Ali Hortaçsu, University of Chicago Steve Puller, Texas A&M

1. Testing Models of Strategic Bidding in Auctions:  A Case Study of the Texas Electricity Spot Market Ali Hortaçsu, University of Chicago Steve Puller, Texas A&M

2. Motivations • “Deregulation” of electricity markets • Optimal mechanism for procurement? • Empirical auction literature • Bid data + equilibrium model  valuation • Analog in “New Empirical IO” • Eqbm (p,q) data + demand elasticity + behavioral assumption  MC • Can equilibrium models be tested? • Laboratory experiments • Electricity markets are a great place to study firm pricing behavior • This paper measures deviations from theoretical benchmark & explores reasons

3. Texas Electricity Market • Largest electric grid control area in U.S. (ERCOT) • Market opened August 2001 • Incumbents • Implicit contracts to serve non-switching customers at regulated price • Various merchant generators

4. Electricity Market Mechanics • Forward contracting • Generators contract w/ buyers beforehand for a delivery quantity and price • Day before production: fixed quantities of supply and demand are scheduled w/ grid operator • (Generators may be net short or long on their contract quantity) • Spot (balancing) market • Centralized market to balance realized demand with scheduled supply • Generators submit “supply functions” to increase or decrease production from day-ahead schedule

5. Balancing Energy Market • Spot market run in “real-time” to balance supply (generation) and demand (load) • Adjusts for demand and cost shocks (e.g. weather, plant outage) • Approx 2-5% of energy traded (“up” and “down”) • “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 “elbow points” (20 up and 20 down) • Market separated into zones if transmission lines congested – we focus on uncongested hours

6. Quantity Traded in Balancing Market Mean = -24 Stdev = 1068 Min = -3700 25th Pctile = -709 75th Pctile = 615 Max = 2713 Sample: Sept 2001-January 2003, 6:00-6:15pm, weekdays, no transmission congestion

7. Who are the Players?

8. Incentives to Exercise Market Power • 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 (monopsony)

9. Empirical Strategy Price MR1 Sio(p,QCi) B D MCi(q) A RD1 Quantity QCi

10. Empirical Strategy Price MR1 MR2 Sio(p,QCi) B D MCi(q) C A RD1 RD2 Quantity QCi

11. Empirical Strategy Price Sixpo(p,QCi) MR1 MR2 Sio(p,QCi) B D MCi(q) C A RD1 RD2 Quantity QCi

12. Reliant’s Residual Demand Reliant’s MC Ex Post Optimal Bid Schedule Reliant’s Bid Schedule

13. Preview of Results • Largest firm bids close to benchmarks for optimal bidding • Small firms significantly deviate, but there’s some evidence of improvement over time • Efficiency losses from “unsophisticated” bidding at least as large as losses from “market power”

14. Methods to Test Expected Profit Maximizing Behavior Difficult to compare actual to ex-ante optimal bids • Wolak (2000,2001)  solving ex-ante optimal bid strategy (under equilibrium beliefs about uncertainty) is computationally difficult Options • Restrict economic environment so ex-post optimal = ex-ante optimal • Intuitively, uncertainty and private information shift RD in parallel fashion • Check (local) optimality of observed bids (Wolak, 2001) • Do bids violate F.O.C. of Eε[π(p,ε)]? • Can simple trading rules improve upon realized profits?

15. Uniform-Price Auction Model of ERCOT • Setup • Static game, N firms, costs of generation Cit(q) • Contract quantity (QCit) and price (PCit) • Total demand • Generators bid supply functions Sit(p) • Note: in “balancing market” terminology, these bids take form of INCrements and DECrements from “day-ahead” scheduled quantities • Market-clearing price (pc) given by (removing t subscript from now on):

16. Model (cont’d) • Ex-post profit: • Information Structure • Ci(q) common knowledge • Private information: • QCi • PCi – but does not affect maximization problem • is unknown, but this is aggregate uncertainty  important sources of uncertainty from perspective of bidder i • Rival contract positions (QC-i) and total demand (ε)

17. Sample Genscape Interface

18. Characterization of Bayesian Nash Equilibrium

19. Equilibrium (cont’d)

20. Equilibrium (cont’d)

21. Computing Ex Post Optimal Bids (Prop 3) Ex post best response is Bayesian Nash Eqbm • Uncertainty shifts residual demand parallel in & out (observed realization of uncertainty provides “data” on RDi'(p) for all other possible realizations) • Can trace out ex post optimal/equilibrium bidpoint for every realization of uncertainty (distribution of uncertainty doesn’t matter)

22. Do We Expect to See Optimal Bidding? • First year of market • Some traders experienced while others brought over from generation and transmission sectors • Many bidding & optimization decisions being made • Real-time information? • Frequency charts & Genscape sensor data  rival costs • Aggregate bid stacks with 2-3 day lag  “adaptive best-response” bidding? • Is there enough $$ at stake in balancing market? • Several hundred to several thousand per hour • “Bounded rationality”

23. Sample Bidding Interface

24. Sample Bidder’s Operations Interface

25. Data (Sept 2001 thru Jan 2003) • 6:00-6:15pm each day • Bids • Hourly firm-level bids • Demand in balancing market – assumed perfectly inelastic • Marginal Costs for each operating fossil fuel unit • Fuel efficiency – average “heat rates” • Fuel costs – daily natural gas spot prices & monthly average coal spot prices • Variable O&M • SO2 permit costs • Each unit’s daily capacity & day-ahead schedule

26. Balancing MC Total MC Price MW Day-Ahead Schedule 0 Measuring Marginal Cost in Balancing Market • Use coal and gas-fired generating units that are “on” and the daily capacity declaration • Calculate how much generation from those units is already scheduled == Day-Ahead Schedule

27. Reliant (biggest seller) Example

28. TXU (2nd biggest seller) Example

29. Guadalupe (small seller) Example

30. $ Optimal Actual Avoid Calculating Deviation from Optimal Producer Surplus

32. Testing Expected Profit Maximizing Behavior • Restrict economic environment so ex-post optimal = ex-ante optimal • No restrictions – uncertainty can “shift” and “pivot” RD • Can simple trading rules improve upon realized profits? • Check (local) optimality of observed bids (Wolak, 2001)

33. “Naïve Best Reply Test” of Optimality • Bidders can see aggregate bids with a few day lag • Simple trading rule: use bid data from t-3, assume rivals don’t change bids, and find ex post optimal bids (under parallel shift assumption) • Does this outperform actual bidding?

34. Generator’s Ex-Ante Problem • Max Eε[π(p,ε)] uncertainty (ε) can enter RD(p,ε) very generally • Wolak test for (local) optimality: • Ho: Each bidpoint chosen optimally • Changing the price/quantity of each (pk,qk) will not incrementally increase profits on average

35. Test for (Local) Optimality of Bids Moment condition for each bidpoint on day t:

36. Test for (Local) Optimality of Bids

37. What the Traders Say about Suboptimal Bidding • Lack of sophistication at beginning of market • Some firms’ bidders have no trading experience; are employees brought over from generation & distribution • 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 • Newer generators • If a unit has debt to pay off, bidders follow a formula of % markup to add

38. What the Traders Say (cont’d) • 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 in 2nd year of market • 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

39. Possible Explanations for Deviations from Benchmarks • Unmeasured “adjustment costs” • Transmission constraints • Collusion / dynamic pricing • Type of firm • Stakes matter

40. Adjustment Costs? • Flexible gas-fired units often are marginal • 70-90% of time for firms serving as own bidders • “Bid-ask” spread smaller for firms closer to benchmark • Decreases over time for higher-performing firms

41. Transmission Constraints? • Does bidding strategy from congested hours spillover into uncongested hours? • 1 std dev increase in percent congestion  only 3% ↓Pct Achieved

42. Collusion? • Collusion not consistent with large bid-ask spreads • Collusion  smaller sales than ex-post optimal • Bid-ask spread  no sales • Would be small(!) players - unlikely

43. Do Stakes Matter?

44. Explaining “Percent Achieved” Across Firms (4) Own Bidders: 1000MWh increase in sales  86 percentage point increase in Pct Achieved (5) Own Bidders: 1000MWh increase in sales  97 percentage point increase in Pct Achieved

45. Learning?

46. Efficiency Losses from Observed Bidding Behavior • Which source of inefficiency is larger? • Exercise of market power by large firms? • Bidding “to avoid the market” by “unsophisticated” firms? • “Strategic” == top 6 in Pct Achieved • Total efficiency loss = 27% • Fraction “strategic” = 19% Fraction “unsophisticated”=81%!!

47. Conclusions • Electricity markets are a great “field” setting to understand firm behavior under uncertainty and private information • Stakes appear to matter in strategic sophistication • Both sophistication (“market power”) and lack of sophistication (“avoid the market”) contribute to inefficiency in this market • Equilibrium bidding models • For large firms, models closely predict actual bidding • For small firms/new markets, models less accurate • Market design • If strategic complexity imposes large participation costs, may wish to choose mechanisms with dominant strategy equilibrium (e.g. Vickrey auction)

48. The End

50. Evolution of Bid-Ask Spread