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Development of a virtual power market model to investigate strategic and collusive behavior of market players

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  • Shafie-khah, Miadreza
  • Parsa Moghaddam, Mohsen
  • Sheikh-El-Eslami, Mohamad Kazem

Abstract

In this paper, a virtual power market model is proposed to investigate the behavior of power market players from regulator's point of view. In this approach, strategic players are modeled in a multi-agent environment. These agents which are virtual representative of actual players forecast the prices and participate in the markets, exactly the same as real world situation. In addition, the role of ISO is encountered by using security constraint unit commitment (SCUC) and security constraint economic dispatch (SCED) solutions. Moreover, the interaction between market players is modeled using a heuristic dynamic game theory algorithm based on the supply function equilibria (SFE). In addition to the collusive behavior, using the proposed model, the short-term strategic behavior of players, which their effects will appear in long-term, can be simulated.

Suggested Citation

  • Shafie-khah, Miadreza & Parsa Moghaddam, Mohsen & Sheikh-El-Eslami, Mohamad Kazem, 2013. "Development of a virtual power market model to investigate strategic and collusive behavior of market players," Energy Policy, Elsevier, vol. 61(C), pages 717-728.
    • Handle: RePEc:eee:enepol:v:61:y:2013:i:c:p:717-728
    DOI: 10.1016/j.enpol.2013.06.003
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    3. Kasaei, Mohammad Javad & Gandomkar, Majid & Nikoukar, Javad, 2017. "Optimal management of renewable energy sources by virtual power plant," Renewable Energy, Elsevier, vol. 114(PB), pages 1180-1188.
    4. Przemysław Kaszyński & Aleksandra Komorowska & Jacek Kamiński, 2023. "Revisiting Market Power in the Polish Power System," Energies, MDPI, vol. 16(13), pages 1-23, June.
    5. Tascikaraoglu, A. & Erdinc, O. & Uzunoglu, M. & Karakas, A., 2014. "An adaptive load dispatching and forecasting strategy for a virtual power plant including renewable energy conversion units," Applied Energy, Elsevier, vol. 119(C), pages 445-453.
    6. Rode, David C. & Fischbeck, Paul S., 2018. "Reduced-form models for power market risk analysis," Applied Energy, Elsevier, vol. 228(C), pages 1640-1655.
    7. Wu, Zhongqun & Sun, Hongxia, 2015. "Behavior of Chinese enterprises in evaluating wind power projects: A review based on survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 133-142.
    8. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2017. "A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 341-363.
    9. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2016. "Stochastic profit-based scheduling of industrial virtual power plant using the best demand response strategy," Applied Energy, Elsevier, vol. 164(C), pages 590-606.
    10. Liu, HuiHui & Zhang, ZhongXiang & Chen, Zhan-Ming & Dou, DeSheng, 2019. "The impact of China's electricity price deregulation on coal and power industries: Two-stage game modeling," Energy Policy, Elsevier, vol. 134(C).
    11. Brown, David P. & Eckert, Andrew & Silveira, Douglas, 2023. "Screening for collusion in wholesale electricity markets: A literature review," Utilities Policy, Elsevier, vol. 85(C).

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