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

  • Deddy Koesrindartoto

    ()

    (Economics Iowa State University)

  • Junjie Sun

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.

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2005 with number 50.

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Date of creation: 11 Nov 2005
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Handle: RePEc:sce:scecf5:50
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  1. Kalaba, Robert E. & Tesfatsion, Leigh S., 1991. "Solving Nonlinear Equations By Adaptive Homotopy Continuation," Staff General Research Papers 11186, Iowa State University, Department of Economics.
  2. 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.
  3. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, EconWPA, revised 15 Aug 2002.
  4. Kalaba, R. & Tesfatsion, L., 1989. "Nonlocal Automated Sensitivity Analysis," Papers m8911, Southern California - Department of Economics.
  5. Tesfatsion, Leigh, 2001. "Nonlocal Sensitivity Analysis with Automatic Differentiation," Staff General Research Papers 1990, Iowa State University, Department of Economics.
  6. Tesfatsion, Leigh, 1991. "Automatic Evaluation of Higher-Order Partial Derivatives for Nonlocal Sensitivity Analysis," Staff General Research Papers 11183, Iowa State University, Department of Economics.
  7. Severin Borenstein, 2002. "The Trouble With Electricity Markets: Understanding California's Restructuring Disaster," Journal of Economic Perspectives, American Economic Association, vol. 16(1), pages 191-211, Winter.
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