Understanding strategic bidding in multi-unit auctions: a case study of the Texas electricity spot market.

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 congested, ERCOT divides the state into separate markets with different market clearing prices. During congested hours, bids by some firms are technically feasible while bids by others are not. Therefore, our analysis uses only uncongested hours, which represent 74% of the time intervals during our sample period.

We analyze weekdays from the beginning of the market in September 2001 to January 2003. Although we can analyze any period of the day, we focus on 6:00-6:15 pm because the most flexible type of generators that can respond to balancing calls without large adjustment costs are likely to be online during this peak hour of the day. The average number of megawatts (MW) traded (both positive and negative) during this time interval is 915 MW. (10) Demand for balancing power is both negative and positive in many 6:00-6:15 pm intervals--the interquartile range is from--709 MW to +615 MW.

The Texas market consists of a variety of investor-owned utilities, independent power producers, municipal utilities, and power cooperatives. The two largest players are the two large former incumbent utilities: TXU and Reliant, owning 24% and 18% of capacity, respectively. The other major investor-owned utilities, (and capacity shares) are Central Power and Light (7%) and West Texas Utilities (2%). Municipal utilities, (e.g., City of San Antonio Public Service (8%) and City of Austin (6%)) and power cooperatives (e.g., Lower Colorado River Authority (4%)) also sell to the balancing market. Finally, there are a large number of merchant generation firms of various sizes (e.g., Calpine (5%)). The generation technology is primarily fueled by natural gas and coal with small amounts of nuclear, hydroelectric, and wind generation.

Each firm has some level of contract obligation to directly supply retail customers or provide power to utilities that serve retail load. If the day-ahead generation schedule differs from the contract obligation, the firm is either long or short entering into the balancing market. This residual contract position affects bidding incentives in the auction (Wolak, 2003a). We propose a method to estimate the long or short position in Section 3.

3. An equilibrium model of bidding on the ERCOT balancing market