normal probability density function. The degree of smoothing

is able to be controlled by varying the parameter H. Increasing the value of H

increases the degree of smoothing. Similar to Bosco (2011), a value of H = $1 was

used.

4.1.3 Estimating the Residual Demand Derivative

Rather than requiring a full simulation of the price setting process all that is needed

for this analysis is an estimate of the residual demand derivative faced by the �rm.

44


Figure 4.4: Intersection of �rm supply and residual demand with Gaussian kernelsmoothing

Using the previously de�ned residual demand curve and �rm bid function we can

now estimate the residual demand derivative by simply �nding the intersection point

of the two curves and calculating the gradient of the residual demand curve at that

point.

One signi�cant advantage of using the Gaussian kernel smoothing approach to

estimate the gradient is that we need not perform the smoothing process on the

entire curve. Utilising the fact that we expect the intersection point to be in the

vicinity of the actual market price, localised smoothed curves can be produced

around the actual value. Figure 4.4 shows an example of the intersection of these

localised smoothed bid and residual demand curves.

4.2 Forward contract positions and retail demand

Firms are hedged from spot market prices primarily through two methods, forward

contracts and retail market participation. The former involves taking out contract

with retailers or �nancial institutions to e�ectively pre-sell electricity for a

predetermined price. The latter results from the fact that �rms that participate in

both the wholesale and retail markets are acting on both sides of the spot market

and so are insulated from the market during instances when their supply and

demand in the spot market is equal.

The absence of a detailed understanding of contracting poses a problem for

45


regulators and market designers since having a full knowledge of each �rms

contracting behaviour is required to fully understand an individual �rms behaviour

and incentives. In reality knowing a �rms contract position is di�cult, �rstly, due

to a lack of transparent information, and secondly, due to the complexity of the

contracting behaviour by the �rm, which often involves not only forward

contracting but other more speci�c �nancial derivative products and physical

hedging arrangements. The importance of contract is reiterated by Wolak (2002),

�even with knowledge of a �rm's bidding behavior in a competitive

electricity market, it is di�cult, if not impossible, to determine if the

�rm is able to exercise market power without knowing the generation

unit owner's forward contract position. For a speci�c bid function and

marginal cost function, there is a portfolio of forward �nancial contracts

that can rationalize that bid function as expected pro�t-maximizing.�

The forward contract position methodology used in this analysis was arrived at

from the results of Anderson et al. (2007), the data of Wolak (2007) and ACIL

Tasman (2009) and a set of intuitive rules applied to observations of �rms outputs.

The contractable output of a �rm is assumed to be equal to the average output

pro�le for a given season of a �rms baseload and intermediate generation. This

approach has one key advantage in that by looking at observed market behaviour

rather than relying on contract positions one can approximate forward contract and

retail demand obligations jointly. In this study, the term `'forward contract' or

`hedge position' will refer to this joint hedge achieved by both contracting and retail

demand.

As noted in Anderson et al. (2007), a key consideration for a �rm's contracting

behaviour is whether it can still meet its contractual obligations if a generating unit

was to fail. Therefore, a test was performed on the pro�le to con�rm that if the

largest single generating baseload or intermediate unit was to fail then the �rm would

be able to still meet its contract requirements with its peaking and hydro generation.

This idea is summarised in Equation 4.4 and the data for the calculation is presented

46


Firm Maximum unit size (MW) Peaking capacity (MW)

AGL Energy 82 800

Alinta Energy 190 948

Infratil Energy 0 50

International Power 125 352

Origin Energy 327 972

TRUenergy 263 150*

Table 4.2: Maximum generator sizes and peaking capacity*TRUenergy has a hedge agreement with EcoGen for 936MW of capacity

in Table 4.2

QT = Average baseload output+ Average intermediate output (4.4)

+min(peaking capacity −maximum unit size, 0)

This formula was applied to each of the generating �rms for each season to account

for the di�erent daily output pro�les across seasons. Utilising this methodology, all

�rms can cover the failure of their largest plant, including TRUenergy, which despite

having insu�cient peaking and hydro capacity to cover a failure it has a physical

hedge agreement in place with EcoGen, another electricity generating �rm.

4.3 Estimating marginal costs

Utilising the estimates for the forward contract positions for each �rm we can now

estimate the marginal cost functions for each �rm using Equation 4.3. Rearranging

the equation gives

MC = p− QT−DR(p)D′

R(p)(4.5)

This formula is evaluated at each time interval for price quantity pairs. The results

of this are shown in Section 5.3.

An interesting result of the above equation is that, assuming D′R(p) < 0, at outputs

lower that the forward contract position it is in fact pro�t maximising to bid at

a price higher than the �rm's marginal cost. This is due to the fact that when

47


the �rm's output is less than the forward contract position, a �rm is required to

enter the spot market to purchase a quantity of electricity equal to their shortfall

in output. Therefore as they are purchasing electricity from the spot market it is in

their interest for the spot price to be lower.

The methodology used for estimating the marginal cost function is as follows:

1. For a given �rm;

(a) For a each time period;

i. Retrieve forward contract estimates for the �rm for that time period

in that season

ii. Retrieve price, demand, dispatch, constraint, semi-scheduled

generation values

iii. Retrieve bid data from database

iv. Split bids into those from the �rm of concern and other �rms

v. Construct localised smoothed �rm bid curve using Gaussian Kernel

Smoothing

vi. Construct localised smoothed residual demand curve and derivative

using Gaussian Kernel Smoothing

vii. Find the intersection between the bid function and residual demand

curve and calculate the residual demand derivative

viii. Estimate marginal cost value for that point using Equation 4.5

ix. Store marginal cost estimate and other parameters

(b) Repeat for each time period

2. Repeat for each �rm

A number of restrictions are placed on the marginal cost estimates to eliminate

outlying values. The fact that the residual demand curve, while smoothed, is still

derived from a step function means there is large variation in the gradient of the

curve. This is particularly problematic when the gradient approaches zero as this

will produce either extremely large positive or negative estimates (depending on

48


the relative values of contract position and �rm output). In order to counteract

this, marginal cost estimates were restricted to being in the range $0 - $200/MWh,

re�ecting an approximate range of reasonable estimates for the marginal cost.

Another problem arose due to the approximation of the dispatch process with the

equating of the supply and demand curves for the market. Such an approach

means that on occasions where transmission constraints have a material impact on

generator dispatch this process is a bad approximation. To reduce this in�uence,

data points were eliminated from the sample if the di�erence between actual �rm

output and the output estimated from equating supply an demand was greater

than 10%.

With the sample of marginal cost estimates for each �rm available, it is now possible

to estimate the marginal cost curve for each �rm. This was conducted using a

linear �t on marginal cost values across �rm output. Ideally, a second or third order

polynomial would have been used to �t the data, however, given the scarcity of points

at high and low outputs and the absence of data for output below certain points ,

these higher order polynomials tended to produce erratic estimates, particularly at

high and low output levels. While a linear �t will not capture all of the characteristics

of the marginal cost curves, the trade o� was deemed worthwhile to have more stable

estimates.

In general the expectation for the marginal cost curves is that they will have a

positive gradient and track at a level below the average price curve for output above

their average contract position and above for output below their average contract

position.

The assumption of pro�t maximisation at a �rm level made in this analysis has one

signi�cant drawback when estimating the marginal costs. The assumption of pro�t

maximisation at a �rm level means that marginal costs can only be estimated at a

�rm level. Ideally, the estimation of marginal costs would be done at a generator

level, as each generator has di�erent cost characteristics and this would allow the

�rm level estimates to re�ect the fact that at di�erent times the same level of �rm

output consists of di�erent combinations of outputs from the underlying generating

assets. However, for this to be the case, the assumption of pro�t maximisation

49


would have to be applied at a generator level, which is deemed not be a valid

assumption, especially in the simpli�ed framework used here. The complex nature

of transmission networks and locational factors in the market means �rms may make

a non-pro�t maximising output for one generator which may increase the pro�t for

another generator.

4.4 The Exercise of Market Power at the Firm Level

With a full description of the �rm's hedge behaviour and costs we can now turn

to the question of the measurement of market power. In this study we will look at

market power from two perspective, �rstly, the exercise of market power at a �rm

level in this section, and secondly, at a market level in Section 4.5. Determining the

actual exertion of market power by a particular �rm, i.e. speci�c behaviour that

has caused a rise in price, is particularly hard to identify using these large scale

techniques. In order to identify the impact of speci�c actions by �rms more detailed

case by case analysis needs to be performed. Periods where �rms may have restricted

output need to be identi�ed and then the impact of their actions assessed, which

among a range of technical in�uences such as a generator outages and transmission

constraints is di�cult. Therefore this study, as a �rst pass analysis, looks at the

questions around the exercise market power using a simplify set of assumptions.

One way we can measure market power at a �rm level is to look at the Lerner Index,

de�ned as

L =p−MC

p

From the equation for the price-cost margin derived previously, Equation 4.3, the

Lerner Index can be written as

L =QT −DR(p)

p ·D′R(p)(4.6)

Now we turn to the concept of the elasticity of residual demand. The elasticity of

residual demand can be interpreted as the percentage change in quantity in response

to a percentage change in price. Assuming elasticity is positive, in the notation used

50


here it can be expressed as

εD =∂DR(p)

∂p· p

DR(p)

=p ·D′R(p)DR(p)

Alternatively it can be expressed as an inverse as

1

εD=

DR(p)

p ·D′R(p)

Assuming that the price used in the Lerner Index is the pro�t maximising price we

can substitute this expression into Equation 4.6 to give

L = − 1

εD

DR(p)−QT

DR(p)

Therefore, the Lerner Index in the presence of contracting can be interpreted as

the inverse elasticity of residual demand adjusted for the net position of the �rm.

Further, we can de�ne the net elasticity of the residual demand curve, εN , as the

elasticity of residual demand adjusted for the presence of contracting. This can be

expressed as

εN =DR(p)

DR(p)−QT

εD

or, substituting out for the elasticity of residual demand we get

εN =p ·D′R(p)

DR(p)−QT

− 1

εN=QT −DR(p)

p ·D′R(p)

and �nally, we get the relationship

L = − 1

εN

Therefore, the Lerner Index can also be interpreted as the negative inverse of the

51


net elasticity of residual demand. A higher Lerner Index re�ects a higher ability to

in�uence the market price by changing output and a higher incentive for a �rm to

exercise market power. This index is di�erent from typical use of the Lerner Index

as it allows for �rms to be both a net seller as well as a net buyer and thus the

Lerner Index need not be bound the range of 0 to 1 as is typically the case. While

the index can still not exceed 1, negative values are now possible, which occur when

the �rm is a net buyer in the spot market. When a �rm is generating less than its

contract position it is forced to enter the market to purchase the di�erence, thus

becoming a net buyer.

Regarding the relationship of the Lerner Index with wind output, utilising theory

to provide a detailed characterisation of the relationship is di�cult. The impact

of wind output on the Lerner Index is dependent on the elasticity of the bid curve

as well as how the inverse elasticity changes with di�erent levels of demand. We

can, however, make the broad hypothesis that the higher the wind output, the less

incentive there will be to exercise market power, a result of the reduction in demand

for conventional generation. This however will vary by �rm, due to their bid curve

elasticities and variation in levels of wind capacity ownership. Another observation

that can be made is that one can expect wind to have a larger in�uence on the

elasticity of residual demand curve than does demand, a result due to wind and

demand's di�ering in�uence on the �rm's ability to calculate contract levels.

4.5 The Exercise of Market Power at the Market

Level

We now turn to the measuring of the market power exercised on a market level as

measured against SRMC. The �rst step in understanding market power at this level

is having a relevant counter-factual scenario to which the actual market behaviour

can be compared. Intuitively, the counter-factual scenario we are looking for is one

where no transient market power is exerted, i.e. when �rms bid at their marginal

cost. In this case, a �rm's bid function should be equal to their marginal cost

function and the market supply curve should be equal to the aggregated marginal

52


cost functions of each of the generators. The spot price under this scenario would

be equal to the marginal cost of the highest cost generator to be dispatched.

Adapted from Wolak (2009), Figure 4.5 shows a simpli�ed representation of this

counter-factual scenario relative to the actual market supply curve. The market

revenue at a point in time can be decomposed into three components; aggregate

cost, competitive rent and market power rent, as shown in 4.5a. The aggregate cost

is the integral of the marginal cost curve up to the level of market demand and

represents the total cost to the market of producing the electricity. The competitive

rent is a result of the single price auction used in setting the price. Every generator

receives the highest bid of the dispatched generation and therefore all dispatched

generators beside the last to be dispatched receives a price higher than its cost. The

sum of these price di�erences over the dispatched generation is de�ned as competitive

rent. Market power rent is de�ned as the di�erence between the highest marginal

cost value and the actual market price multiplied by the level of market demand. If

there is no market power rent then the bid curve and marginal cost are identical.

As outlined in Section 2.5, the systematic displacement of higher cost generation by

wind power has a direct impact on the exercise of market power for the market as a

whole. Since wind power acts as an exogenous change in the demand for conventional

generators it tends to reduce the market price as well as the marginal cost of the

marginal generator, the result being that there is an inverse relationship between

the level of wind output and the level of market power rent.

In order to create a basis for the examination of the relationships between wind,

dispatch and the various components of market revenue we now turn to a simple

theoretical model. Consider the scenario depicted in Figure 4.5a where there is no

wind supply, linear bid and marginal cost curves and a demand of DM .

The market supply functions assuming for the actual and counterfactual (no market

power scenarios) are

SM,1(p) =p− c1m1

SM,2(p) =p− c2m2

53


(a) w/o wind

(b) w/ wind

Figure 4.5: Market power rent, competitive rent and aggregate marginal costs

54


or can be written in terms of price as

p1(q) = m1q + c1

p2(q) = m2q + c2

where c1, c2 are the minimum marginal cost of a conventional generator and m1,m2

are the slopes of the linear supply curves.

If we introduce an additional supply of zero marginal cost wind, W , as shown in

Figure 4.5b, the market supply functions become

SM,1(p) =p− c1m1

+W

SM,2(p) =p− c2m2

+W

or in terms of price

p1(q) = m1(q −W ) + c1

p2(q) = m2(q −W ) + c2

We can clearly see from Figure 4.5 that with the introduction of wind there is a

reduction in market power rent and aggregate cost. The impact on competitive

rent is less obvious. Using this model we can calculate the relative movements of

these values for varying wind and demand. In doing this we must make a few

basic assumptions; �rstly, that we are operating in a closed market, i.e. supply

and demand are equal, demand is greater than wind output (D > W ≥ 0), the

slope of the actual supply curve is greater than the slope of the counter-factual

marginal cost curve, (m1 > m2 > 0) and that market power rent is weakly positive

((m1−m2)(DM −W )+ c1− c2 ≥ 0). The calculations and partial derivatives of the

three components of revenue are calculated as follows.

Market power rent (MPR)

MPR = DM (m1(DM −W ) + c1 −m2(DM −W )− c2)

55


= DM ((m1 −m2)(DM −W ) + c1 − c2)

Di�erentiating with respect to wind and dispatch gives

∂MPR

∂W= −DM(m1 −m2)

∂MPR

∂DM

= ((m1 −m2)(DM −W ) + c1 − c2) +DM(m1 −m2)

Competitive rent (CR)

CR = DM(m2(DM −W ) + c2)−DMˆ

W

(m2(q −W ) + c2)dq

= DM(m2(DM −W ) + c2)−[m2q

2

2−m2Wq + c2q

]DM

W

=m2(D

2M −W 2)

2+Wc2


∂MPR

∂W= c2 −m2W

∂MPR

∂DM

= m2DM

Aggregate cost (AC)

AC =

DMˆ

W

(m2(q −W ) + c2)dq

=

[m2q

2

2−m2Wq + c2q

]DM

W

=m2(D

2M −W 2)

2−m2WDM + c2(DM −W )


56


∂MPR

∂W= −m2(DM −W )− c2

∂MPR

∂DM

= m2(DM −W ) + c2

From the above, we can make a number of inferences around the expected relative

in�uences of wind and demand on the components of market revenue. We can see

that the expected impact of wind on MPR is negative while the impact of demand

on MPR is positive. Comparing the magnitudes of the two coe�cients we can see,

following on from the assumption of MPR ≥ 0, the impact of demand on MPR

will be greater than or equal to that of wind. The di�erence being equal to the

di�erence in prices under the market power and no market power scenarios. This is

a result of the fact that a unit of wind only decreases MPR to the extent to which

it shifts the points of intersection of bid and marginal cost curves downward on the

demand curve, whereas a shift in demand both raises the points of intersection but

also increases MPR through shifting the demand curve.

Turning to competitive rent, a comparison of the impact of wind and demand is

di�cult to compare without having estimates of c2 and m2. Using reasonable

estimates of the variables, c2 = $25, m2 = 1/200, W = 400MW and

D = 1600MW , we �nd the impact of wind to be $23/MWh and demand to be

$8/MWh. Therefore, we expect that on average wind will have a larger impact on

competitive rent than demand.

In this model, the sensitivities of aggregate cost to changes in wind and demand

are of identical magnitude but opposite sign. This is intuitive since the cost to the

market is equivalent whether second cost wind meets a portion of demand or the

demand is lower by an amount equal to the wind output. Therefore, in the empirical

results we expect the two values to have an in�uence on aggregate cost of identical

magnitude but opposite size .

57

Chapter 5

Results

To investigate the exercise of market power in the National Electricity Market four

separate analyses were conducted, of which the �rst two derive necessary information

from the market behaviour of generating �rms and the second two analyse market

power and its relationship with wind generator output. Section 5.1 outlines the data

set used in the analysis. Section 5.2 presents the results of the estimation of the

forward contract position for the 5 dominant �rms in the South Australian (SA)

region of the NEM. Section 5.3 presents the results of the estimation of marginal

cost functions for each of the �rms. Section 5.4 presents the results of the analysis

of the exercise of market power by each �rm by looking at the Lerner Index facing

each �rm and how market demand and wind output a�ect it. Section 5.5 presents

the results of the market level analysis by estimating the exercise of market power in

the SA region of the NEM and provides an analysis of the impact of wind generation

on the exercise of market power.

5.1 Data

The data for this analysis was obtained from the Australian Electricity Market

Operator 1 with some assistance from the market analysis software NemSight.2 The

1See www.aemo.com.au2See www.analytics.com.au

58

Results

analysis period is a year from 1/6/2011 00:00 to 31/5/2012 23:55 or 105,408 5-minute

intervals. The data includes:

• 5 minute price data for each region

• 5 minute demand data for each region

• 5 minute dispatch data for each generator

• 5 minute bids for all scheduled and semi-scheduled generators (~40

million/annum)

• 5 minute dispatch data for each non-scheduled generator

• Generator ownership data

• Generator fuel and technology information

More detailed information on �rm generator data and wind generation can be found

in Appendix A and an outline of the implementation of the analysis in Appendix B

5.2 Forward Contract Pro�les

Figures 5.1 to 5.5 show the forward contract positions for the �ve major �rms

operating in the South Australian region of the NEM for weekdays. The day is

broken up into 288 5-min intervals and the daily pro�les show the breakdown of

generation and forward contract estimates from 12:00am to 11:55pm. Contract

positions were also calculated for Saturday and Sunday and the results are

provided in Appendix C. Wind output has been omitted as it is assumed to be

uncontracted and not competitively bid into the market.

Re�ecting the underlying market demand pro�les, from the results it can be seen

that the forward contract positions of the �rms can change quite signi�cantly over

the day and can have quite distinctly di�erent pro�les across the seasons. One of

the most notable di�erence across the results for each �rm is the amount of non-

contracted output, i.e. output above the forward contract position. The pro�les

59

Results

of Alinta, International Power and TRUenergy show that on average their forward

contract position is approximately equal to their average output for that time of

day and season, meaning that on average they contract almost all of their output.

This is a result of the fact that the vast majority of their output consists of baseload

coal and gas �red generation. AGL Energy and Origin Energy, however, have a

signi�cant portion of their quantity uncontracted, a result of the assumption that

peaking and hydro plant are left uncontracted.

A comparison of the weekday results with the two weekend days demonstrates the

need for the serparate treatment of the three parts of the week. Overall, the demand

tends to be lower on the weekends due to fact that there is less industrial activity on

weekends. This is re�ected in the output pro�les of the �rms, in particular, AGL,

which has the most variable output. Further, the output pro�les tend to have more

pronounced `peaks' in demand, a result of the fact that a higher proportion of the

output is from residential users whose usage tends to peak in the evenings in winter

and during the middle of the day in summer.

Figure 5.1: Forward Contract Pro�les - AGL Energy

60

Results

Figure 5.2: Forward Contract Pro�les - Alinta Energy

Figure 5.3: Forward Contract Pro�les - International Power

61

Results

Figure 5.4: Forward Contract Pro�les - Origin Energy

Figure 5.5: Forward Contract Pro�les - TRUenergy

5.3 Marginal Cost Functions

Figures 5.6 to 5.10 show the estimated marginal cost curves for the �ve dominant

�rms in the South Australian region of the NEM. Linear average price curves are

provided for each �rm for comparison. The cost curves represent an average across

the entire �eet of generators for each �rm. As the graphs show, the �rms have

62

Results

varying sensitivities of their output to price. The price and outputs of AGL, Origin

Energy (Origin) and TRUenergy have a strong positive relationship, whereas for

Alinta and International Power these factors have a weaker positive relationship.

The di�erence between the marginal cost curves and price curves also varies across

the �rms. This di�erence is a result of two main factors, the average elasticity of

residual demand faced by the �rm and the amount of forward contracting relative to

average output. The tendency is for larger �rms to have a larger di�erence between

the two curves as they tend to face a more inelastic residual demand curve. This is

a result of the fact that larger �rms remove a larger portion of the supply that goes

into forming the residual supply curve, thus they are faced with less competition.

This explains the pronounced di�erences between the price and marginal cost curves

for International Power, Origin and TRUenergy. The other in�uencing factor is the

amount of forward contracting relative to average output. The �rms AGL and

Origin have the largest portions of peaking and hydro capacity which in this study

has been assumed to be uncontracted. This, therefore, explains the di�erence for

AGL. Alinta Energy has relatively small di�erence between the average price and

marginal cost curve suggesting that it faces a relatively elastic residual demand curve

and has most of its output contracted.

Figure 5.6: Marginal Cost Curve - AGL Energy

63

Results

Figure 5.7: Marginal Cost Curve - Alinta

Figure 5.8: Marginal Cost Curve - International Power

Figure 5.9: Marginal Cost Curve - Origin Energy

64

Results

Figure 5.10: Marginal Cost Curve - TRUenergy

5.4 The Exercise of Market Power at the Firm Level

In this analysis the Lerner Index is considered an index of the exercise of market

power by a �rm. As described in Section 4.4, the higher the inverse elasticity, the

higher the incentive a �rm has to exercise market power. The Lerner Index, L,

was calculated for each time interval using the actual price and quantity market

clearing values for the �rm, an estimate of the residual demand derivative and

forward contract position for the time of day and season, using the formula

L =QT −DR(p)

p ·D′R(p)

This section shows the results of the analysis in a number of ways. Section 5.4.1

shows hows the index varies across the day, Section 5.4.2 then investigates how the

index measure relates to total market demand, Section 5.4.3 looks at the relationship

between the index and wind output and Section 5.4.3 performs some basic regression

analysis to further characterise the relationships between wind, demand and the

Lerner Index.

Throughout this section, in interpreting the results it must be remembered that

estimates of the Lerner Index are taken from a subset of the dispatch intervals, where

interconnector and transmission e�ects are not deemed to be signi�cant. This means

65

Results

that than in general these residual demand elasticities will tend to underestimate

the full incentives to exercise market power than a �rm faces. This is because the

elasticities are expected to be higher when competition faced by a �rm is reduced

due to interconnector and transmission e�ects.

5.4.1 Daily Pro�les

Figures 5.11a to 5.11e show the daily pro�les of the Lerner Index for the �ve

dominant SA generators in 30-minute intervals from 12:00am to 11:30pm. Figure

5.11f provides a comparison of these pro�les. The comparison in Figure 5.11f

shows the relative pro�les of each �rm. The most pronounced feature of these

curves are the sharp peaks at around 6pm for AGL and Origin. This timing

corresponds with what is typically peak demand throughout the day and so the

pro�les predict, as is intuitive, that these �rms have the most signi�cant incentive

to exercise market power when demand is at its peak. The fact that AGL and

Origin exhibit this characteristic and the other �rms do not is largely a result of

the underlying generator technology types in their �eets. AGL and Origin have the

most signi�cant peaking and hydro generation capacity which is assumed to be

uncontracted.

66

Results

(a) AGL Energy (b) Alinta Energy

(c) International Power (d) Origin Energy

(e) TRUenergy (f) All

Figure 5.11: Average Lerner Index across the day

5.4.2 The Lerner Index and Market Demand

Figures 5.12a to 5.12f show the relationship between demand and the Lerner Index

for the �ve �rms. Figure 5.12f provides a comparison of the quadratic trend lines

for each �rm. The results show that Origin and AGL are the �rms who's Lerner

67

Results

Index is the most sensitive to demand and that the remaining �rms, while showing

some sensitivity to demand, the show a more subtle relationship.




Figure 5.12: Lerner Index and Demand

68

Results

5.4.3 The Lerner Index and Wind Output

Figures 5.13a to 5.13e show the relationships between wind output and the Lerner

Index for each of the �rms under consideration. Figure 5.13f shows a comparison of

the quadratic trend lines for each �rm. The results show that wind has a signi�cant

impact on Origin and AGL's elasticity of residual demand. When wind output is

low, Origin and AGL appear to exercise more market power.

The relationship between wind output and the Lerner Index for International Power

is a particularly interesting one, whereby it has an incentive to drive down price

when wind is high. This is due to the fact that it becomes a net buyer of electricity.

This suggests that when wind is high, International Power reduces output and thus

is increasingly short of its contract position, thereby having an incentive to drive

down price. It follows that International Power is likely operating the plants that

reduce their output most signi�cantly during periods of high wind. This suggests

that they hold expensive baseload or intermediate plant that responds signi�cantly

to wind output. This result is particularly interesting as International Power is the

owner of Hazelwood Power Station, widely regarded as one of the most ine�cient

and emissions intensive coal �red power plants in the world. Further analysis of the

particular generators of International Power that respond to wind output would be

an interesting further analysis.

69

Results




Figure 5.13: Lerner Index and Wind Output

5.4.4 Wind, Demand and the Lerner Index

Table 5.1 shows the results of the simple regressions of wind and market demand

on the Lerner Index for each �rm. For each �rm the simple linear equation was

estimated

70

Results

L = c+ αdemand+ βwind+ ε

where L is the Lerner Index, α, β, c are the regression estimators and ε is the error

term. In the presence of a very complex market and broad assumptions, the purpose

of these regressions, rather than providing de�nitive quanti�cations, is to merely

assist in describing the relative relationships.

At a broad level, information about the �rm's degree of market power can be derived

from the explanatory power of the regression equation for that �rm. The R-squared

values of the models are generally quite low, from around 0.004 to 0.06. This is

due to a number of reasons. The primary reason is a result of the stepped nature

of the residual demand curve. The smoothing algorithm alleviated the issue of zero

and in�nite gradient but there is still signi�cant variation in the gradient of the

curve. This then �ows through into the estimation of the elasticity. Further, the

methodology for the estimation of forward contract positions may not be capturing

the full variation that occurs in a �rm's contract cover. If contract cover tracked

output more closely and, therefore, there was less deviation between the two values,

this would reduce the amount of variation in elasticity estimates.

Comparing the R-squared values it can be seen that Origin and AGL have

signi�cantly higher values than the remaining �rms. This is consistent with what

has been observed in the previous sections, where these �rms showed the most

sensitivity to wind and demand. A comparison of the coe�cients of market

demand and wind in the regression shows that, as expected, Origin and AGL have

the highest sensitivities to both demand and wind. In all cases, except for

TRUenergy, the sensitivity is higher for wind than demand on a per MW basis. It

is expected that this is a result of the fact that in contracting, it is far easier to

produce a reasonable medium to long term forecast of demand for a point in time

than it is for wind. Therefore, it is easier to take into account expected demand,

that it is wind output, in making forward contract cover decisions. The fact that

TRUenergy does not exhibit this trend (if fact, the coe�cient of wind in the

71

Results

TRUenergy model is the only non-statistically signi�cant estimation) suggests that

the �rms output may not vary signi�cantly with wind output, however the

explanatory power of the model is very low and so caution needs to be taken in

making �rm conclusions.

Symbol Origin

Energy

AGL Energy International

Power

TRUenergy Alinta

Energy

R2 0.0561 0.0655 0.0122 0.0066 0.0044

SSE 87.707 59.964 70.065 69.204 36.17

Demand α -0.0067258**

(0.0001132)

-0.0044938**

(0.0000793)

-0.0011899**

(0.0000872)

-0.0018613**

(0.0000859)

-0.0005713**

(0.0000419)

Wind β 0.0117057**

(0.0010377)

0.0276082**

(0.0007121)

0.020719**

(0.0007692)

0.001231

(0.0007615)

0.0046568**

(0.0003773)

Const. c 139.422**

(2.778974)

73.63916**

(1.855482)

19.29544**

(2.110992)

44.57538**

(2.100859)

10.7942**

(1.024845)

Table 5.1: Wind, Demand and Lerner Index regression analysis* Signi�cant at the 5% level

** Signi�cant at the 1% level

In summary, the results in this Section have shown that the Lerner Index is highly

variable but clearly in�uenced by the level of market demand and wind output at

each point in time. The results show that the �rms that possess more �exible plant

have a higher incentive to exercise market power. It is hypothesised that this is due

to the fact that the output of these �rm's is more highly correlated with the values

of wind and demand. These results have provided evidence for the hypothesis that

the incentive to exercise market power is more sensitive to wind than demand. This

is driven by the fact that it harder to predict future values of wind at a certain point

in time than it is demand when making contracting decision.

The implications of these results are numerous. Firstly, the in�uence of wind

power on the market power of the �rm is asymmetric, depending on the underlying

characteristics of the �rm's generation �eet. The result being that the active

promotion of wind output by policy can be said to have asymmetrically e�ected

the competitive position of each �rm. Those with more �exible plant have had a

more signi�cant negative impact on their competitive position.

Further, the short term uncertainty in demand for conventional generation created

by varying wind output means that medium to long term forward contract decision

72

Results

making is more di�cult. Without the ability to actively hedge against the level

of wind output, this uncertainty may lead to more conservative contract coverage

to avoid the risk of falling short of contract cover and therefore having to purchase

electricity from the spot market. This reduction in contract cover relative to capacity

may in fact mean that any reduction in the incentive to exercise market power due

to the introduction of wind may be negated by a corresponding reduction in contract

cover.

Finally, regarding the incentives for new generation investment, the exercise of

market power by �rms acts to exaggerate the already present discrepancy between

the prices received by conventional generation and wind generation.

73

Results

5.5 The Exercise of Market Power at the Market

Level

This Section presents the results of an analysis of the exercise of market power in

the South Australian region of the NEM. Broadly speaking, the results show that

market power rent, as de�ned in this analysis, is present to varying degrees at most

points in time. Over the time period in question it is estimated that market revenue

consisted of 8.6% market power rent, 3.5% competitive rent and 87.9% aggregate

costs.

Before presenting the results it is important to reiterate how these results should

be correctly interpreted. The presence of market power rent as measured against

SRMC is not necessarily an indicator of the persistence of market power that is in

need of regulation. A very important concept is that it is likely that included in what

is de�ned here as market power rent is a portion of market revenue that is a signal

for new investment as a result of a genuine shortage of capacity (rather than a �rm

strategically withholding capacity). It is almost impossible to distinguish between

these two possible causes of high prices, which is a large part of the di�culty in

analysing the presence of market power in wholesale electricity markets, especially

those with an energy only structure. These signals are an important characteristic

of energy only electricity spot markets and their presence is essential for a well-

functioning market. Irrespective of the source of these high prices, however, they

are still a incentive for the entry of new generation, and so to this end, the impact

of wind on these incentives is an important relationship to understand.

5.5.1 Market power rent over time

Figures 5.14 and 5.16 show the breakdown of market revenue into the components

market power rent, competitive rent and aggregate cost in terms of weekly averages

and the average daily pro�le, respectively.

Figure 5.14 shows that levels of market power rent vary considerably across the

year. This is largely driven by the di�erent seasonal patterns. The 366 day period

74

Results

start on the 1/6/2012 and so the �rst season is Winter followed by Spring, Summer

then Autumn. The results suggest that during Winter there was a persistent level

of market power rent being earned. Spring had a number of high price events that

made for weeks where signi�cant market power rent was earned. Summer shows an

unusually low amount of market revenue and limited presence of market power rent,

as result of uncharacteristically mild temperatures. Autumn presents a similar trend

to Summer - a moderate presence of market power rent - though has less variation

in the market revenue earned.

Figure 5.14: Revenue breakdown across the year

The daily pro�le in Figure 5.17 shows how the three components of market revenue,

market power rent, competitive rent and aggregate cost, vary across the 288 5-

minute intervals from 12:00am to 11:55pm. All three components follows a similar

pattern of variation across the day with the minimum values occurring at around

4:00am (or interval 48) and the maximum values occurring during the evening peak

at around 6.30pm (or interval 222). Interestingly, the average market power rent

is almost zero at its minimum point in the early hours of the morning. The high

price e�ects that cause the few periods of very high market revenue show a fairly

random distribution across the day and interestingly, none occur in the vicinity of

the evening demand peak.

75

Results

Figure 5.15: Average revenue breakdown across the day

5.5.2 Market power, demand and wind output

Figure 5.16 shows how the three components of market revenue vary with region

dispatch in South Australia. As the results show, there is a clear increasing

relationship between the three component and region dispatch. Market power rent

shows an approximately linear trend, punctuated by a number of high market

power rent events. The small number of increased market power rent estimates at

very low dispatch is likely a result of baseload generators shutting down particular

units which means a requirement for transient periods of generation from more

�exible, higher cost generators. Competitive rent and aggregate cost both follow

similar increasing trends with competitive rent showing a more pronounced

quadratic trend.

76

Results

Figure 5.16: Average revenue breakdown versus dispatch

Figure 5.17 shows how the three components of market revenue vary with wind

output in South Australia. Market power rent exhibits a prominent inverse

relationship with wind output. Competitive rent approximately linearly increases

with wind output and aggregate cost approximately linearly decreases with wind

output.

77

Results

Figure 5.17: Average revenue breakdown versus wind output

Regression Analysis

In order to better understand the relationships between wind, demand and the

various components of market revenue we now turn to a set of simple linear regression

models. The following equation were estimated

MPR = c+ αdispatch+ βwind+ ε

CR = c+ αdispatch+ βwind+ ε

AC = c+ αdispatch+ βwind+ ε

MR = c+ αdispatch+ βwind+ ε

where MPR is market power rent, CR is competitive rent, AC is aggregate cost

and MR is market revenue, α, β, c are the regression estimators and ε is the error

term. Table 5.2 show the results of the regression analysis of wind and dispatch

on the various components of market revenue for the SA region. The table present

separate regressions of the independent variables on market power rent, competitive

rent, aggregate cost and total revenue (where total revenue equals the sum of the

78

Results

other three components).

Variable Symbol Market

power rent

Competitive

rent

Aggregate

cost

Total

revenue

R2 0.1633 0.9292 0.9923 0.8431

SSE 471.25 129.94 64.71 507.47

dispatch α 8.238788**

(0.1119924)

7.415613**

(0.030494)

30.09481**

(0.0088794)

46.05614**

(0.1206019)

wind β -7.264993**

(0.2168577)

27.54968**

(0.0594017)

-30.1383**

(0.0128345)

-9.357513**

(0.2335288)

const. c -460.2241**

(13.32317)

-120.5818**

(3.639856)

-164.343**

(0.965316)

-789.2238**

(14.34739)

Table 5.2: Wind and dispatch regression results* Signi�cant at the 5% level

** Signi�cant at the 1% level

Before intepreting the analysis it is important to address a key empirical issue

associated with analysing the exercise of market power in a particular sub-portion

of the NEM. While on a whole of market level supply (or dispatch and demand are

equal, in a particular region of a market with interconnection, they need not be

equal. This is a result of the fact that electricity can �ow in and out of a region.

The theoretical framework outlined in Section 4.5 assumes that these two values

are equal. In these circumstances, the same mathematics as used in the theoretical

framework holds when dispatch is used but not so when demand is used. However,

using dispatch gives rise to another issue, while wind output and demand are

independent (ρ ≈ −0.05) wind and dispatch are not (ρ ≈ 0.28), and therefore the

coe�cient estimates in a regression equation that uses both these variables may

contain some bias. In the case of South Australia this correlation is a result of the

fact that when wind is high, because of its low cost, South Australia tends to

generate more and export electricity into Victoria. Due to the relatively low

correlation the e�ect is expected to be minimal. None-the-less, in interpreting the

results, the emphasis should be more on the relative magnitudes and relationships

rather than on the speci�c values.

Returning to the simple theoretical model in Section 4.5 we can see that the

predicted relative magnitudes and signs hold for the regressions on the components

of market revenue. The market power rent regression suggests that both wind

79

Results

output and the level of dispatch have an e�ect of similar magnitude but of

opposite sign, which is consistent with the predictions of the theoretical model.

The competitive rent regression suggests that the amount of competitive rent in

the market is most signi�cantly a�ected by the level of wind output and to a much

lesser extent, the level of dispatch. This is a result of the fact that given wind

generation has a very low marginal cost when it is dispatched it adds almost

entirely competitive rent to the market without any addition of cost, meaning for

the same level of dispatch, competitive rent will be higher and costs lower when

there is more wind generation. The coe�cients are consistent with the theoretical

model predictions and in fact yield similar values to those found using a set of

reasonable approximations for the parameters. The aggregate cost regression

suggests that wind output and market dispatch have an almost identical but

opposite e�ect, which con�rms the hypothesis that their impact should be of equal

magnitude but opposite sign.

Interestingly, when looking at the combined impact of wind on competitive rent and

aggregate cost, the net e�ect is only a marginal decrease in market revenue. This

suggests that for an additional unit of wind, while there is a sign�ciant saving in

terms of the total costs to meet the level of demand, this is not transferred into

signi�cantly lower prices or market revenue since a large portion of the savings are

merely transformed into competitive rent. This means that end consumers are not

seeing the full bene�t of the reduction in cost needed to supply the electricity due

to the single price auction structure.

The merit order e�ect of wind generation

Using the breakdown of market revenue into its three components we can now

investigate how changes in the ability of �rms to exercise market power will e�ect

the average price that wind receives relative to other forms of generation.

Reducing this di�erential means the asymmetry for incentives to invest in the

di�erent generation types is reduced. The analysis results suggest that a 20.1%

reduction in the price di�erential between wind and the market average would be

achieved if no market power rent existed and a 10.7% reduction would be achieved

80

Results

if the market power rent was halved. In dollar terms, if market power rent was to

be halved this would mean the di�erential would move from around $4.30/MWh to

around $3.87/MWh. However, due to the fact that wind generation does in fact

bene�t from the exercise of market power through the single price auction format

the average price received, should market power rent be halved, would be around

60 cents less. Therefore, while the di�erence in price wind output receives would

be reduced so would the average price. This suggests that the scope for limiting

the merit order e�ect of wind generation is limited as it would not be in the best

interests of wind generators.

81

Chapter 6

Conclusion

This study has conducted one of the �rst empirical analyses of the impact of

renewable energy generation on competition in wholesale electricity markets. The

results provide evidence for the presence of transient market power as well as for

wind generation having a signi�cant impact on the extent of transient market

power exercised, both at a �rm and market level. These results also provide

empirical evidence for the theoretical results of Twomey (2010).

The analysis of the exercise of market power at a �rm level was conducted for each

�rm operating in the South Australian region of the NEM by looking at the Lerner

Index of each �rm. The analysis showed that there is a strong relationship between

the exercise of market power, their contract levels and their underlying generation

technologies. It found that the �rms, Origin and AGL, both had Lerner Index

values that were quite sensitive to both wind and demand, an outcome that is a

direct result of the fact that these �rms have larger proportions of uncontracted

peaking and hydro generation. The other �rms showed a marginal sensitivity to

these values. The in�uence of wind was found to be higher than that of demand

for the majority of �rms. This is expected to be a result of the inherent di�culty

for �rms in predicting the value of wind output, relative to the ability to predict

demand, when medium to long term contract cover decisions are being made. A

possible implication of this additional uncertainty in the demand for conventional

generation is that �rms that do not own signi�cant wind capacity may adopt more

conservative contracting strategies to avoid the risk of being over contracted during

82

Conclusion

times of high wind output.

The analysis of the exercise of market power in the South Australian market found

that there were clear seasonal and daily trends in the extent of market power rent

earned. Winter 2011 showed the most persistent levels of market power rent and

Summer 2011/12 showed uncharacteristically low levels of market power rent. The

Summer result is expected to be a product of the unusually mild weather during

that period. The relationship between market power rent and wind and dispatch was

inline with the expectation developed through the theoretical model. The strong

relationship between wind output and market power rent is direct empirical evidence

for the theoretical prediction of Twomey (2010).

Methodologically, a method was proposed for estimating the hedge position for

vertically integrated �rms that relies only on public data. These estimates are then

used to estimate the marginal cost functions for the �ve major �rms operating in

the South Australian region of the NEM. The cost function estimation procedure

has produced results largely consistent with other studies and inline with technical

estimates from other sources.

This study has shed light on the complex issue of how subsidised, low marginal

cost renewable generation impacts competition in wholesale electricity markets.

While the expansion of renewable energy generation is a vital part of the transition

towards a carbon constrained economy, care needs to be taken in understanding

and mitigating the potential impacts that this expansion will have on electricity

market outcomes and consequently, the prices paid by consumers in the long run.

As this study has shown, the rapid expansion of wind energy is reducing the

market power rent received by conventional generators and thus average spot

prices, which, in the short term reduce wholesale prices but in the medium term

may have the unintended consequences of accelerating the phase out of

conventional generation and the corresponding potential security of supply issues.

The expansion of renewable generation therefore needs to be managed in a way

that reaches a balance between these environmental goals and e�ective market

outcomes.

83

Conclusion

6.1 Further Work

This analysis by no means provides a de�nitive answer to the question of the

impct of market power in the National Electricity Market or the relationship with

wind output and market power. As has been discussed throughout the study, a

number of assumptions and simpli�cations have had to be made in order to create

a tractable analysis. Consequently, there is a broad scope for developing and

re�ning the methodology in order to reach a more robust set of conclusions.

The methodology for the estimation of forward contracts, while valuable in that it

only relies on public data, does not incorporate the full complexity of the hedging

activities of electricity generating �rms. With access to con�dential contracting

information, more accurate costs functions could be estimated and more accurate

conclusions could be made around �rm behaviour. The method used for calculation

of marginal cost functions also required a number of simplifying assumptions. Apart

from the need to use linear cost functions, the price setting process used to calculate

the residual demand derivative did not explicitly take into account a number of

market characteristics such as transmission constraints and network losses, rather,

in order to mitigate these e�ects, only periods of time where these were deemed

to be negligible were used in the analysis. A more accurate model of the market

dispatch process would increase the accuracy of the marginal cost estimates.

More broadly, the analysis would bene�t from advances in the measurement of

market power in electricity markets, in particular the incorporation of transmission

constraints in the use of the residual demand approach. Bringing these advances

to analysing the impact of renewables would enable valuable insight into the role of

transmission constraints in the relationship between wind output and the exercise of

market power. Such an analysis would be particularly valuable in the context of the

South Australian region of the NEM where transmission constraints are frequently

blamed for exaggerating the exercise of market power by �rms in the region.

84

Appendix A

Data

A.1 Firm generation data

To analyse the data at a �rm level, the output from the generators owned by each

�rm had to be collated. Tables A.1 to A.6 show the generators assumed to be under

the control of each �rm. Some ambiguity can arise when a �rm has partial ownership

of a generator, for example during the analysis period Loy Yang A was 37.5% owned

by AGL Energy. Without direct knowledge of how bidding is conducted for such

generators it was assumed that a �rm controlled the bidding when it had a majority

ownership stake in the generator.

85

Data

DUID Capacity Fuel

TORRA1 120 Natural Gas




TORRB1 210 Natural Gas




OAKEY1 141 Natural Gas

OAKEY2 141 Natural Gas

YABULU 160 Natural Gas

YABULU2 82 Natural Gas

AGLSOM 160 Natural Gas

DARTM1 150 Hydro

EILDON1 60 Hydro

EILDON2 60 Hydro

MCKAY1 300 Hydro

MCKAY2 300 Hydro

WKIEWA1 31 Hydro

WKIEWA2 31 Hydro

Table A.1: AGL Energy Generators

DUID Capacity Fuel

NPS1 265 Brown Coal

NPS2 265 Brown Coal

PLAYB-AG 60 Brown Coal

BRAEMAR1 168 Coal Seam Methane






Table A.2: Alinta Energy Generators

DUID Capacity Fuel

ANGAS1 30 Natural Gas

ANGAS2 20 Natural Gas

Table A.3: Infratil Energy Generators

86

Data

DUID Capacity Fuel

DRYCGT1 57 Natural Gas



MINTARO 105 Natural Gas

POR01 55 Diesel

POR03 23 Diesel

SNUG1 69 Diesel

PPCCGT 160 Natural Gas

LOYYB1 500 Brown Coal

LOYYB2 500 Brown Coal

HWPS1 200 Brown Coal








Table A.4: International Power Generators

DUID Capacity Fuel

LADBROK1 50 Natural Gas

LADBROK2 50 Natural Gas

OSB-AG 204 Natural Gas

QPS1 25 Natural Gas

QPS2 25 Natural Gas

QPS3 25 Natural Gas

QPS4 25 Natural Gas

QPS5 128 Natural Gas

ER01 660 Black Coal

ER02 660 Black Coal

ER03 660 Black Coal

ER04 660 Black Coal

MSTUART1 146 Kerosene



DDPS1 280 Natural Gas

ROMA_7 40 Natural Gas

ROMA_8 40 Natural Gas

URANQ11 166 Natural Gas




Table A.5: Origin Energy Generators

87

Data

DUID Capacity Fuel

AGLHAL 16.8 Natural Gas

WW7 500 Black Coal

WW8 500 Black Coal

TALWA1 460 Natural Gas

YWPS1 360 Brown Coal




CATHROCK 66 Wind

WATERLWF 111 Wind

Table A.6: TRUenergy Generators

88

Data

A.2 Wind generation data

Wind generation is categorised under two categories in the National Electricity

Market, non-scheduled generation and semi-scheduled. Non-scheduled is treated

outside of the centralised dispatch process and therefore acts as a negative impact

on the demand from the dispatch process. Semi-scheduled generation is

incorporated into the centralised dispatch process. Table A.7 shows the wind farms

owned by each of the �rms and Table A.8 shows all wind farms in the South

Australian region.

Firm Name DUID Capacity Region

Hallett 1 Wind Farm HALLWF1 94.5 SA1

Hallett 2 Wind Farm HALLWF2 71.4 SA1

AGLNorth Brown Hill Wind Farm NBHWF1 132.3 SA1

The Blu� Wind Farm BLUFF1 52.5 SA1

Wattle Point Wind Farm WPWF 90.75 SA1

Oaklands Hill Wind Farm OAKLAND1 67 VIC1

Cullerin Range Wind Farm CULLRGWF 30 NSW1

Origin Lake Bonney Wind Farm LKBONNY1 80.5 SA1

Wonthaggi Wind Farm WONWP 12 VIC1

TRUenergyCathedral Rocks Wind Farm CATHROCK 66 SA1

Waterloo Wind Farm WATERLWF 111 SA1

Table A.7: Wind Generators by Firm

Name DUID Capacity Firm

Cathedral Rocks Wind Farm CATHROCK 66 TRUenergy Pty Ltd

Clements Gap Wind Farm CLEMGPWF 57 Paci�c Hydro Clements Gap Pty Ltd

Hallett 1 Wind Farm HALLWF1 94.5 AGL Energy

Hallett 2 Wind Farm HALLWF2 71.4 AGL Energy

Lake Bonney Stage 2 Windfarm LKBONNY2 159 Lake Bonney Wind Power Pty Ltd

Lake Bonney Stage 3 Wind Farm LKBONNY3 39 Lake Bonney Wind Power Pty Ltd

Lake Bonney Wind Farm LKBONNY1 80.5 Origin Energy

Mt Millar Wind Farm MTMILLAR 70 Mt Millar Wind Farm Pty Ltd

North Brown Hill Wind Farm NBHWF1 132.3 AGL Energy

Snowtown Wind Farm SNOWTWN1 99 Snowtown Wind Farm Pty Ltd

Star�sh Hill Wind Farm STARHLWF 34.5 Star�sh Hill Wind Farm Pty Ltd

The Blu� Wind Farm BLUFF1 52.5 AGL Energy

Waterloo Wind Farm WATERLWF 111 TRUenergy Pty Ltd

Wattle Point Wind Farm WPWF 90.75 AGL Energy

Table A.8: SA Wind Generators

89

Appendix B

Analysis Implementation

The data was collated using scripting in AutoIt and Python and data analysis was

performed using Python and Stata. A vast amount of preliminary data manipulation

and aggregation work had to be conducted to arrive at the �nal data sets used in

the analysis, primarily done through customised Python scripts. This consisted of:

• Extracting relevant data price, demand and generator output data from the

AEMO csv �les;

• Extracting bid stacks from NemSight market analysis software;

• Determining both semi-scheduled and non-scheduled wind output at �rm,

regional and market levels by aggregating individual generators outputs; and

• Determining the breakdown of �rm output by generation types; baseload,

intermediate, peaking, hydro, wind and other.

Approximately 40 gigabytes of data was extracted from various data sources,

primarily the AEMO databases at www.aemo.com.au. The most time intensive

element of the process was the estimation of the marginal costs at each point in

time. For each �rm and each of the approximately 105,000 intervals approximately

400 bids were analysed to create the bid and residual demand curves and a custom

Gaussian Kernel Density Smoothing algorithm was run on each of the curves in

order to �nd their intersection and estimate the residual demand derivative. Once

90

Analysis Implementation

the �nal data sets were obtained the analysis followed the structure outlined in

Figure B.1.

Figure B.1: Analysis schematic

91

Appendix C

Forward Contract Pro�les

This Appendix provides the additional forward contract pro�le results for the

weekend days, Saturday and Sunday, for the �ve dominant �rms operating in the

South Australian region of the National Electricity Market. For the analysis

conducted in this study, forward contract pro�les were calculated for three cases,

weekdays, Saturdays and Sundays. The weekday results are reported in Section

5.2. Wind output has been omitted as it is assumed to be uncontracted and not

competitively bid into the market. As with the results previously reported, the day

is broken up into 288 5-min intervals and the daily pro�les show the breakdown of

generation and forward contract estimates from 12:00am to 11:55pm.

Figure C.1: Forward Contract Pro�les - Saturday - AGL Energy

92


Figure C.2: Forward Contract Pro�les - Sunday - AGL Energy

Figure C.3: Forward Contract Pro�les - Saturday - Alinta Energy

93


Figure C.4: Forward Contract Pro�les - Sunday - Alinta Energy

Figure C.5: Forward Contract Pro�les - Saturday - International Power

94


Figure C.6: Forward Contract Pro�les - Sunday - International Power

Figure C.7: Forward Contract Pro�les - Saturday - Origin Energy

95


Figure C.8: Forward Contract Pro�les - Sunday - Origin Energy

Figure C.9: Forward Contract Pro�les - Saturday - TRUenergy

96


Figure C.10: Forward Contract Pro�les - Sunday - TRUenergy

97

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