1. Testing Market Structures for Electricity Using PowerWeb Tim Mount Department of Applied Economics and Management Cornell University Ithaca, NY 14853-7801 607-255-4512 TDM2@cornell.edu

2. Page 2 2  FACULTY PARTICIPANTS  EngineersEconomists  Bob Thomas (Director) Duane Chapman  Jim Thorp Tim Mount  Bernie Lesieutre (visiting) Dick Schuler  Ray Zimmerman Bill Schulze  FINANCIAL SUPPORT  - Transmission Reliability Program, US Dept. of Energy  - Industrial and Government Members of PSERC  - California Energy Commission PSERC Research at Cornell University

3. Page 3 3 Why Use Experiments?  Market structures for electricity auctions are too complicated to derive analytical results.  Experiments are inexpensive compared to experimenting directly on the public.  Paying participants in experiments on the basis of their performance duplicates market behavior effectively.  The effects of specific market characteristics can be isolated and tested.  PowerWeb supports a full AC network, so that the market implications of congestion and ancillary services -- as well as real power -- can be studied.  Our motto: TEST NOW or PAY LATER

4. Page 4 4 Types of Auction Tested  Uniform Price Auction using the Last Accepted Offer to set the clearing price  Discriminative Auction paying blocks the Actual Offers submitted  Soft-Cap Auction combining a uniform price auction below the cap and a discriminative auction above the cap

5. Page 5 5 Experiments Using PowerWeb (Spring 2001) Auctions Tested Uniform Uniform with Price – Responsive Load Discriminative Soft – Cap Participants (Representing Suppliers) 1. Cornell University Students (Auctions 1 – 4)  3 Groups of 6 (25 periods) 2. University of Illinois Students (Auctions 1 – 4)  2 Groups of 6 (50 periods) 3. New York Department of Public Services (Auctions 1 and 2)  4 Groups of 6 (30 periods)

6. Page 6 6 Average Prices for Experiment 1 (uniform)

7. Page 7 7 Average Prices for Experiment 2 (uniform, price responsive)

8. Page 8 8 Average Prices for Experiment 3 (discriminative)

9. Page 9 9 Average Prices for Experiment 4 (soft cap)

10. Page 10 10 Illustrative Offer Curve for Experiment 1 (uniform)

11. Page 11 11 Illustrative Offer Curve for Experiment 3 (discriminative)

12. Page 12 12 Average Prices for High and Low Loads

13. Page 13 13  Types of Auction Tested  Uniform price auction with price inelastic load  Soft-cap auction with price inelastic load  Soft-cap auction with price responsive load  Uniform price auction with price responsive load  Objectives  Will experts do better than students?  Is it practical to run experiments over the internet?  Can people exploit a soft-cap auction without experience in a discriminative auction?  Will price responsive load be effective as a way to reduce prices in a soft-cap auction? National Experiment (Using PowerWeb)

14. Page 14 14 National Experiment Analysis of Variance Main Features  6 Experiments  25 Periods per Experiment  3 Groups of 6 Industry Professionals Average Price For Last 10 Periods $/MWh  Source of VariationPercentage F Statistic  Experiments 73 6.78*  Groups 6 1.34  Unexplained 21  TOTAL 100 * Statistically Significant

15. Page 15 15 National Experiment Legend for Regression Analysis  UN – Uniform Price Auction  SC – Soft Cap Auction (Cap at $75/MWh)  IN – Inelastic Load  PR – Price Responsive Load  * * – Initial Costs are High for Marginal Units UNSCSC** INABE PRDCF

16. Page 16 16 National Experiment Regression Analysis  Average Price $/MWh  VariableCoefficient T-Statistic  Mean 67 74.0  Exp.A UN-IN 1 0.5  Exp.B SC-IN -3 -1.5  Exp.C SC-PR -3 -1.6  Exp.D UN-PR -7 -3.7*  Exp.E SC-IN** 8 4.3*  Exp.F SC-PR** 4 1.9  Group 1 2 1.6  Group 2 -1 1.1  Group 3 -1 0.5  * – Statistically Significant

17. Page 17 17 National Experiment: Soft-Cap Auction (Average Prices for 3 Groups of Industry Professionals)

18. Page 18 18 SUNY Binghamton (Course taught my Ed Kokkelenberg)  UN – Uniform Price Auction  SC – Soft Cap Auction (Cap at $75/MWh)  IN – Inelastic Load  PR – Price Responsive Load  * * – Initial Costs are High for Marginal Units UN**SC** INAB PRDC

19. Page 19 19 SUNY Binghamton: Soft-Cap Auction (Average Prices for 3 Groups of Undergraduates)

20. Page 20 20 SUNY Binghamton: Uniform Price Auction (Average Prices for 3 Groups of Undergraduates)

21. Page 21 21 Combined Regression Results 1 Average price ($/MWh) in high cost periods with t-ratio in parentheses. 2 A Uniform price auction with inelastic load B Soft-cap auction with inelastic load C Soft-cap auction with price responsive load D Uniform price auction with price responsive load S Students P Professionals 3 Estimated price change ($/MWh) with t-ratio in parentheses. * Denotes statistical significance at the 5% level.

22. Page 22 22  Uniform Price Auction (Pay same price)  Infrequent high price spikes are typical  Speculating with a FEW units is rational behavior  The supply curve looks like a hockey stick  Price responsive load mitigates price spikes effectively  Discriminative (Soft-Cap) Auction (Pay actual offers)  Persistent high prices may occur  Speculating with MANY units is rational  The supply curve is relatively flat  Price responsive load does NOT mitigate high prices Summary of The Experiments

23. Page 23 23  Effective countervailing power by loads to mitigate high prices in electricity markets  Effective orchestration of distributed resources for supplying real energy and ancillary services  Active trading of forward contracts in public markets, and better ways to hedge against the uncertainty of price and load  Consistent standards of reporting data to the public  Predictability of regulation Missing Pieces of The Puzzle

24. Page 24 24 COMING THIS SUMMER TO A PC NEAR YOU P OWER W EB II