Testing Market Structures for Electricity Using PowerWeb

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. PSERC Research at Cornell University • FACULTY PARTICIPANTS • Engineers Economists • 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 Page 2

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 Page 3

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 Page 4

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) Page 5

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

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

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

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

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

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

12. Average Prices for High and Low Loads Page 12

13. National Experiment (Using PowerWeb) • 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? Page 13

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 Variation Percentage F Statistic • Experiments 73 6.78* • Groups 6 1.34 • Unexplained 21 • TOTAL 100 • * Statistically Significant Page 14

15. National ExperimentLegend 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 Page 15

16. National ExperimentRegression Analysis • Average Price $/MWh • Variable Coefficient 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 Page 16

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

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 Page 18

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

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

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

22. Summary of The Experiments • 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 Page 22

23. Missing Pieces of The Puzzle • 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 Page 23

24. COMING THIS SUMMER TO A PC NEAR YOU POWERWEB II Page 24