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An agent-based approach with collaboration among agents: Estimation of wholesale electricity price on PJM and artificial data generated by a mean reverting model

  • Sueyoshi, Toshiyuki
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    This study examines the performance of MAIS (Multi-Agent Intelligent Simulator) equipped with various learning capabilities. In addition to the learning capabilities, the proposed MAIS incorporates collaboration among agents. The proposed MAIS is applied to estimate a dynamic change of wholesale electricity price in PJM (Pennsylvania-New Jersey-Mainland) and an artificial data set generated by a mean reverting model. Using such different types of data sets, the methodological validity of MAIS is confirmed by comparing it with other well-known alternatives in computer science. This study finds that the MAIS needs to incorporate both the mean reverting model and the collaboration behavior among agents in order to enhance its estimation capability. The MAIS discussed in this study will provide research on energy economics with a new numerical capability that can investigate a dynamic change of not only wholesale electricity price but also speculation and learning process of traders.

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    File URL: http://www.sciencedirect.com/science/article/B6V7G-4YPPR2V-1/2/0fd56046ed5f4dde9031d38195a72ece
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    Article provided by Elsevier in its journal Energy Economics.

    Volume (Year): 32 (2010)
    Issue (Month): 5 (September)
    Pages: 1025-1033

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    Handle: RePEc:eee:eneeco:v:32:y:2010:i:5:p:1025-1033
    Contact details of provider: Web page: http://www.elsevier.com/locate/eneco

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    1. Crespo Cuaresma, Jesús & Hlouskova, Jaroslava & Kossmeier, Stephan & Obersteiner, Michael, 2004. "Forecasting electricity spot-prices using linear univariate time-series models," Applied Energy, Elsevier, vol. 77(1), pages 87-106, January.
    2. Alvaro Cartea & Marcelo Figueroa, 2005. "Pricing in Electricity Markets: A Mean Reverting Jump Diffusion Model with Seasonality," Applied Mathematical Finance, Taylor & Francis Journals, vol. 12(4), pages 313-335.
    3. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
    4. Demers, Jean-Guy, 2009. "Multiple zone power forwards: A value at risk framework," Energy Economics, Elsevier, vol. 31(5), pages 714-726, September.
    5. Bask, Mikael & Widerberg, Anna, 2009. "Market structure and the stability and volatility of electricity prices," Energy Economics, Elsevier, vol. 31(2), pages 278-288, March.
    6. Goto, Mika & Tsutsui, Miki, 2008. "Technical efficiency and impacts of deregulation: An analysis of three functions in U.S. electric power utilities during the period from 1992 through 2000," Energy Economics, Elsevier, vol. 30(1), pages 15-38, January.
    7. Sueyoshi, Toshiyuki, 2010. "An agent-based approach equipped with game theory: Strategic collaboration among learning agents during a dynamic market change in the California electricity crisis," Energy Economics, Elsevier, vol. 32(5), pages 1009-1024, September.
    8. Benth, Fred Espen & Koekebakker, Steen, 2008. "Stochastic modeling of financial electricity contracts," Energy Economics, Elsevier, vol. 30(3), pages 1116-1157, May.
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