1. Testing Strategic Models of Firm Behavior in Restructured Electricity Markets: A Case Study of ERCOT Ali Hortacsu, University of Chicago Steve Puller, Texas A&M

2. Motivation Can we measure how effective bids are at… –Maximizing profit? –Achieving least cost system dispatch? Auction theory has predictions –Can we test if bids are close to “optimal” bids? –Complications: uncertainty and private information  difficult to calculate the equilibrium This paper… –Derives model of bidding that analysts can use to calculate optimal bids –Compares optimal bids to actual bids  Technique market monitors can use to evaluate competitiveness of balancing markets

3. Texas Electricity Market ERCOT is largest grid control area in U.S. –Currently has “excess capacity” (2002: 77 GW installed capacity vs. peak of 56 GW) Market opened August 2001 Bilateral transactions scheduled daily through the system operator plus a “balancing market” Players –Incumbents (e.g. Reliant, TXU…) with implicit vesting contracts to serve non-switching customers at price-to-beat –Various merchant generators (e.g. Calpine)

4. Balancing Energy Market Bilateral trades scheduled in day-ahead –may be long or short on contract position Balancing market is approx. 2-5% of energy traded –“up”  bidding price to receive to produce more –“down”  bidding price to pay to produce less Uniform-price auction using hourly portfolio bids that clear every 15-minute interval Bids: monotonic step functions with up to 40 steps Zonal pricing of congestion – we focus on uncongested hours

5. Quantity Traded in Balancing Market Mean = -257 Stdev = 1035 Min = -3700 25 th Pctile = -964 75 th Pctile = 390 Max = 2713 Sample: Sept 2001-July 2002, 6:00-6:15pm, weekdays, no transmission congestion

6. Who are the Players? GeneratorAverage Balancing Sales** (MWh) % of Installed Capacity TXU Electric15624 Reliant Energy47318 City of San Antonio Public Service*8 Central Power & Light287 City of Austin406 Calpine785 Lower Colorado River Authority*4 Lamar Power Partners234 Guadalupe Power Partners82 West Texas Utilities102 Midlothian Energy*2 Dow Chemical*1 Brazos Electric Power Coop51 Others*16 * Cannot uniquely identify the bids ** Sales in zones where bids can be uniquely identified

7. Bidding Incentives Suppose no further contract obligations upon entering balancing market INCremental demand periods –Bid above MC to raise revenue on inframarginal sales –Just “monopolist on residual demand” DECremental demand periods –Bid below MC to reduce output –Make yourself “short” but drive down the price of buying your short position

8. Price Quantity RD i (p) S i (p) MC i (q) MR i (p) QC i A B C E D

9. Overview of Model Assume –Static one-shot game –Marginal Cost i is public information –Contract quantity (QC i ) and price (PC i ) are private information –Generators bid supply functions S i (p,QC i ) Sources of uncertainty –Total load stochastic (Klemperer & Meyer) –Rivals’ bids S -i (QC -i )  Market clearing price is uncertain (application of Wilson’s 1979 share auction)

10. Solving for Equilibrium Bids Ex ante problem: If supply functions take form: S i (p,QC i )=α i (p)+β i (QC i ) Then ex post best response is a (Bayesian Nash) equilibrium  Uncertainty shifts residual demand parallel in & out  Can trace out ex post optimal / equilibrium bids

11. Measuring “Residual” Marginal Cost Use coal and gas-fired generating units that are “on” that hour and the daily capacity declaration (Nukes, Wind, Hydro may not have ability to INC or DEC) Calculate MC (using heat rates, fuel spot prices, VOM similar to Wolfram, BBW, Joskow&Kahn, Mansur…) Calculate how much generation from those units is already scheduled == Day-Ahead Schedule Total MC Residual MC Day-Ahead Schedule Price MW

12. Reliant (biggest seller) Example

13. TXU (2 nd biggest seller) Example

15. Calpine (3 rd biggest seller) Example

16. Guadalupe (small seller) Example

17. Calculating Deviation from Optimal Profits Optimal Actual Avoid $

18. Measures of Foregone Profits

19. Percent of Potential Gains Achieved vs. Size

20. Possible Explanations for Suboptimal Bidding 1.Not enough $$ at stake  avoid the balancing market –Potential profits for each 6-7pm Reliant = $6,165 Lamar Power Partners = $1,391 But Bryan = $315!! 2.Learning –Decrease in bid-ask spread –Profitability over time –Use more bid points over time

21. Learning by Larger Players?

23. Possible Explanations for Suboptimal Bidding 3.Adjustment costs? Marginal generating unit most often is gas (very flexible) Incremental HR Avg HR “locally” 4.Is transmission congestion important? –We analyze only periods with no interzonal transmission congestion b/c congestion changes residual demand –Does congestion “spillover” to uncongested hours? 5.Collusion? Would be small(!) players – seems unlikely

24. Conclusions Market power on DEC side can be inefficient just as on INC side (“prices can be too low”) Stakes appear to matter in strategic sophistication Both sophistication (“market power”) and lack of sophistication (“avoid the market”) contribute to inefficiency in this market Methodology allows calculation of dispatch costs and compare Actual bidding to: (1)Unilateral Best-Reply (Uniform-price auction) (2)Competitive bidding (Vickrey multiunit auction) (3)"Large Unilateral" and "Small Competitive"

25. The End

27. Do We Expect to See Optimal Bidding? First year of market’s operation Different levels of sophistication –Some firms hired experienced traders and some didn’t Real-time information? –Frequency charts & Genscape sensor data  cost data –Aggregate bids with day or two lag Is there enough $$ at stake in balancing market? –Several hundred to several thousand per hour

28. Sample Genscape Interface

29. Data (Sept 2001 thru July 2002) Bids –Hourly firm-level (“portfolio”) bids into Balancing Market Marginal Costs for fossil fuel units Fuel efficiency (“Heat rates”) Spot Fuel costs – gas & coal Variable O&M SO2 permit prices –Generating unit-level day-ahead scheduled generation Periods analyzed: 6:00-6:15pm when no interzonal congestion

30. Zones in ERCOT 2002 Source: Public Utility Commission of Texas, MOD Annual Report (2003)

31. Difference in average system loads: INC = 33GW DEC=29GW Can marginal costs differ by that much?

32. Smaller Players Appear to bid to “withhold capacity” to avoid the balancing market  productive inefficiencies Not market power because markups/markdowns are too large given their small inframarginal sales Policy implications: –Fixed costs to participation? –But some small players are closer to optimal Sticky market for managerial efficiency? Incentives in compensation packages

37. Example of Data We See Sept 14, 2001 6:00-6:15pm Total Balancing Demand = -996 MW Aggregate Bids and MC One Firm’s Bids and MC

39. What the Traders Say about Suboptimal Bidding 1.Lack of sophistication at beginning of market Some firms’ bidders have no trading experience; are employees brought over from generation & distribution 2.Heuristics Most don’t think in terms of “residual demand” Rival supply not entirely transparent b/c Eqbm mapping of rival costs to bids too sophisticated Some firms do not use lagged aggregate bid data Bid in a markup & have guess where price will be 3.Newer generators If a unit has debt to pay off, bidders follow a formula of % markup to add

40. What the Traders Say (contd) 4.TXU “old school” – would prefer to serve it’s customers with own expensive generation rather than buy cheaper power from market Anecdotal evidence that relying more on market after our sample ends 5.Small players (e.g. munis) “scared of market” – afraid of being short w/ high prices Don’t want to bid extra capacity into market because they want extra capacity available in case a unit goes down

41. Metrics of Deviation from Optimal Bidding 1.Eyeball metric 2.(Multiplicative) optimization error 3.Fraction of ex post optimal producer surplus achieved relative to a benchmark of “not bidding”

42. Medians of Reduced-form ‘conduct’ measures