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Learning Agents in an Artificial Power Exchange: Tacit Collusion, Market Power and Efficiency of Two Double-auction Mechanisms

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  • Eric Guerci

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  • Stefano Ivaldi

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  • Silvano Cincotti

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Abstract

This paper investigates the relative efficiency of two double-auction mechanisms for power exchanges, using agent-based modeling. Two standard pricing rules are considered and compared (i.e., "discriminatory" and "uniform") and computational experiments, characterized by different inelastic demand level, explore oligopolistic competitions on both quantity and price between learning sellers/producers. Two reinforcement learning algorithms are considered as well--"Marimon and McGrattan" and "Q-learning"--in an attempt to simulate different behavioral types. In particular, greedy sellers (optimizing their instantaneous rewards on a tick-by-tick basis) and inter-temporal optimizing sellers are simulated. Results are interpreted relative to game-theoretical solutions and performance metrics. Nash equilibria in pure strategies and sellers' joint profit maximization are employed to analyze the convergence behavior of the learning algorithms. Furthermore, the difference between payments to suppliers and total generation costs are estimated so as to measure the degree of market inefficiency. Results point out that collusive behaviors are penalized by the discriminatory auction mechanism in low demand scenarios, whereas in a high demand scenario the difference appears to be negligible.
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Suggested Citation

  • Eric Guerci & Stefano Ivaldi & Silvano Cincotti, 2008. "Learning Agents in an Artificial Power Exchange: Tacit Collusion, Market Power and Efficiency of Two Double-auction Mechanisms," Computational Economics, Springer;Society for Computational Economics, vol. 32(1), pages 73-98, September.
  • Handle: RePEc:kap:compec:v:32:y:2008:i:1:p:73-98
    DOI: 10.1007/s10614-008-9127-5
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    References listed on IDEAS

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    1. Junjie Sun & Leigh Tesfatsion, 2007. "Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 291-327, October.
    2. Paul L. Joskow, 2006. "Markets for Power in the United States: An Interim Assessment," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 1-36.
    3. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    4. Kahn, Alfred E. & Cramton, Peter C. & Porter, Robert H. & Tabors, Richard D., 2001. "Uniform Pricing or Pay-as-Bid Pricing: A Dilemma for California and Beyond," The Electricity Journal, Elsevier, vol. 14(6), pages 70-79, July.
    5. Klemperer, Paul D & Meyer, Margaret A, 1989. "Supply Function Equilibria in Oligopoly under Uncertainty," Econometrica, Econometric Society, vol. 57(6), pages 1243-1277, November.
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    Cited by:

    1. Balint, T. & Lamperti, F. & Mandel, A. & Napoletano, M. & Roventini, A. & Sapio, A., 2017. "Complexity and the Economics of Climate Change: A Survey and a Look Forward," Ecological Economics, Elsevier, vol. 138(C), pages 252-265.
    2. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    3. Christopher Boyer & B. Brorsen, 2014. "Implications of a Reserve Price in an Agent-Based Common-Value Auction," Computational Economics, Springer;Society for Computational Economics, vol. 43(1), pages 33-51, January.
    4. Tomas Balint & Francesco Lamperti & Antoine Mandel & Mauro Napoletano & Andrea Roventini & Sandro Sapio, 2017. "Complexity and the economics of climate change : a survey and a look foreward," Sciences Po publications info:hdl:2441/1nlv566svi8, Sciences Po.

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