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Simple-Offer vs. Complex-Offer Auctions in Deregulated Electricity Markets

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  • Rimvydas Baltaduonis

    (University of Connecticut and George Mason University)

Abstract

In my recent experimental research of wholesale electricity auctions, I discovered that the complex structure of the offers leaves a lot of room for strategic behavior, which consequently leads to anti- competitive and inefficient outcomes in the market. A specific feature of these complex-offer auctions is that the sellers submit not only the quantities and the minimum prices at which they are willing to sell, but also the start-up fees that are designed to reimburse the fixed start-up costs of the generation plants. In this paper, using the experimental method I compare the performance of two complex-offer auctions (COAs) against the performance of a simple-offer auction (SOA), in which the sellers have to recover all their generation costs --- fixed and variable ---through a uniform market-clearing price. I find that the SOA significantly reduces consumer prices and lowers price volatility. It mitigates anti-competitive effects that are present in the COAs and achieves allocative efficiency more quickly.

Suggested Citation

  • Rimvydas Baltaduonis, 2007. "Simple-Offer vs. Complex-Offer Auctions in Deregulated Electricity Markets," Working papers 2007-14, University of Connecticut, Department of Economics.
    • Handle: RePEc:uct:uconnp:2007-14
    Note: The author would like to thank the National Science Foundation under grant SES 0648937, and the International Foundation for Research in Experimental Economics for financial support. The author is grateful to the Engineering and Economics faculty and students at the University of Connecticut working on the electricity project and the faculty and students at the Interdisciplinary Center for Economic Science at George Mason University for their helpful comments. The author would like to thank in particular Vicki Knoblauch and Bart Wilson for their valuable suggestions, Jeffrey Kirchner for writing the software for the experiments, Feng Zhao and William Blankson for explaining the complex-offer optimization algorithms. All mistakes are the responsibility of the author. The data are available upon request.
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    References listed on IDEAS

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    1. Rassenti, Stephen J & Smith, Vernon L & Wilson, Bart J, 2003. "Discriminatory Price Auctions in Electricity Markets: Low Volatility at the Expense of High Price Levels," Journal of Regulatory Economics, Springer, vol. 23(2), pages 109-123, March.
    2. Rimvydas Baltaduonis, 2006. "Efficiency in Deregulated Electricity Markets: Offer Cost Minimization vs. Payment Cost Minimization Auction," Working papers 2006-04, University of Connecticut, Department of Economics.
    3. Lynne Kiesling & Bart Wilson, 2007. "An experimental analysis of the effects of automated mitigation procedures on investment and prices in wholesale electricity markets," Journal of Regulatory Economics, Springer, vol. 31(3), pages 313-334, June.
    4. Rimvydas Baltaduonis, 2007. "An Experimental Study of Complex-Offer Auctions: Payment Cost Minimization vs. Offer Cost Minimization," Working papers 2007-13, University of Connecticut, Department of Economics.
    5. Davis, Douglas D. & Holt, Charles A., 2008. "The Exercise of Market Power in Laboratory Experiments," Handbook of Experimental Economics Results, in: Charles R. Plott & Vernon L. Smith (ed.), Handbook of Experimental Economics Results, edition 1, volume 1, chapter 17, pages 138-145, Elsevier.
    6. Yan, Joseph H. & Stern, Gary A., 2002. "Simultaneous Optimal Auction and Unit Commitment for Deregulated Electricity Markets," The Electricity Journal, Elsevier, vol. 15(9), pages 72-80, November.
    7. Nicholas Shunda, 2005. "Strategic Behavior in Day-Ahead and Real-Time Markets for Electricity: Offer Cost or Payment Cost Minimization?," Working papers 2005-48, University of Connecticut, Department of Economics.
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    More about this item

    Keywords

    strategic behavior; sealed-bid auction; complex offer auction; electricity; efficiency;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • D61 - Microeconomics - - Welfare Economics - - - Allocative Efficiency; Cost-Benefit Analysis
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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