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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

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  • Sueyoshi, Toshiyuki

Abstract

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.

Suggested Citation

  • Sueyoshi, Toshiyuki, 2010. "An agent-based approach with collaboration among agents: Estimation of wholesale electricity price on PJM and artificial data generated by a mean reverting model," Energy Economics, Elsevier, vol. 32(5), pages 1025-1033, September.
    • Handle: RePEc:eee:eneeco:v:32:y:2010:i:5:p:1025-1033
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    References listed on IDEAS

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    Cited by:

    1. Sueyoshi, Toshiyuki & Goto, Mika & Sugiyama, Manabu, 2013. "DEA window analysis for environmental assessment in a dynamic time shift: Performance assessment of U.S. coal-fired power plants," Energy Economics, Elsevier, vol. 40(C), pages 845-857.
    2. Sueyoshi, Toshiyuki & Goto, Mika, 2017. "Measurement of returns to scale on large photovoltaic power stations in the United States and Germany," Energy Economics, Elsevier, vol. 64(C), pages 306-320.
    3. Lin, Boqiang & Wang, Yao, 2020. "Analyzing the elasticity and subsidy to reform the residential electricity tariffs in China," International Review of Economics & Finance, Elsevier, vol. 67(C), pages 189-206.
    4. Sueyoshi, Toshiyuki & Wang, Derek, 2017. "Measuring scale efficiency and returns to scale on large commercial rooftop photovoltaic systems in California," Energy Economics, Elsevier, vol. 65(C), pages 389-398.
    5. Sueyoshi, Toshiyuki & Goto, Mika, 2014. "Photovoltaic power stations in Germany and the United States: A comparative study by data envelopment analysis," Energy Economics, Elsevier, vol. 42(C), pages 271-288.
    6. Sueyoshi, Toshiyuki & Goto, Mika, 2012. "Environmental assessment by DEA radial measurement: U.S. coal-fired power plants in ISO (Independent System Operator) and RTO (Regional Transmission Organization)," Energy Economics, Elsevier, vol. 34(3), pages 663-676.
    7. Sueyoshi, Toshiyuki & Goto, Mika, 2015. "Environmental assessment on coal-fired power plants in U.S. north-east region by DEA non-radial measurement," Energy Economics, Elsevier, vol. 50(C), pages 125-139.
    8. Young, David & Poletti, Stephen & Browne, Oliver, 2014. "Can agent-based models forecast spot prices in electricity markets? Evidence from the New Zealand electricity market," Energy Economics, Elsevier, vol. 45(C), pages 419-434.
    9. Goto, Hisanori & Goto, Mika & Sueyoshi, Toshiyuki, 2011. "Consumer choice on ecologically efficient water heaters: Marketing strategy and policy implications in Japan," Energy Economics, Elsevier, vol. 33(2), pages 195-208, March.
    10. Sun, Chuanwang & Lin, Boqiang, 2013. "Reforming residential electricity tariff in China: Block tariffs pricing approach," Energy Policy, Elsevier, vol. 60(C), pages 741-752.

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