Testing Market Structures for Electricity Using PowerWeb
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Testing Market Structures for Electricity Using PowerWeb
Tim MountDepartment of Applied Economics and Management
Cornell University
Ithaca, NY 14853-7801
607-255-4512
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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
PSERC Research at Cornell University
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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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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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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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Average Prices for Experiment 1 (uniform)
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Average Prices for Experiment 2 (uniform, price responsive)
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Average Prices for Experiment 3 (discriminative)
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Average Prices for Experiment 4 (soft cap)
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Illustrative Offer Curve for Experiment 1 (uniform)
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Illustrative Offer Curve for Experiment 3 (discriminative)
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Average Prices for High and Low Loads
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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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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 1.34Unexplained 21TOTAL 100
• * Statistically Significant
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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
UN SC SC**
IN A B E
PR D C F
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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
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National Experiment: Soft-Cap Auction(Average Prices for 3 Groups of Industry Professionals)
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SUNY Binghamton(Course taught my Ed Kokkelenberg)
UN – Uniform Price Auction SC – Soft Cap Auction (Cap at $75/MWh) IN – Inelastic Load PR – Price Responsive Load * * – Initial Costs are High for Marginal Units
UN** SC**
IN A B
PR D C
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SUNY Binghamton: Soft-Cap Auction(Average Prices for 3 Groups of Undergraduates)
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SUNY Binghamton: Uniform Price Auction(Average Prices for 3 Groups of Undergraduates)
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Combined Regression Results Intercept1 82.69 (94.8)*
Experiment2 Difference from the average price in high cost periods3
Changes in price from high cost to low cost periods3
A – S + 11.42 (5.9)* - 7.91 (2.6)* B – S + 4.43 (2.3)* + 6.22 (2.1) B – P - 4.19 (2.2)* - 2.54 (0.8) C – S - 6.29 (3.3)* - 3.52 (1.2) C – P - 6.72 (3.5)* - 5.58 (1.8) D – S + 1.36 (0.7) - 12.15 (4.0)*
1 Average price ($/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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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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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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COMING THIS SUMMER TO A PC NEAR YOU
POWERWEB II