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Development of Open Source Software for Power Market Research: The AMES Test Bed


  • Li, Hongyan
  • Tesfatsion, Leigh S.


Open Source Software (OSS) expresses the idea that developers should be able to license the publication of their software in a manner permitting anyone to freely use, modify, and distribute the software. Today OSS is widely used in the software industry, such as for language development tools (e.g., NetBeans for Java), office document processors (e.g., OpenOffice), and operating systems (e.g., Linux, OpenSolaris).Yet OSS has been slow to penetrate the power industry; heavy reliance is still placed on closed-source commercial software packages. The OSS in use tends to be for specialized purposes (e.g., circuit design) rather than for the general-purpose analysis of power systems. This study discusses potential benefits and drawbacks of developing OSS for power market research, using the AMES Wholesale Power Market Test Bed for concrete illustration. AMES downloads, tutorials, and research publications can be accesssed at

Suggested Citation

  • Li, Hongyan & Tesfatsion, Leigh S., 2009. "Development of Open Source Software for Power Market Research: The AMES Test Bed," Staff General Research Papers Archive 13069, Iowa State University, Department of Economics.
  • Handle: RePEc:isu:genres:13069

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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. Silva, Francisco & Teixeira, Brígida & Pinto, Tiago & Santos, Gabriel & Vale, Zita & Praça, Isabel, 2016. "Generation of realistic scenarios for multi-agent simulation of electricity markets," Energy, Elsevier, vol. 116(P1), pages 128-139.
    3. repec:gam:jeners:v:10:y:2017:i:7:p:883-:d:103217 is not listed on IDEAS
    4. Pinto, T. & Morais, H. & Oliveira, P. & Vale, Z. & Praça, I. & Ramos, C., 2011. "A new approach for multi-agent coalition formation and management in the scope of electricity markets," Energy, Elsevier, vol. 36(8), pages 5004-5015.
    5. Li, Hongyan & Tesfatsion, Leigh, 2012. "Co-learning patterns as emergent market phenomena: An electricity market illustration," Journal of Economic Behavior & Organization, Elsevier, vol. 82(2), pages 395-419.
    6. Pinto, Tiago & Vale, Zita & Sousa, Tiago M. & Praça, Isabel, 2015. "Negotiation context analysis in electricity markets," Energy, Elsevier, vol. 85(C), pages 78-93.
    7. Santos, Gabriel & Pinto, Tiago & Praça, Isabel & Vale, Zita, 2016. "MASCEM: Optimizing the performance of a multi-agent system," Energy, Elsevier, vol. 111(C), pages 513-524.
    8. Krishnamurthy, Dheepak & Li, Wanning & Tesfatsion, Leigh, 2016. "An 8-Zone Test System Based on ISO New England Data: Development and Application," ISU General Staff Papers 201601010800001449, Iowa State University, Department of Economics.
    9. Tadahiro Taniguchi & Koki Kawasaki & Yoshiro Fukui & Tomohiro Takata & Shiro Yano, 2015. "Automated Linear Function Submission-Based Double Auction as Bottom-up Real-Time Pricing in a Regional Prosumers’ Electricity Network," Energies, MDPI, Open Access Journal, vol. 8(7), pages 1-26, July.
    10. Voudouris, Vlasios & Stasinopoulos, Dimitrios & Rigby, Robert & Di Maio, Carlo, 2011. "The ACEGES laboratory for energy policy: Exploring the production of crude oil," Energy Policy, Elsevier, vol. 39(9), pages 5480-5489, September.
    11. Gabriel Santos & Tiago Pinto & Isabel Praça & Zita Vale, 2016. "An Interoperable Approach for Energy Systems Simulation: Electricity Market Participation Ontologies," Energies, MDPI, Open Access Journal, vol. 9(11), pages 1-22, October.
    12. Ge, Jiaqi, 2014. "Stepping into new territory: Three essays on agent-based computational economics and environmental economics," ISU General Staff Papers 201401010800004899, Iowa State University, Department of Economics.
    13. Tiago Pinto & Zita Vale & Isabel Praça & E. J. Solteiro Pires & Fernando Lopes, 2015. "Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning," Energies, MDPI, Open Access Journal, vol. 8(9), pages 1-26, September.

    More about this item


    Agent-based test bed; Wholesale electric power markets; open-source software; AMES software package;

    JEL classification:

    • B4 - Schools of Economic Thought and Methodology - - Economic Methodology
    • 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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