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An Agent-Based Bidding Simulation Framework to Recognize Monopoly Behavior in Power Markets

Author

Listed:
  • Ye He

    (Nanjing Vocational Institute of Transport Technology, Nanjing 211188, China)

  • Siming Guo

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Yu Wang

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Yujia Zhao

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Weidong Zhu

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Fangyuan Xu

    (Department of Electrical Engineering, School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Chun Sing Lai

    (Brunel Interdisciplinary Power Systems Research Centre, Department of Electronic and Electrical Engineering, Brunel University London, Kingston Lane, London UB8 3PH, UK)

  • Ahmed F. Zobaa

    (Brunel Interdisciplinary Power Systems Research Centre, Department of Electronic and Electrical Engineering, Brunel University London, Kingston Lane, London UB8 3PH, UK)

Abstract

Although many countries prefer deregulated power markets as a means of containing power costs, a monopoly may still exist. In this study, an agent-based bidding simulation framework is proposed to detect whether there will be a monopoly in the power market. A security-constrained unit commitment (SCUC) is conducted to clear the power market. Using the characteristics that the agent can fully explore in a certain environment and the Q-learning algorithm, each power producer in the power market is modeled as an agent, and the agent selects a quotation strategy that can improve profits based on historical bidding information. The numerical results show that in a power market with monopoly potential among the power producers, the profits of the power producers will not converge, and the locational marginal price will eventually become unacceptable. Whereas, in a power market without monopoly potential, power producers will maintain competition and the market remains active and healthy.

Suggested Citation

  • Ye He & Siming Guo & Yu Wang & Yujia Zhao & Weidong Zhu & Fangyuan Xu & Chun Sing Lai & Ahmed F. Zobaa, 2022. "An Agent-Based Bidding Simulation Framework to Recognize Monopoly Behavior in Power Markets," Energies, MDPI, vol. 16(1), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:434-:d:1020384
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    References listed on IDEAS

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    1. Makkonen, Mari & Pätäri, Satu & Jantunen, Ari & Viljainen, Satu, 2012. "Competition in the European electricity markets – outcomes of a Delphi study," Energy Policy, Elsevier, vol. 44(C), pages 431-440.
    2. Fotouhi Ghazvini, Mohammad Ali & Canizes, Bruno & Vale, Zita & Morais, Hugo, 2013. "Stochastic short-term maintenance scheduling of GENCOs in an oligopolistic electricity market," Applied Energy, Elsevier, vol. 101(C), pages 667-677.
    3. 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.
    4. Haas, R. & Auer, H., 2006. "The prerequisites for effective competition in restructured wholesale electricity markets," Energy, Elsevier, vol. 31(6), pages 857-864.
    5. Brooks, Adria E. & Lesieutre, Bernard C., 2022. "A locational marginal price for frequency balancing operations in regulation markets," Applied Energy, Elsevier, vol. 308(C).
    6. Hesamzadeh, Mohammad R. & Biggar, Darryl R. & Hosseinzadeh, Nasser, 2011. "The TC-PSI indicator for forecasting the potential for market power in wholesale electricity markets," Energy Policy, Elsevier, vol. 39(10), pages 5988-5998, October.
    7. Arellano, M. Soledad & Serra, Pablo, 2008. "The competitive role of the transmission system in price-regulated power industries," Energy Economics, Elsevier, vol. 30(4), pages 1568-1576, July.
    8. Ding, Jie & Xu, Yujie & Chen, Haisheng & Sun, Wenwen & Hu, Shan & Sun, Shuang, 2019. "Value and economic estimation model for grid-scale energy storage in monopoly power markets," Applied Energy, Elsevier, vol. 240(C), pages 986-1002.
    9. Aliabadi, Danial Esmaeili & Kaya, Murat & Şahin, Güvenç, 2017. "An agent-based simulation of power generation company behavior in electricity markets under different market-clearing mechanisms," Energy Policy, Elsevier, vol. 100(C), pages 191-205.
    10. Xu, Fang Yuan & Tang, Rui Xin & Xu, Si Bin & Fan, Yi Liang & Zhou, Ya & Zhang, Hao Tian, 2021. "Neural network-based photovoltaic generation capacity prediction system with benefit-oriented modification," Energy, Elsevier, vol. 223(C).
    11. Rabindra Nepal & Antonio Carvalho & John Foster, 2016. "Revisiting electricity liberalization and quality of service: empirical evidence from New Zealand," Applied Economics, Taylor & Francis Journals, vol. 48(25), pages 2309-2320, May.
    12. Viehmann, Johannes & Lorenczik, Stefan & Malischek, Raimund, 2021. "Multi-unit multiple bid auctions in balancing markets: An agent-based Q-learning approach," Energy Economics, Elsevier, vol. 93(C).
    13. Poursalimi Jaghargh, Mohammad Javad & Mashhadi, Habib Rajabi, 2021. "An analytical approach to estimate structural and behavioral impact of renewable energy power plants on LMP," Renewable Energy, Elsevier, vol. 163(C), pages 1012-1022.
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