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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
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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
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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
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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
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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)
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Page 6 6 Average Prices for Experiment 1 (uniform)
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Page 7 7 Average Prices for Experiment 2 (uniform, price responsive)
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Page 8 8 Average Prices for Experiment 3 (discriminative)
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Page 9 9 Average Prices for Experiment 4 (soft cap)
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Page 10 10 Illustrative Offer Curve for Experiment 1 (uniform)
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Page 11 11 Illustrative Offer Curve for Experiment 3 (discriminative)
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Page 12 12 Average Prices for High and Low Loads
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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)
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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
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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
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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
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Page 17 17 National Experiment: Soft-Cap Auction (Average Prices for 3 Groups of Industry Professionals)
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Page 18 18 SUNY Binghamton (Course taught my Ed Kokkelenberg) UN – Uniform Price Auction SC – Soft Cap Auction (Cap at $75/MWh) IN – Inelastic Load PR – Price Responsive Load * * – Initial Costs are High for Marginal Units UN**SC** INAB PRDC
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Page 19 19 SUNY Binghamton: Soft-Cap Auction (Average Prices for 3 Groups of Undergraduates)
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Page 20 20 SUNY Binghamton: Uniform Price Auction (Average Prices for 3 Groups of Undergraduates)
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Page 21 21 Combined Regression Results 1 Average price ($/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.
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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
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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
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Page 24 24 COMING THIS SUMMER TO A PC NEAR YOU P OWER W EB II
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