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Article Excerpt
We examine the bidding behavior of firms in the Texas electricity spot market, where bidders submit hourly supply schedules to sell power. We characterize an equilibrium model of bidding and use detailed firm-level data on bids and marginal costs to compare actual bidding behavior to theoretical benchmarks. Firms with large stakes in the market performed close to the theoretical benchmark of static profit maximization. However, smaller firms utilized excessively steep bid schedules significantly deviating from this benchmark. Further analysis suggests that payoff scale has an important effect on firms' willingness and ability to participate in complex, strategic market environments.
1. Introduction
* Many recent empirical analyses of oligopoly competition, including the analysis of bidding in auction markets, rely crucially on assumptions regarding the model of firm behavior. In a typical paper, a researcher has data on firms' prices or bids and seeks to estimate the underlying costs of production or valuation of the auctioned object. By assuming that firms behave according to a particular strategic equilibrium model of profit maximization, the researcher can map firms' observed pricing or bidding decisions into their unobserved costs or valuations. (1) The inferences drawn from such approaches rely on the assumed strategic behavior. In most instances, testing the validity of a particular equilibrium model is left to the laboratory, where the researcher assigns costs/valuations to subjects and compares the subject behavior to the behavior predicted by the equilibrium model of competition. Outside of the laboratory, it is difficult to assess equilibrium models because data usually are not available on bidder costs/valuations.
In this article, we analyze the recently restructured electricity market in Texas, where we have the advantage of having very detailed bidding and marginal cost data on a rich cross-section of generation firms. Our study builds on the work of Wolfram (1999) and Sweeting (2007) on the electricity market of England and Wales, Wolak (2003a) on Australia, and Borenstein, Bushnell, and Wolak (2002) and Puller (2007) on California, who also use marginal cost data to investigate theories of oligopolistic firm behavior. (2) These data allow us to construct benchmarks for each firm's optimal bid functions and compare those to the actual bids. Thus, we "measure" the extent to which the different firms in our sample maximize expected profits and explore reasons for observed deviations from (static) profit maximization.
To construct profit maximization benchmarks, we need to account for institutional complexities of the Texas electricity market in our theoretical model. In this market, most of the electricity is traded through bilateral forward contracts between generators and users of electricity. To meet last-minute changes in aggregate electricity demand that fall beyond or below contracted quantities, generation firms submit bids to adjust their production into an hourly "balancing market" administered by ERCOT (Electric Reliability Council of Texas). Firms participating in this market include large formerly regulated utilities, merchant generating firms, and small municipal utilities and power cooperatives. The hourly market clearing mechanism is a multi-unit, uniform-price auction--firms bid supply functions and winning sellers earn the price at which aggregate supply bids equal demand.
We model competition in the hourly balancing market using Wilson's (1979) "share auction" formulation. (3) In our model, firms choose bid functions to maximize expected profits under uncertainty coming from two sources. First, total demand for balancing power is determined by events such as weather shocks, so it is stochastic from the perspective of the bidder. Second, firms cannot predict the equilibrium bids of their rivals with certainty because each firm possesses private information on their own forward contracts to supply power. These contract obligations determine the firms' net buy or net sell positions in the balancing market, and therefore affect bidding incentives. (4) Because they are private information, these obligations generate uncertainty from the perspective of other bidders. We characterize the Bayesian-Nash equilibrium of bidding into the balancing market. We show that when supply schedules are restricted to be additively separable in price and private information on contract quantities, equilibrium bid schedules are "ex post optimal" and therefore are straightforward to compute, given information on firms' contract positions and their marginal costs of generation.
A simiar benchmark, "best-response bidding," is utilized in Wolak (2003a) and Sweeting (2007) to analyze bidding behavior in the Australian, and England and Wales electricity markets. Wolak (2003a) builds on the Klemperer and Meyer (1989) supply-function equilibrium (SFE) model to motivate this benchmark. (5) In the SFE model, which is nested by the Wilson (1979) model, the source of uncertainty is aggregate demand shifts, and firms do not possess private information regarding each others' marginal costs or contract positions. While the assumption that generation costs are common knowledge across bidders is realistic in electricity markets, where a lot of information is publicly available about each firm's generation technology and the spot price of fuel, it is less likely that firms have accurate information regarding each others' contract positions on a high-frequency basis. Our modelling framework allows for the presence of private information regarding contract positions, and provides conditions under which "best-response bidding" can be supported as an ex post optimal (Bayesian-Nash) equilibrium outcome. The ex post optimality feature of this benchmark allows us to avoid pooling data across auctions, and hence avoids potential measurement biases due to the presence of unobserved (to the econometrician) factors that vary from auction to auction. In Section 4, we also provide a test of the conditions needed for ex post optimality. Along with the ex post optimal bidding benchmark, we also test the ex ante optimality of bidding. We assess whether one can outperform the bidders by constructing the ex post optimal benchmark, conditioning on past realizations of the residual demand curve, which is available to the bidders.
An important requirement for constructing all of these bidding benchmarks is having hour-to-hour information on contract positions. However, this information is typically not available to economic researchers. Wolak (2003a) avoids this problem by utilizing proprietary information on contracts obtained from a generation company in the Australian, market. (6) Because our empirical focus is to analyze the heterogeneity of bidding performance across a wide variety of firms operating in ERCOT, and obtaining information on contract quantities for this wide array of firms was not feasible, we develop a method to infer contract quantities using marginal cost data and the observed bid function. Our method, described in Section 3, relies on a behavioral assumption that is much weaker than profit maximization. In essence, we merely require bidders to understand that they can end up being either net buyers, in which case, they should try to mark down the market price, or net sellers, in which case they should mark up. This practice was acknowledged by all of the firms that we interviewed during our research. (7)
The main empirical finding of the article is that larger firms perform closer to our benchmark for (static) profit maximization. The smaller firms tend to submit bid functions that are "excessively steep" so that these firms are not called to supply much power to the balancing market even when it is ex post profit-maximizing to do so. In Section 5, we argue that this finding is best explained by the presence of scale economies in setting up and maintaining a successful bidding operation--an intuition confirmed by our interviews with traders in the market. Thus, the observed patterns of bidding in this market can be "rationalized" given the fixed costs of establishing a sophisticated trading operation. We discuss this cost of" sophistication" in Section 5. Finally, we find some evidence of learning by the small firms over our sample period. The learning rate is a 10% performance improvement per year.
The observed deviations from theoretical benchmarks are quantitatively important; we find that this behavior leads to significant efficiency losses. In Section 6, we describe the two sources of efficiency losses. The first is the efficiency loss due to the (optimal) exercise of market power by profit-maximizing firms. The second is the efficiency loss due to the "excessive steepness" of small firms' bid schedules that we cannot reconcile with expected profit-maximizing behavior. When we decompose the total efficiency losses into these two components, we find, somewhat surprisingly, that the latter source of inefficiency is larger. The inefficiency generated by the smaller firms suggests that market performance could be improved by the consolidation of small-firm bidding operations or the use of a market mechanism with less strategic complexity.
Our contributions to the growing literature on characterizing strategic behavior on restructured electricity markets are both methodological and policy oriented. By applying the Wilson (1979) model on share auctions, we extend the analysis of Wolak (2003a) and Sweeting (2007) to the case where firms possess private information regarding their contract positions. We also devise a method to estimate firms' (private information) contract positions based on a weak behavioral restriction. This allows us to conduct tests of ex post and ex ante profit maximization for a rich cross-section of firms on this market for which information on contracts is not available.
We also believe that our empirical results have generalizable implications for settings outside of the electricity industry. Aside from controlled experimental settings, there is limited empirical evidence on the importance of payoff scale and learning in real-world strategic environments. We present clear evidence that both mechanisms are in effect on ERCOT. We also document, however, that deviations from profit-maximizing behavior are economically significant. We believe that this latter finding has important implications for market design.
The outline of the rest of the article is as follows: in Section 2, we describe the institutional setting of the Texas electricity balancing market. In Section 3, we model strategic bidding in this market as a uniform-price share auction. We discuss the empirical implications of our model. In Section 4, we compare our theoretical benchmarks with the actual bids in the data. Section 5 discusses these results and explores the role of payoff scale in explaining deviations from ex post optimal bidding. Section 6 calculates the efficiency losses and Section 7 concludes.
2. How bidding occurs in ERCOT's balancing energy market
* We analyze electricity transactions that occur through spot-market auctions. In the Texas wholesale electricity market, most trades occur via bilateral agreements. In addition to this bilateral market, ERCOT, the system operator, conducts an auction to balance supply and demand in real time. Approximately 2-5% of energy is traded in this "spot market," called the Balancing Energy Services auction, and we analyze the bidding into this auction.
The mechanics of electricity transactions on this market can be summarized as follows. (8) One day before production and consumption occur, ERCOT accepts schedules of quantities of electricity to inject and withdraw at specific locations on the transmission grid. Firms' day-ahead schedules are fixed quantities that do not vary in price. The day-ahead schedules may differ from the firms' forward contract position. Those supply ("generation") and demand ("load") schedules also may differ from the actual production and consumption in real time for a variety of reasons such as an unpredictably hot day or an outage at a power plant. The balancing market operates in real time to balance actual load and generation. Depending upon whether more or less power is needed than the day-ahead schedule, the balancing demand can be positive or negative. As the time of production and consumption nears, ERCOT estimates how much balancing energy is required. Because there are virtually no sources of demand that can respond to prices in real time, balancing demand is perfectly inelastic.
Bidders offer to increase (INC) and decrease (DEC) the amount of power supplied relative to their day-ahead schedule. Firms submit hourly INC and DEC bid schedules that must be increasing monotonic step functions with up to 40 " elbow" points (20 INC and 20 DEC bids). These bids may be changed up until one hour prior to the operating hour. The bid schedules apply to each of the four 15-minute intervals of the hour. In addition, the bidder observes real time information on its units' generation, the load it is obligated to serve, and its net short or long position in the balancing market. (9)
Procurement occurs using a uniform-price, multi-unit auction. ERCOT clears the balancing market four times every hour by intersecting the hourly aggregate bid function with the 15-minute perfectly inelastic demand function. A generator called to INC is paid the market clearing price for all INC sales (i.e., production beyond the day-ahead schedule). Likewise, a generator called to DEC pays the market clearing price for the quantity of output reduced. A generator that DECs reduces output and purchases power from ERCOT at the market clearing price to satisfy existing contract obligations.
Bidders appear to have a great deal of information on the competitive environment when they choose their bid functions. Our conversations with several market participants suggest that traders have good information on their rivals' marginal costs. The power plants in Texas have very similar production technologies, and there are publicly available data on the fuel efficiency of each generating unit. Traders appear to know the major generating units that are on--and offline at any point in time. In addition, some market participants purchase real timedata on the generation of large rival plants from an energy information company named Genscape that developed a technology measuring real time output with remote sensors installed near the transmission lines out of a plant. This can be useful strategic information not only when initial bids are submitted but also if the trader wants to change the bids an hour before the market clears.
However, even if firms have a good idea of each others' marginal cost schedules, they seldom have information about competitors' contract obligations. These contracts are signed bilaterally in an over-the-counter market where it is difficult to monitor transactions, and they are seldom publicized. As pointed out by Wolak (2000, 2003a) and will become clear in the next Section, these contract obligations significantly affect bidders' incentives to exercise market power, hence this constitutes a very important source of private information.
The information available to the bidders may allow them to accurately estimate the distribution of their residual demand curve in an upcoming auction. The residual demand is the perfectly inelastic total balancing demand minus bids by all other firms. Total demand is stochastic, but shocks to total demand (e.g., weather) only shift residual demand left and right in a parallel fashion. The distribution of rival bids can be inferred in two ways. A trader equipped with knowledge of rivals' marginal costs and the distribution of their contract positions can compute (as we do in Section 3) the equilibrium mapping of costs and forward positions to bids. Alternatively, and perhaps more plausibly, every trader has access to the aggregate supply schedule with a two-day lag. By knowing the recent aggregate supply curve as well as her own recent bids, the trader can infer the recent aggregate bids by all rivals. To the extent that rival bids several days before are similar to current rival bids, the trader can infer the shape of residual demand before placing her bids.
Congestion of the transmission grid poses a slight complication for our analysis. ERCOT is geographically divided into several zones. If transmission lines between zones are not congested, the balancing market is a single unified market across all Texas. However, when lines become...
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