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An Agent-Based Computational Laboratory for Testing the Economic Reliability of Wholesale Power Market Designs

Author

Listed:
  • Deddy Koesrindartoto

    () (Economics Iowa State University)

  • Junjie Sun

Abstract

In April 2003 the U.S. Federal Energy Regulatory Commission proposed the Wholesale Power Market Platform (WPMP) for common adoption by all U.S. wholesale power markets. The WPMP is a complicated market design envisioning day-ahead, real-time, and ancillary service markets maintained and operated by an independent system operator or regional transmission organization. Variants of the WPMP have been implemented or accepted for implementation in several regions of the U.S. However, strong opposition to the WPMP still persists in many regions due in part to a perceived lack of adequate reliability testing. This presentation will report on the development of an agent-based computational laboratory for testing the economic reliability of the WPMP market design. The computational laboratory incorporates several core elements of the WPMP design as actually implemented in March 2003 by the New England independent system operator (ISO-NE) for the New England wholesale power market. Specifically, our modeled wholesale power market operates over a realistically rendered AC transmission grid. Computationally rendered generator agents (bulk electricity sellers) and load-serving entity agents (bulk electricity buyers) repeatedly bid into the day-ahead and real-time markets using the same protocols as actual ISO-NE market participants. In each trading period the agents use reinforcement learning to update their bids on the basis of past experience. We are using our agent-based computational laboratory to test the extent to which the core WPMP protocols are capable of sustaining efficient, orderly, and fair market outcomes over time despite attempts by market participants to gain individual advantage through strategic pricing, capacity withholding, and induced transmission congestion. This presentation will report on some of our initial experimental findings.

Suggested Citation

  • 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 50, Society for Computational Economics.
  • Handle: RePEc:sce:scecf5:50
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    References listed on IDEAS

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    1. Tesfatsion, Leigh, 2001. "Nonlocal Sensitivity Analysis with Automatic Differentiation," Staff General Research Papers Archive 1990, Iowa State University, Department of Economics.
    2. Kalaba, R. & Tesfatsion, L., 1989. "Nonlocal Automated Sensitivity Analysis," Papers m8911, Southern California - Department of Economics.
    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. Tesfatsion, Leigh, 1991. "Automatic Evaluation of Higher-Order Partial Derivatives for Nonlocal Sensitivity Analysis," Staff General Research Papers Archive 11183, Iowa State University, Department of Economics.
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    6. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, University Library of Munich, Germany, revised 15 Aug 2002.
    7. Kalaba, Robert E. & Tesfatsion, Leigh S., 1991. "Solving Nonlinear Equations By Adaptive Homotopy Continuation," Staff General Research Papers Archive 11186, Iowa State University, Department of Economics.
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    Cited by:

    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. Sun, Junjie & Tesfatsion, Leigh, 2006. "DC Optimal Power Flow Formulation and Solution Using QuadProgJ," Working Papers 18221, Iowa State University, Department of Economics.
    3. Giorgio Fagiolo & Paul Windrum & Alessio Moneta, 2006. "Empirical Validation of Agent Based Models: A Critical Survey," LEM Papers Series 2006/14, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    4. Safarzynska, Karolina & van den Bergh, Jeroen C.J.M., 2011. "Industry evolution, rational agents and the transition to sustainable electricity production," Energy Policy, Elsevier, vol. 39(10), pages 6440-6452, October.
    5. Bernardo Alves Furtado & Isaque Daniel Rocha Eberhardt, 2016. "A Simple Agent-Based Spatial Model of the Economy: Tools for Policy," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(4), pages 1-12.
    6. 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 1-8.
    7. Giorgio Fagiolo & Alessio Moneta & Paul Windrum, 2007. "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 195-226, October.
    8. 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.
    9. Francis Tseng & Fei Liu & Bernardo Alves Furtado, 2017. "Humans of Simulated New York (HOSNY): an exploratory comprehensive model of city life," Papers 1703.05240, arXiv.org, revised Mar 2017.
    10. Zhe Xiao & Tinghua Li & Ming Huang & Jihong Shi & Jingjing Yang & Jiang Yu & Wei Wu, 2010. "Hierarchical MAS Based Control Strategy for Microgrid," Energies, MDPI, Open Access Journal, vol. 3(9), pages 1-17, September.
    11. Imran, Kashif & Hassan, Tehzeebul & Aslam, Muhammad Farooq & Ngan, Hon-Wing & Ahmad, Intesar, 2009. "Simulation analysis of emissions trading impact on a non-utility power plant," Energy Policy, Elsevier, vol. 37(12), pages 5694-5703, December.

    More about this item

    Keywords

    Agent-based computational economics; Wholesale power market design; Learning agents;

    JEL classification:

    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L5 - Industrial Organization - - Regulation and Industrial Policy
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory

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