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The AMES Wholesale Power Market Test Bed: A Computational Laboratory for Research, Teaching, and Training


  • Li, Hongyan
  • Tesfatsion, Leigh S.


Wholesale power markets around the world are currently undergoing a controversial restructuring of their architecture and rules of operation. Some commentators have argued that restructuring has not produced the intended improvements in market efficiency while at the same time it has complicated efforts to ensure reliability and fairness of operations. This situation suggests the desirability of having publicly available test beds suitable for the objective study of this restructuring process. This study reports on the AMES Wholesale Power Market Test Bed. AMES is an open-source agent-based computational laboratory designed for the systematic study of restructured wholesale power markets operating over AC transmission grids subject to congestion. The AMES traders have learning capabilities permitting them to evolve their trading strategies over time. The potential usefulness of AMES for research, teaching, and training purposes is discussed and illustrated. AMES downloads, tutorials, and publications can be accessed at the AMES homepage at

Suggested Citation

  • Li, Hongyan & Tesfatsion, Leigh S., 2009. "The AMES Wholesale Power Market Test Bed: A Computational Laboratory for Research, Teaching, and Training," Staff General Research Papers Archive 13073, Iowa State University, Department of Economics.
  • Handle: RePEc:isu:genres:13073

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    Cited by:

    1. Zhao, Huan, 2011. "Four Market Studies for the Beef and Electric Power Industries," ISU General Staff Papers 201101010800001360, Iowa State University, Department of Economics.
    2. Li, Gong & Shi, Jing, 2012. "Agent-based modeling for trading wind power with uncertainty in the day-ahead wholesale electricity markets of single-sided auctions," Applied Energy, Elsevier, vol. 99(C), pages 13-22.
    3. 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.
    4. Haghnevis, Moeed & Askin, Ronald G. & Armbruster, Dieter, 2016. "An agent-based modeling optimization approach for understanding behavior of engineered complex adaptive systems," Socio-Economic Planning Sciences, Elsevier, vol. 56(C), pages 67-87.

    More about this item


    Wholesale power markets; restructuring; Locational marginal pricing; Agent-based test bed; AMES;

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory
    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • D6 - Microeconomics - - Welfare Economics
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L2 - Industrial Organization - - Firm Objectives, Organization, and Behavior
    • L3 - Industrial Organization - - Nonprofit Organizations and Public Enterprise
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy


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