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. Related work can be accessed at: http://www.econ.iastate.edu/tesfatsi/AMESMarketHome.htm
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Paper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number
13070.
Length: 8 pages Date of creation: 03 Jun 2009 Date of revision: Handle: RePEc:isu:genres:13070
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Find related papers by JEL classification: C6 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming D4 - Microeconomics - - Market Structure and Pricing D43 - Microeconomics - - Market Structure and Pricing - - - Oligopoly and Other Forms of Market Imperfection L0 - Industrial Organization - - General L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets L2 - Industrial Organization - - Firm Objectives, Organization, and Behavior L3 - Industrial Organization - - Nonprofit Organizations and Public Enterprise Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
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