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Capacity withholding in restructured wholesale power markets: An agent-based test bed study

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  • Li, Hongyan
  • Tesfatsion, Leigh

Abstract

This study uses a dynamic 5-bus test case implemented via the AMES Wholesale Power Market Test Bed to investigate strategic capacity withholding by generation companies (GenCos) in restructured wholesale power markets under systematically varied demand conditions. The strategic behaviors of the GenCos are simulated by means of a stochastic reinforcement learning algorithm motivated by human-subject laboratory experiments. The learning GenCos attempt to improve their earnings over time by strategic selection of their reported supply offers. This strategic selection can involve both physical capacity withholding (reporting of lower-than-true maximum operating capacity) and economic capacity withholding (reporting of higher-than-true marginal costs). We explore the ability of demand conditions to mitigate incentives for capacity withholding by letting demand bids vary from 100% fixed demand to 100% price-sensitive demand.

Suggested Citation

  • Li, Hongyan & Tesfatsion, Leigh, 2009. "Capacity withholding in restructured wholesale power markets: An agent-based test bed study," ISU General Staff Papers 200901010800001033, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genstf:200901010800001033
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    Cited by:

    1. Li, Hongyan & Tesfatsion, Leigh, 2012. "Co-learning patterns as emergent market phenomena: An electricity market illustration," Journal of Economic Behavior & Organization, Elsevier, vol. 82(2), pages 395-419.
    2. Vespucci, Maria Teresa & Innorta, Mario & Cervigni, Guido, 2013. "A Mixed Integer Linear Programming model of a zonal electricity market with a dominant producer," Energy Economics, Elsevier, vol. 35(C), pages 35-41.
    3. Li, Hongyan & Sun, Junjie & Tesfatsion, Leigh S., 2009. "Separation and Volatility of Locational Marginal Prices in Restructured Wholesale Power Markets," Staff General Research Papers Archive 13075, Iowa State University, Department of Economics.
    4. Moradi, Mohammad H. & Razini, Saleh & Mahdi Hosseinian, S., 2016. "State of art of multiagent systems in power engineering: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 814-824.

    More about this item

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • D6 - Microeconomics - - Welfare Economics
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L2 - Industrial Organization - - Firm Objectives, Organization, and Behavior
    • L3 - Industrial Organization - - Nonprofit Organizations and Public Enterprise
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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