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Dynamic Testing of Wholesale Power Market Designs: An Open-Source Agent-Based Framework

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  • Junjie Sun

    ()

  • Leigh Tesfatsion

    ()

Abstract

In April 2003 the U.S. Federal Energy Regulatory Commission proposed a complicated market design - the Wholesale Power Market Platform (WPMP) ï¾– for common adoption by all U.S. wholesale power markets. Versions of the WPMP have been implemented in New England, New York, the mid-Atlantic states, the Midwest, and the Southwest, and California. Strong opposition to the WPMP persists among some industry stakeholders, however, due largely to a perceived lack of adequate performance testing. This study reports on the model development and open-source implementation (in Java) of a computational wholesale power market organized in accordance with core WPMP features and operating over a realistically rendered transmission grid subject to congestion effects. The traders within this market model are strategic profit-seeking agents whose learning behaviors are based on data from human-subject experiments. Our key experimental focus is the complex interplay among structural conditions, market protocols, and learning behaviors in relation to short-term and longer-term market performance. Findings for a dynamic 5-node transmission grid test case are presented for concrete illustration. Annotated pointers to related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/AMESMarketHome.htm

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File URL: http://hdl.handle.net/10.1007/s10614-007-9095-1
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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 30 (2007)
Issue (Month): 3 (October)
Pages: 291-327

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Handle: RePEc:kap:compec:v:30:y:2007:i:3:p:291-327

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Web page: http://www.springerlink.com/link.asp?id=100248
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Keywords: Wholesale power market restructuring; Empirical input validation; Market design; Behavioral economics; Learning; Market power; Agent-based modeling; AMES wholesale power market framework; Java; RepastJ; L1; D8; L9; C6;

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References

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  1. Paul Windrum & Giorgio Fagiolo & Alessio Moneta, 2007. "Empirical Validation of Agent-Based Models: Alternatives and Prospects," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 8.
  2. James Nicolaisen & Valentin Petrov & Leigh Tesfatsion, 2000. "Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing," Computational Economics, EconWPA 0004005, EconWPA.
  3. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics, EconWPA 0203001, EconWPA, revised 15 Aug 2002.
  4. Sun, Junjie, 2005. "U.S. Financial Transmission Rights: Theory and Practice," Staff General Research Papers 12266, Iowa State University, Department of Economics.
  5. Paul L. Joskow, 2006. "Markets for Power in the United States: An Interim Assessment," The Energy Journal, International Association for Energy Economics, International Association for Energy Economics, vol. 0(Number 1), pages 1-36.
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  7. Widergren, Steven & Sun, Junjie & Tesfatsion, Leigh S., 2006. "Market Design Test Environments," Staff General Research Papers 12516, Iowa State University, Department of Economics.
  8. Sun, Junjie & Tesfatsion, Leigh, 2006. "DC Optimal Power Flow Formulation and Solution Using QuadProgJ," Staff General Research Papers 12558, Iowa State University, Department of Economics.
  9. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
  10. Bower, John & Bunn, Derek, 2001. "Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and Wales electricity market," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 25(3-4), pages 561-592, March.
  11. Axelrod, Robert & Tesfatsion, Leigh, 2006. "A Guide for Newcomers to Agent-Based Modeling in the Social Sciences," Staff General Research Papers 12515, Iowa State University, Department of Economics.
  12. Deddy Koesrindartoto & Junjie Sun, 2005. "An Agent-Based Computational Laboratory for Testing the Economic Reliability of Wholesale Power Market Designs," Computing in Economics and Finance 2005, Society for Computational Economics 50, Society for Computational Economics.
  13. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers 10368, Iowa State University, Department of Economics.
  14. Sun, Junjie & Tesfatsion, Leigh S., 2007. "Open-Source Software for Power Industry Research, Teaching, and Training: A DC Optimal Power Flow Illustration," Staff General Research Papers 12775, Iowa State University, Department of Economics.
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Citations

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Cited by:
  1. Jean-Luc Gaffard & Mauro Napoletano, 2012. "Agent-based models and economic policy," Sciences Po publications info:hdl:2441/53r60a8s3ku, Sciences Po.
  2. Anke Weidlich & Daniel Veit, 2008. "Agent-Based Simulations for Electricity Market Regulation Advice: Procedures and an Example," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 228(2+3), pages 149-172, June.
  3. 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, Society for Computational Economics, Society for Computational Economics, vol. 32(1), pages 73-98, September.
  4. Li, Hongyan & Sun, Junjie & Tesfatsion, Leigh S., 2009. "Separation and Volatility of Locational Marginal Prices in Restructured Wholesale Power Markets," Staff General Research Papers 13075, Iowa State University, Department of Economics.
  5. Rahimiyan, Morteza & Rajabi Mashhadi, Habib, 2010. "Evaluating the efficiency of divestiture policy in promoting competitiveness using an analytical method and agent-based computational economics," Energy Policy, Elsevier, vol. 38(3), pages 1588-1595, March.
  6. Albert Banal-Estañol & Augusto Rupérez-Micola, 2010. "Are agent-based simulations robust? The wholesale electricity trading case," Economics Working Papers 1214, Department of Economics and Business, Universitat Pompeu Fabra.
  7. Rubin, Ofir D. & Babcock, Bruce A., 2011. "A novel approach for modeling deregulated electricity markets," Energy Policy, Elsevier, vol. 39(5), pages 2711-2721, May.
  8. Block, C.A. & Collins, J. & Ketter, W. & Weinhardt, C., 2009. "A Multi-Agent Energy Trading Competition," ERIM Report Series Research in Management, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasm ERS-2009-054-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  9. Ketter, Wolfgang & Collins, John & Reddy, Prashant, 2013. "Power TAC: A competitive economic simulation of the smart grid," Energy Economics, Elsevier, Elsevier, vol. 39(C), pages 262-270.
  10. Blake LeBaron & Peter Winker, 2008. "Introduction to the Special Issue on Agent-Based Models for Economic Policy Advice," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 228(2+3), pages 141-148, June.
  11. Giorgio Fagiolo & Andrea Roventini, 2008. "On the Scientific Status of Economic Policy: A Tale of Alternative Paradigms," LEM Papers Series 2008/03, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  12. Yu, Nanpeng & Liu, Chen-Ching & Tesfatsion, Leigh S., 2008. "Modeling of Suppliers Learning Behaviors in an Electricity Market Environment," Staff General Research Papers 12976, Iowa State University, Department of Economics.
  13. Anna Kowalska-Pyzalska & Katarzyna Maciejowska & Katarzyna Sznajd-Weron & Rafal Weron, 2013. "Going green: Agent-based modeling of the diffusion of dynamic electricity tariffs," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Technology HSC/13/05, Hugo Steinhaus Center, Wroclaw University of Technology.
  14. repec:spo:wpecon:info:hdl:2441/53r60a8s3kup1vc9l564igg8g is not listed on IDEAS
  15. Weidlich, Anke & Veit, Daniel, 2008. "A critical survey of agent-based wholesale electricity market models," Energy Economics, Elsevier, Elsevier, vol. 30(4), pages 1728-1759, July.
  16. Banal-Estañol, Albert & Rupérez Micola, Augusto, 2011. "Behavioural simulations in spot electricity markets," European Journal of Operational Research, Elsevier, Elsevier, vol. 214(1), pages 147-159, October.
  17. Azadeh, A. & Skandari, M.R. & Maleki-Shoja, B., 2010. "An integrated ant colony optimization approach to compare strategies of clearing market in electricity markets: Agent-based simulation," Energy Policy, Elsevier, vol. 38(10), pages 6307-6319, October.

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