GENERATORS STRATEGIES IN THE ENGLAND AND WALES ELECTRICITY MARKET - A SYNTHESIS OF SIMULATION MODELLING AND ECONOMETRIC ANALYSIS

Transcription

1 TECHNICAL PAPER 16 GENERATORS STRATEGIES IN THE ENGLAND AND WALES ELECTRICITY MARKET - A SYNTHESIS OF SIMULATION MODELLING AND ECONOMETRIC ANALYSIS Phil Burns Mike Huggins Reamonn Lydon

2 The University of Bath School of Management is one of the oldest established management schools in Britain. It enjoys an international reputation for the quality of its teaching and research. Its mission is to offer a balanced portfolio of undergraduate, postgraduate and post-experience programmes, research and external activities, which provide a quality of intellectual life for those involved in keeping with the best traditions of British universities.

3 Generators Strategies in the England and Wales Electricity Market A Synthesis of Simulation Modelling and Econometric Analysis CRI Technical Paper 16 Phil Burns Mike Huggins Reamonn Lydon Desktop published by Jan Marchant The University of Bath All rights reserved ISBN

4 Centre for the study of Regulated Industries (CRI) The CRI is a research centre of the University of Bath School of Management. The CRI was founded in 1991 as part of the Chartered Institute of Public Finance and Accountancy (CIPFA). It transferred to the University of Bath School of Management in It is situated on the 8 th floor of Wessex House (North), adjacent to West car park. The CRI is an interdisciplinary research centre investigating how regulation and competition are working in practice, both in the UK and abroad. It is independent and politically neutral. It aims to produce authoritative, practical contributions to regulatory policy and debate, which are put into the public domain. The CRI focuses on comparative analyses across the regulated industries. CRI activities and outputs include: Regulatory statistics, information and analysis Discussion papers and Occasional papers Regulatory Briefs, Reviews and International series Research Reports and Technical papers Seminars, courses and conferences Direct links with regulated industries, the regulators, the academic community and other interested parties are an important feature of the work of the CRI. The CRI is non-profit making. Its activities are supported by a wide range of sponsors. BAA CIPFA Department of Trade and Industry Environment Agency National Audit Office NERA National Grid Transco Network Rail OFWAT RSM Robson Rhodes Royal Mail Group Thames Water United Utilities Wessex Water Further information about the work of the CRI can be obtained from:- Peter Vass, Director-CRI, School of Management, University of Bath, Bath, BA2 7AY or CRI Administrator, Jan Marchant, Tel: , Fax: , mnsjsm@management.bath.ac.uk and from the CRI s web site, which includes events and the publications list. CRI Publications and publications list can be obtained from Jan Marchant as above.

5 Preface The CRI is pleased to publish Technical Paper 16, Generators Strategies in the England and Wales Electricity Market - A Synthesis of Simulation Modelling and Econometric Analysis by Phil Burns, Mike Huggins and Reamonn Lydon of Frontier Economics. It is an important question whether the reforms to the Pool system, resulting in the new electricity trading arrangements (NETA), have reduced actual or potential abuse of market power by generators. This paper takes an innovative approach to examining the problem, and challenges traditional explanations and predictors based on measures of concentration. It is an excellent example of a blend of theory and empirical application which addresses policy questions, and yields new insights for competition authorities in discharging their responsibilities to protect consumers. The CRI publishes a wide range of occasional and technical papers, research reports and regulatory briefs, and encourages those working in the field whether as academics or in other types of organisation to submit suitable material for consideration for publication. Enquiries and manuscripts should be addressed to: CRI, School of Management, University of Bath, Bath BA2 7AY. Peter Vass Director, CRI September 2004 iii

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7 Contents Preface Page iii 1 Introduction 1 2 Overview of the industry 3 The Pool 3 Contract trading 4 Structure of ownership 4 Regulatory developments and the evolution of prices 6 3 Approaches to analysing market power 8 4 Modelling framework 9 Evolution of generators strategies 9 Evaluation of concentration measures 12 5 Data 13 Calculation of marginal cost 13 Calculation of mark-ups for each half-hour 15 Calculation of strategic variables 16 Calculation of the concentration measures 19 6 Results 20 Overview of the period 20 Regression results - gaming indicators 26 Regression results - concentration measures 30 7 Extending the analysis into the NETA 33 8 Conclusions 34 9 Bibliography and other references 35 Annex 1: Description of the SPARK model 37 Annex 2: Tables of regression results 41 v

8 Abstract Through econometric analysis of half-hourly price mark-ups, we have demonstrated that prices in the England and Wales market over the period 1996 to 2001 are poorly explained by the evolution of the HHI and other simple measures of concentration. We have developed an alternative approach to simulating market power in wholesale electricity markets. This approach makes use of not only the capacity shares of the major players, but also encompasses the cost structure of the industry and demand conditions. We have applied this approach to the England and Wales market using Frontier s simulation model - SPARK. Our analysis has illustrated that this approach leads to indicative variables that capture the scope for the exercise of market power and that these variables fit the actual data from England and Wales well. We have also illustrated how that approach can be used to assess the extent to which the exercise of market power might be restrained by the threat of entry and/or regulatory intervention. We believe that this approach represents a fruitful area for further academic research and has a clear policy application in the assessment of mergers in wholesale electricity markets. Phil Burns Mike Huggins Reamonn Lydon Frontier Economics 71 High Holborn London WC1V 6DA Tel: Tel: vi

9 1 Introduction Over the last twenty years, many electricity markets around the world have become liberalised and generation activities have been opened up to competition. While many of the markets created by this process of liberalisation have functioned well, delivering savings to customers compared to a regulated counterfactual, there is a concern that, at least in some cases, many of the potential benefits are not being delivered to customers because of the presence of an unacceptable level of market power. Electricity markets have a number of characteristics that make them particularly susceptible to the exercise of market power (e.g., highly inelastic demand, frequently repeated interaction, clearly visible prices, etc.). As a result, there is a need for further work to be undertaken to develop a robust framework to assess and quantify the extent of market power. The experience of the England and Wales wholesale electricity market provides a rich source of data, which can be used to identify the sources of market power, and the extent to which it was exercised. This experience is crucial in informing new reforms elsewhere in the world, and in developing a set of market power indicators that reflect the particular characteristics of electricity systems that can be used, for example, in anti-trust investigations. As we discuss in section 3, a number of important papers have attempted to measure the actual and potential market power of the participants in wholesale electricity markets. The defining feature of these papers is a simulation of market outcomes using oligopoly frameworks that model the strategic behaviour of firms. These models can then be used to simulate the effects of alternative market structures on market outcomes, and, depending on the accuracy of the calibration of the model, the analysis can also be extended to consider the extent to which the predicted outcomes from the simulation model compare with actual outcomes in the market. The most compelling aspect of this literature has been to highlight the importance of simulating the details of a market the structure of ownership, the role of contracts, the plant mix of specific players - as a means of assessing the degree of competitiveness in the industry, which perform better than traditional, generic, measures of concentration such as HHIs or concentration ratios. Such measures make use of only a limited subset of the information that is available on wholesale electricity markets. In this paper we extend this work in a number of ways. First, we combine simulation analysis with econometric analysis to infer robustly what type of game the key strategic players have been playing in England and Wales. We do this by using our electricity market simulation model SPARK to simulate a large number of N person games, each of which usually result in multiple equilibria. We then use regression analysis to identify which of these equilibria best explain actual market outcomes, and thereby identify the types of games that have been played over the period 1996 to This part of the paper can therefore be summarised as taking the price cost margin that has prevailed, and then allowing the data to choose the type of market behaviour that generates those margins by explicitly simulating the Acknowledgment We are grateful to Antoine Duvauchelle for his excellent research assistance, and to Ron Smith, Richard Green, Mike Waterson and colleagues at Frontier Economics for helpful comments. All remaining errors are, of course, our own. Phil Burns, Director, Mike Huggins, Manager and Reamonn Lydon, Consultant, Frontier Economics 1

10 GENERATORS STRATEGIES IN THE ELECTRICITY MARKET outcomes of that behaviour, and entering those outcomes as independent variables in regression analysis. Consequently, we go beyond the measurement of market power to an analysis of the key drivers of market power. As noted by Sweeting, this area of the literature is under-developed, and our methodology and results offer a possible route for future research. 1 Second, in calculating the actual market outcomes the mark-up of wholesale spot prices over marginal cost we employ a far more rigorous approach to calculating marginal cost than in previous work. We use the dispatch module of SPARK, which is a sophisticated system dispatch model that takes account of plant dynamics to calculate marginal cost in a given half-hour. This is in contrast to the common approach in the literature, which is to estimate marginal cost on the basis of a simple marginal cost stack. As has been noted in the literature, the marginal cost stack approach is likely to result in a measurement bias. 2 Third, although the simulation modelling does not account for the threat of entry on behaviour, we use a relatively simple approach to test whether entry or regulatory pressure has had an impact on market outcomes, and therefore whether indeed it is the case that the actual market prices that have prevailed have indeed been too low, relative to the levels that the market participants could have achieved as, for example, Wolfram (1999) suggests. 3 Fourthly, we investigate the usefulness of headline measures of concentration. Not surprisingly, in their unadjusted form they do not appear to be related to market outcomes. However, an alternative construction of the HHI does yield a measure that is statistically related to price movements, and therefore has the potential to act as a rule of thumb in evaluating the impact of a change in ownership. However, whilst useful, this would not substitute for a detailed evaluation of how behaviour could change in response to structural changes in the market, which of course is the value of the simulation-based approach. Finally, much of the analysis of the England and Wales market has been undertaken for the early 1990s, at a time when the market was duopolistic in structure, when vesting contracts were in place and when regulatory interventions were common. We have undertaken our empirical analysis from 1996 to 2001, which corresponds to a time when the market was evolving from a duopoly to a competitive market, and which therefore provides more variation in the sample that is due to changing market structure rather than changes in other factors. The structure of the remainder of the paper is as follows. In section 2, we provide an overview of the industry in England and Wales by way of background. Section 3 provides a brief review of existing approaches to analysing market power before describing our own approach in detail in section 4. Section 5 describes the data construction process and in section 6 we report our results. In Section 7 we briefly indicate the implications of our results for explaining the fall in prices since 1999, and in section 8 we draw together conclusions. 1 Sweeting A T (2001), Market Outcomes and Generator Behaviour in the England and Wales Wholesale Electricity Market, mimeo, MIT. 2 See Brennan T J (2002), Preventing Monopoly or Discouraging Competition? The Perils of Price-Cost Tests for Market Power in Electricity, RFF Discussion Paper, October for a detailed discussion on the pitfalls associated with using the average variable cost of the last generator as a measure of marginal cost. The paper by Joskow P and Kahn E (2001), A Quantitative Analysis of Pricing Behaviour in California s Wholesale Electricity Market in Summer 2000, NBER Working Paper No. 8157, also contains a discussion on the topic. 3 Wolfram C (1999), Measuring Duopoly Power in the British Electricity Market, American Economic Review, 84 (4 Sep),

11 PHIL BURNS, MIKE HUGGINS, REAMONN LYDON 2 Overview of the industry The electricity market in England and Wales was reformed in Prior to this date all the generating assets were owned by the Central Electricity Generating Board (CEGB), a state owned monopoly. The CEGB acted as a central planner, deciding what assets to build and retire and how those assets should be dispatched in order to meet demand. The 1990 reforms separated electricity generation from transmission and divided the generating assets into a number of portfolios. Two of the successor generating companies, National Power (NP) and PowerGen (PG), were privatised in 1990, and a third, British Energy, which operated the PWR and AGR nuclear reactors, was privatised in Only the Magnox stations remain in state hands, operated by British Nuclear Fuels Ltd. (BNFL), which account for only around 3GW of capacity. In addition, the transmission business in England and Wales, the National Grid Company (NGC) was privatised in 1990, along with the twelve area distribution boards, the vertically integrated monopoly distribution and retailers. The reforms of 1990 also established a programme of liberalisation of the wholesale and retail markets for electricity. Entry into the generation market was to become effectively unregulated, subject to the award of a licence; and there was a phased liberalisation of the retail market, ending in 1999 when all customers were free to choose their own electricity supplier. Finally, in addition to changing radically the ownership of the electricity industry, the reforms also introduced a set of trading arrangements to allow generators and suppliers to trade with one another while ensuring that the system could be balanced in real time. A central feature of these arrangements was the electricity Pool. The Pool Under the Pool, generators would compete with one another to supply the electricity required in a given day. Generators were required to submit complex bids for each generating unit they owned by 10am the previous day, with their bids comprising a start-up price, a no-load price and at most three different marginal costs, together with the thresholds at which each marginal price would apply. The bid submitted for each unit would stand for the entire day in question, although the generator could vary the available capacity from half hour to half hour. In addition, the generator would submit information on any dynamic constraints that might apply, such as ramp up and ramp down rates. Given this set of input data, the system operator would use a complex algorithm (GOAL) to determine how the available plant could be scheduled to produce the least cost feasible dispatch, assuming no transmission constraints apply, ie, an unconstrained schedule. A further output of this algorithm was a set of prices for each half hour based on the cost of the marginal unit on the system in each half hour, the System Marginal Price (SMP). Generators scheduled to run would receive the SMP for each unit generated regardless of the actual bids that they submitted. For example, it was common for nuclear plant, which has a low marginal cost, to submit very low (often zero) bids into the Pool in order to ensure that they would be scheduled to run, safe in the knowledge that they would receive SMP. 3

12 GENERATORS STRATEGIES IN THE ELECTRICITY MARKET In addition, generators would receive an additional capacity payment. Capacity payments were intended to provide generators with incentives to make capacity available in order to enhance system security. The size of this payment was based on the value of lost load (VOLL, set by the regulator) and the loss of load probability in each half hour (LOLP, based on the likelihood that capacity would be insufficient to meet demand, ie, due to forced outages or unexpected demand increases). All generators that made capacity available on the day would receive the capacity payment. The price received by generators for each unit produced was therefore SMP plus the capacity payment, the Pool Purchase Price (PPP). While generators received PPP, suppliers were required to pay the Pool Selling Price (PSP), which was equal to PPP plus uplift. Uplift was a charge that covered the additional cost incurred by the system operator as a result of departing from the unconstrained schedule to account for transmission constraints, unexpected changes in demand, unplanned outages and so forth. The pool system existed up to 26 March 2001 when it was abolished and replaced with a bilateral trading system. Contract trading Whilst all electricity had to be traded in the pool, pool price risk could be managed through the contract market. The most common electricity purchase contract traded over the 1990s was the contract for difference (CfD). Under this type of contract, the buyer and seller would agree a fixed price for electricity in a certain period. However, since the buyer would, in the first instance, have to buy from the Pool, and the supplier, in the first instance, would be paid by the Pool for the electricity generated, the settlement between the two parties was in the form of the payment of the difference between the agreed price and the actual pool price that prevailed. The pool price most often used as the price marker was the pool purchase price. The contracts varied in terms of contract length. Between 1990 and 1993, NP and PG were locked into contracts with the downstream monopoly retail businesses. In addition, the new entrants to the market, tended to secure life-of-plant contract terms with their buyers, who would usually have some stake in the plant as well. However, for the period under consideration, the plant owned by the key strategic players were not contracted for lengthy periods of time. This has implications for the simulation of strategic behaviour we undertake. Following Green, we could allow long term contract coverage to affect players strategies and this could be expected to weaken incentives to mark-up prices. 4 However, as noted, the contracting behaviour of the major players after 1996 was not particularly geared towards long-term contracts and shorter-term contracts do little to mitigate incentives to drive up spot market prices as generators will benefit from a higher expectation of prices in forthcoming contract renegotiations. We therefore we do not take account of contracting behaviour in the analysis that follows. Structure of ownership Throughout most of the 1990s the wholesale electricity market in England and Wales exhibited a considerable degree of concentration. When the Pool was introduced almost all 4 Green R (1999), The Electricity Contract Market in England and Wales, Journal of Industrial Economics, Vol XLVII, No 1, pp

13 PHIL BURNS, MIKE HUGGINS, REAMONN LYDON the capacity in England and Wales was owned by just three companies (PowerGen, National Power and Nuclear Electric). Of these three companies, PowerGen (PG) and National Power (NP) owned almost all the plant that might determine the system marginal price. As Figure 1 illustrates, by 1993, the market could still be characterised as a duopoly in which PG and NP had substantial market power over a significant proportion of the year. The figure shows the cumulative capacity of the owners, and indicates that if either NP or PG did not generate electricity, then demand would not be met for a substantial proportion of time. In other words, these generators were pivotal on the system. Figure 1: Capacity stack by owner (1993) 70,000 60,000 50,000 MW 40,000 30,000 Own capacity Cumulative Min demand Max demand 20,000 10,000 0 Other SP S&SE Teesside EdF Edison Magnox BE PowerGen Innogy Source: Frontier Economics However, the structure of the market looked quite different by the time the Pool was abolished and replaced with a bilateral trading system on 27 March 2001, as Figure 2 indicates. No single player was obviously pivotal in meeting peak demand of around 50GW, although the possibility of strategic interaction did exist at peak and mid merit demand periods. The change in the structure of the market was driven by a number of policy measures by the regulator, as well as the continued new entry into the market. Figure 2: Capacity stack by owner (2000) 80,000 70,000 60,000 MW 50,000 40,000 30,000 Own capacity Cumulative Min demand Max demand 20,000 10,000 0 Other SP S&SE Teesside EdF Magnox AES Edison TXU Innogy BE Powergen Source: Frontier Economics 5

14 GENERATORS STRATEGIES IN THE ELECTRICITY MARKET Regulatory developments and the evolution of prices Pool prices increased substantially to 1993, prompting the regulatory agency, Offer, to threaten to refer the generators to the Monopolies and Mergers Commission. Offer indicated that it did not believe that movements in underlying costs justified these price increases. 5 The generators (PG and NP), keen to avoid such an investigation, agreed to limit Pool prices for the next two years and to sell 15% of their plant to a new entrant. This resulted in the long term leasing (99 years) of 6 GW of coal-fired capacity to Eastern in While this change in ownership might have been expected to diminish significantly the opportunities for PG and NP to exercise market power, the terms of the leasing agreement included a payment of 6/MWh from Eastern to PG and NP for each unit of power generated. Hence the marginal cost of these plants to their owner would have been significantly above their true technical marginal cost. As a result it is likely that the terms of the leasing agreement resulted in Eastern bidding this plant into the Pool in a similar way as PG and NP had. Offer s belief that Pool prices had become too high appears to be supported by the waves of new entry that occurred throughout the 1990s, in particular new CCGT build. Nearly 13 GW of CCGT plant entered the E&W market from the introduction of the Pool in 1990 to By the year 2000, the cumulative figure for new CCGT entry was up to 21.6 GW and including the addition of the PWR at Sizewell and some coal build, total new build had exceeded 25 GW by the year While new entrants piled into the E&W market, PG and NP did the opposite, closing a substantial amount of capacity over the same period. Between 1990 and 2000, PG closed 4.5 GW of plant, while NP closed 12.1 GW (together PG and NP accounted for 96% of all exit over the period). The majority of this capacity was coal fired, and these twin trends of rapid gas build combined with coal exit caused serious problems for the UK coal industry. Arguably, it was the political consequences of these electricity market trends on the coal industry that provided the impetus to investigate and review the wholesale trading arrangements. Finally, by 2000 PG and NP completed a second wave of forced divestment. It is generally understood that PG and NP agreed to this divestment in return for being allowed to vertically integrate by purchasing retail businesses. PG completed the sale of Fiddler s Ferry (2 GW) and Ferrybridge (2 GW) to Edison Mission, while NP sold Drax (4 GW) to AES. As a result of these transactions, the market structure in E&W was considerably more fragmented than at the inception of the Pool, as illustrated in Figure 2. This steady erosion in the market power of the original duopoly generators, as measured for example by a HHI, did not seem to affect market outcomes until the late 1990s. Figure 3 shows the evolution of average annual time weighted Pool prices from 1993 to the expiration of the Pool. Also plotted is the HHI in the industry, based upon capacity shares in the market. Quite clearly, throughout most of the 1990s, none of the time-weighted price measures fell, whilst the HHI was falling. Only by the end of the 1990s did prices fall considerably - by around 20% - as concentration in the market became very low, and the market was effectively atomised. This will be investigated further later in the paper. 5 The Office of Electricity Regulation. The office was subsequently merged with the Office of Gas Supply (Ofgas) in 1998 to create the Office of Gas and Electricity Markets (Ofgem). 6

15 PHIL BURNS, MIKE HUGGINS, REAMONN LYDON Figure 3: Development of Pool prices and HHI SMP, PPP HHI SMP PPP HHI Source: Frontier Economics from raw Pool data. The data shown in the figure is a four quarter moving average for each series 7

16 GENERATORS STRATEGIES IN THE ELECTRICITY MARKET 3 Approaches to analysing market power Over the past few years there have been a number of important papers that have evaluated market power in electricity markets using simulation based models of strategic interaction between the major players. The pioneering papers, by Green and Newbury, and Bolle, utilised the supply function equilibrium (SFE) approach first developed by Klemperer and Meyer. 6 Under this approach, generators are assumed to face smooth cost functions, and they bid continuous supply functions into the market, which allows the prospect for price to vary in response to unexpected demand shocks. The advantage of this approach is that there are fewer non-cooperative Nash equilibria than under the case where each player can optimise in each half hour when demands are known with certainty. Indeed, Klemperer and Meyer showed that where there is no demand uncertainty, any non-co-operative Nash equilibrium between Bertrand and Cournot is possible. However, as noted by Borenstein and Bushnell and Borenstein, Bushnell and Knittel the drawback with the SFE approach is that it is difficult to combine it with realistic cost data, since solving for the supply function equilibria requires well-behaved cost and revenue functions. 7 They therefore model pure Cournot strategies, which generate the worst-case outcomes of the exercise of market power. Wolfram compared the mark-ups that actually occurred over an 18 month period between 1992 and 1994, with an updated prediction of the maximum SFE based on the Newbery and Green approach. She found that whilst generators had marked up prices considerably over marginal cost, they had not taken full advantage of their position of market power, and she suggested that this may be due to the threat of entry or the implicit threat of regulatory intervention. 6 Bolle F (1992), Supply Function Equilibria and the Danger of Tacit Collusion: the Case of Spot Markets for Electricity, Energy Economics, April, pp94-102; Green R and Newbery D (1992), Competition in the British Electricity Spot Market, Journal of Political Economy, 100 (5), pp and Klemperer P and Meyer M (1989), Supply Function Equilibria in Oligopoly Under Uncertainty, Econometrica, 57 (6), pp Borenstein S, Bushnell J and Knittel C R (1999), Market Power in Electricity Markets: Beyond Concentration Measures, Program on Workable Energy Regulation (POWER), University of California Energy Institute, Working Paper PWP-059r; Borenstein S and Bushnell J (1999), An Empirical Analysis of the Potential for Market Power in California s Electricity Industry, Journal of Industrial Economics, September, 47, 3, pp

17 4 Modelling framework PHIL BURNS, MIKE HUGGINS, REAMONN LYDON As already indicated, our modelling is focused on two specific issues: first, can we identify the types of games that the major generators were playing over the period; second, can the traditional measures of concentration be modified to act as better rules of thumb to evaluate the overall impact of a change in ownership on market outcomes. We discuss the modelling approach for each in turn. Evaluation of generators strategies In step 1, we use SPARK to calculate the system marginal cost of electricity production for each half-hour of each of the day from 1996 to The dispatch module of SPARK is described in greater detail in section 5. In step 2, we calculate the mark-up of day-ahead pool prices over the system marginal cost for each half-hour between 1996 and 2001, that is: ( P C ) Q = γ 1 P where C Q is the system marginal cost calculated in step 1, and P is the pool purchase price (PPP). In step 3, we use SPARK to simulate the outcomes of many possible N-player static games for each demand level during the day, using a grid-search method 8. The outcome of each game is a set of one or more equilibrium prices, where in each case no generator has an incentive to depart from its chosen bidding strategy, given the bidding strategy chosen by others. We explain the derivation of these equilibrium outcomes in more detail in section 5. In step 4, we seek to explain the actual mark-ups since 1996 by regressing them on the equilibrium mark-ups we have obtained from step 3, and a variety of indicators of other market power such as concentration ratios and the HHI. This stage of the work can therefore be summarised as taking the price cost margin as observable, and then allowing the data to choose the type of market behaviour that generates those margins by explicitly simulating the outcomes of that behaviour, and entering those outcomes as independent variables in regression analysis. We estimate the following equation: γ = α + β Z 2 t i it where Z it is the mark-up from the equilibrium i in the N person game in time period t. We reestimate this regression with the mark-up from all of the other N person games, and then identify which equation best explains the observed mark-ups. For the purpose of analysing 8 It would, of course, be impractical to estimate the Nash Equilibrium price for every observed demand level. Therefore, the approach we follow is to estimate a Nash price for categories of demand in bandwidths of 200MW. Altogether, there are 146 bandwidths in the data, ranging from 22200MW to 51400MW. The estimates of the Nash prices are specific to each band, year and quarter. The approach is explained in more detail in Section 5. 9

18 GENERATORS STRATEGIES IN THE ELECTRICITY MARKET the relationship between the observed mark-ups and the gaming indicators, the sample is divided first by groups of half-hour: 1-12, 13-19, 20-34, 35-48; and then by time period: Q Q1 1999, Q Q4 1999; Q Q Partitioning the sample into blocks of hours reflects the pattern of changing demand over the course of the day, splitting the dataset into blocks that have broadly similar characteristics. Half hours 1-12 cover a period of continuous low demand. Half hours cover the period of the day when demand increases rapidly and generating stations are switched on to cover this increase. Half hours cover the peak periods of the day, when demand is high and the scope for the exercise of market power is at its greatest. Finally, half hours cover the period where demand is decreasing rapidly following the evening peak. Similarly, we have partitioned the sample into blocks over time to reflect possible structural breaks. Q represents a natural starting point for this analysis, as this is the time at which Eastern (TXU) acquired its generating assets from National Power and PowerGen and follows immediately the cessation of Offer s Pool price cap. Q represents, in our view, the next natural breakpoint in the data, as it was around this time that two very large transactions altered radically the structure of ownership in the England & Wales market (Fiddlers Ferry, Ferrybridge and Drax). Our third and final break point is the beginning of 2000, where our analysis suggests that price formation became more stable, possibly reflecting several months of learning on the part of market participants following the substantial transactions that occurred in The gaming variables we use are the maximum, median and minimum static Nash outcomes from 2, 3 and 4 player games. In step 5, we try to explain Wolfram s (1999) observation that observed mark-ups are less than those that would be expected given the duopolistic market structure that existed for much of the 1990s. As Green and Newbery (1992) asserted, low mark-ups are optimal for the incumbents if they do not want to accelerate entry into the market and/or if there is an implicit threat of regulatory intervention if prices rise too high. We employ a relatively simple approach to test whether entry or regulatory pressure has had an impact on market outcomes. We use the insights of Iwata that were developed to analyse market power at the firm level. 9 Iwata modified the Lerner equation as follows: ( P C P where: Q ) ε Q = s (1 + λ ) i i 3 d ( Q q ) i λ i = 4 dqi is the response that firm i expects from all other firms in the industry arising from a change in its own supply (ie, the conjectural variation). Iwata quantified the extent to which the markups over marginal cost achieved by firms were lower than simple profit maximising behaviour might suggest because of this conjectural variation. This logic can be applied to this case in the following way. First we follow Wolfram in defining: 9 Iwata G (1974), Measurement of Conjectural Variations in Oligopoly, Econometrica, Vol 42, No 5, pp , September. 10

19 PHIL BURNS, MIKE HUGGINS, REAMONN LYDON ( P C )ε Q P Q = θ = γε Q 5 Our simulation approach is able to calculate the profit-maximising values of γ, and therefore θ, for a given estimate of the industry demand elasticity. We denote these values as γ* and θ*, respectively. Furthermore, we note that the γ* and θ* that are derived from our simulation modelling will already fully embody the conjectural variation, as the maximum mark-ups are derived from Static Nash Equilibrium outcomes, which account for the best response of all market participants to other s best responses. In addition, using the data that are available we are able to estimate the actual values of γ and θ that have been observed in the England and Wales market. We can therefore measure the extent to which actual mark-ups depart from the theoretical maximums we derive. Since our approach already captures the conjectural variation developed by Iwata, we hypothesize that any remaining difference between the theoretical maximum and actual mark-ups arises as a result of the threat of entry or the threat of regulatory intervention. Therefore, following the logic of Iwata the relationship between the two can be described as follows: * θ = θ s(1 + λ + λ ) E R 6 where we set the market share, s, equal to one as we conduct the analysis at the industry level, and λ E and λ R are associated with the constraints implied by the perceived threat of new entry and regulatory intervention respectively. Clearly, if these constraints do not bind, then the generators should achieve the maximum static Nash available. Following Iwata, we define λ E as: dq NE λ E = 7 dq the response that the industry expects from new entrants in response to a quantity withdrawal (or equivalently a price increase). This enables us to evaluate the extent to which the value of theta is driven by expectations of new entry. In principle, λ E can be calculated by further simulation modelling to evaluate the effects of higher prices that would have prevailed on the speed of entry that actually occurred. This exercise, whilst feasible, goes beyond the scope of this paper, and instead we assume certain values of λ E in order to motivate our analysis. The other effect that may explain the low level of θ is the threat of regulatory intervention. This is calculated as a residual: λ = λ * * * R λ E 8 where λ* is the level of λ that would be required to set θ equal to θ*. 11

20 GENERATORS STRATEGIES IN THE ELECTRICITY MARKET Evaluation of concentration measures To evaluate the usefulness of concentration measures, we take actual mark-ups derived from steps 1 and 2 described above, and regress these on alternative measures of concentration. The measures we use are the HHI and concentration ratios, calculated on the basis of capacity shares, for the wholesale market as a whole. We also employ an alternative construction of the HHI, which is estimated at different points along the merit order. Quite simply, for a given level of demand, the HHI is constructed only by reference to the capacity that exists close to that level of demand in the merit order. In developing such a measure, we do not presuppose that the plant at the top of the merit order is necessarily in a separate market to the more expensive plant at the bottom of the merit order. Instead, we recognise that it is likely, both in real time and over a reasonable planning period, that plant located at some point in the merit order will face more direct competition from those located closer to it in the merit order than those located further away. The alternative measure of the HHI is closer in spirit to the gaming indicators than the simple HHI measure, although it ignores the reaction functions that determine whether a given plant is actually bid-in in the Nash-equilibrium. However, clearly the identification of the plant that is close to a particular level of demand in the merit order is somewhat arbitrary. Therefore, we present results based on three definitions of the alternative HHI. At a given point in the merit order, m, the three HHI measures are defined as follows: [3,6] m n i HHI =, where each plant (i,,n) can dispatch in the range [m-3000mw, s 2 i m+6000mw], and is the capacity share of plant i in that dispatch range; [4,8] m n i s i HHI =, where each plant (i,,n) can dispatch in the range [m-4000mw, s 2 i m+8000mw], and s i is the capacity share of plant i in that dispatch range; and [6,6] m n i HHI =, where each plant (i,,n) can dispatch in the range [m-6000mw, s 2 i m+6000mw], and s i is the capacity share of plant i in that dispatch range. 12

21 PHIL BURNS, MIKE HUGGINS, REAMONN LYDON 5 Data In order to undertake the modelling described in the previous section, we require data on the system marginal cost that prevailed, the mark-ups that existed, the gaming variables and the measures of concentration. We describe each in turn. Calculation of marginal cost In order to calculate the mark-up of prices over cost to assess the extent of market power, we must begin by producing an estimate of the marginal cost of meeting demand in each half hour under analysis. We do this using Frontier s SPARK model. The dispatch module is based on the use of a mixed integer programme (MIP) to derive a least cost dispatch and an associated set of prices. 10 This MIP takes full account of the profile of demand over a typical day, the available generating units at any point in time and the dynamic constraints that affect dispatch decisions over the course of the typical day, such as limits on the speed with which generating units may increase or decrease their output and the presence of start up costs and no load heat costs. We believe that our approach represents a substantial improvement over the simple marginal cost stacks that are usually used to estimate marginal costs. To employ this approach one therefore requires a database of all the generating assets connected to the system, containing the capacity of each unit, its marginal, start up and no load heat costs together with its ramp rates. Data from NGC s Seven Year Statement (SYS) provided an exhaustive list of the assets, together with their capacities, connected to the transmission system and how that list changed over the period of analysis as a result of retirements, new entry (and transactions, although this is only relevant for our analysis of strategic interaction). 11 For thermal generating units, estimates of the marginal cost for each unit were derived from the prevailing primary fuels prices multiplied by an estimate of the efficiency of each unit, plus an estimate of the level of variable O&M per unit generated. Similar calculations were undertaken to estimate no load heat costs and start up costs for each unit, based on its technology type and its vintage, based on our knowledge of typical start up and no load heat costs per kw by technology type and vintage. The capacity of each unit was decreased from the sent out capacity given in the SYS to take account of outages. Hence our treatment of outages is deterministic rather than stochastic. Given the time required to solve the MIP for a typical day, a stochastic treatment is not feasible. Given the size of the data set available to us, comprised of some 5 years of halfhourly data, we do not believe that this substantially limits the analysis. A number of the CCGT units on the system have gas supply contracts that are take-or-pay. Contracts of this type imply that there are times of the year where these units are effectively must-run, since they must burn the gas they are contracted to take and therefore have an 10 Prices emerge from the MIP as the shadow prices on the set of constraints that supply must equal demand in each half hour period. 11 National Grid Company (2004), Seven Year Statement. 12 Coal prices were taken from the DTI and gas prices from information purchased from Heren. Heren collect information on gas contract trades in the UK market. This information can be purchased direct from Heren. More information can be found at 13 We have developed a comprehensive data base of efficiency estimates based on engineering estimates of plant efficiency by technology type and by vintage. 13

22 GENERATORS STRATEGIES IN THE ELECTRICITY MARKET effective marginal cost of zero. 14 On the basis of anecdotal evidence gathered from press reports, we assumed that 50% of the capacity of a typical CCGT was covered by a take-orpay in this way. We therefore split CCGT stations into two halves, with half the capacity bid with a marginal cost of zero and the remainder bid with a marginal cost based on prevailing gas prices. The England and Wales system is interconnected with both France and Scotland. To model the effect of these interconnectors, we analysed actual power flows over the interconnectors over the course of the period of analysis. As a result of this analysis we were able to identify typical power flows over the course of a representative day. These power flows were netted off from system demand. SPARK has the ability to optimise the pumping and dispatch decisions over a given day. However, the demand data with which we were working already accounted for the pumping activity of the pumped hydro stations and as such modelling these units in this way would have led to double counting of pumping load. We therefore modelled pumped storage as reservoir hydro over a representative day, with the station given an endowment of energy to produce over the day in order to maximise revenue. Our estimate of this opening endowment for each station was derived from statistical analysis of actual output from pumped storage stations. The outcome of our approach is a set of prices that accounts fully and rigorously for dynamic constraints, excluding transmission constraints. This is in contrast to the approach adopted in almost all other empirical analysis, which relies solely on simple marginal cost stacks to estimate marginal cost. 15 Figure 4 shows the difference between estimates of the constrained and unconstrained marginal cost over a representative day. Figure 4: The difference between constrained and unconstrained estimates of marginal cost over a single representative day /MWh Simple MC stack Fully optimised dispatch Source: Frontier Economics 14 This assumes that the station cannot sell any gas back to the gas market, i.e., that the gas must be burnt on site. While the details of take-or-pay contracts are not in the public domain, it is our understanding that most contracts prohibit the follow on sale of gas. 15 As Sweeting (2001), who also used a marginal cost stack approach noted, there are inevitably errors associated with marginal cost stack approach, but the literature does not yet cover the extent of those errors. We can explicitly model the difference between the constrained and unconstrained schedule. 14

23 PHIL BURNS, MIKE HUGGINS, REAMONN LYDON The prices emerging from a fully optimised dispatch are markedly different from those derived from analysis of only marginal costs in some periods of the day, in particular those periods in which we typically expect to observe the successful exercise of market power. In this particular example, the marginal cost stack does not pick out the higher prices between half hours 18 and 24, associated with start-up costs incurred by units switching on and running only to cover demand in that period. Similarly, the marginal cost approach does not identify the spike in half hour 34. Price spikes like this in marginal cost estimates are typically caused by large, but short lived, increases in demand from one half hour to the next. In such circumstances, it is often least cost to ask an expensive (in a pure marginal cost sense) but flexible unit to switch on and run for perhaps just a single period. In contrast, the simple marginal cost stack approach will allocate the final units of demand to the next station in the stack, regardless of the start-up costs that the station might incur in running for just one half hour. Finally, in Figure 5 we plot the trend in monthly average of marginal cost over the Q Q period, as estimated using the dispatch module. With the exception a significant kick-up in prices at the end of 2000, the trend in marginal cost is largely downwards, with marginal cost falling by around 20% between 1996 and Figure 5: Trend in marginal cost (optimised dispatch) /MWh Source: Frontier Economics Calculation of mark-ups for each half-hour With our estimates of marginal cost established it is straightforward to calculate a mark-up for the each half-hour in the day following equation 1. We have calculated mark-ups for each of the two Pool price measures, SMP and PPP described above. In the analysis that follows we will focus on the estimated mark-up calculated using the PPP. In practice, the mark-ups obtained using the two price measures are very similar, and there is little to be gained to doing the analysis twice. The evolution of the price mark-up (%) over the period is shown in Figure 6. 15

24 GENERATORS STRATEGIES IN THE ELECTRICITY MARKET Figure 6: Evolution of the price mark-up Mark up of price above marginal cost 50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% Source: Frontier Economics Calculation of strategic variables Having established the variables required for the left hand side of our regression, we now describe the calculation of the strategic variables included on the right hand side. These variables are derived from calculations based on output from the gaming module of SPARK. The gaming module makes use of the same raw input data as the dispatch module that was used to calculate our marginal cost estimates. Our approach to market analysis is based on game theoretic analysis of prices and pay-offs to portfolio generators. Our approach comprises the following steps, which we describe in more detail below: identify a number of potentially strategic players based on the size and characteristics of their generating portfolio (non-strategic portfolios are assumed to bid their units at marginal cost); assign each strategic portfolio a range of strategies; calculate the prices and portfolio payoffs that emerge from all possible combinations of strategies being played by each strategic portfolio; identify which strategy combinations are Static Nash Equilibria by iterated dominance. The extent of any analysis that we might wish to undertake is constrained by available computing power. If we suppose that there are n potentially strategic portfolios and we assign m strategies to each of these, we are required to undertake m n individual dispatches in order to conduct an exhaustive search of the strategy space. As such, analysis can rapidly become infeasible given the time taken to do a single dispatch. In order to reduce the time required we typically limit the range of strategies made available to strategic portfolios. In addition, we do not consider a full dynamic dispatch when conducting gaming analysis, but 16