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Game-theory-based generation maintenance scheduling in electricity markets

Author

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  • Min, C.G.
  • Kim, M.K.
  • Park, J.K.
  • Yoon, Y.T.

Abstract

This paper presents a novel approach to the (generation maintenance scheduling) GMS problem in electricity markets. The main contribution of this study is the modeling of a coordination procedure for an (independent system operator) ISO, based on a game-theoretic framework for the GMS problem. The GMS process of generation companies (Gencos) is designed as a non-cooperative dynamic game, and the Gencos' optimal strategy profile is determined by the Nash equilibrium of the game. The coordination procedure performed by the ISO is characterized by the use of a reliability assessment and a so-called ‘rescheduling signal’. A numerical example for a three-Genco system is used to demonstrate the applicability of the proposed scheme to the GMS problem. The results obtained indicate that the GMS of a profit-oriented Genco can be modified to satisfy the reliability requirements of the ISO.

Suggested Citation

  • Min, C.G. & Kim, M.K. & Park, J.K. & Yoon, Y.T., 2013. "Game-theory-based generation maintenance scheduling in electricity markets," Energy, Elsevier, vol. 55(C), pages 310-318.
    • Handle: RePEc:eee:energy:v:55:y:2013:i:c:p:310-318
    DOI: 10.1016/j.energy.2013.03.060
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    Cited by:

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    4. Mazidi, Peyman & Tohidi, Yaser & Ramos, Andres & Sanz-Bobi, Miguel A., 2018. "Profit-maximization generation maintenance scheduling through bi-level programming," European Journal of Operational Research, Elsevier, vol. 264(3), pages 1045-1057.
    5. Froger, Aurélien & Gendreau, Michel & Mendoza, Jorge E. & Pinson, Éric & Rousseau, Louis-Martin, 2016. "Maintenance scheduling in the electricity industry: A literature review," European Journal of Operational Research, Elsevier, vol. 251(3), pages 695-706.
    6. Srinivasan, Dipti & Rajgarhia, Sanjana & Radhakrishnan, Bharat Menon & Sharma, Anurag & Khincha, H.P., 2017. "Game-Theory based dynamic pricing strategies for demand side management in smart grids," Energy, Elsevier, vol. 126(C), pages 132-143.
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    8. Rokhforoz, Pegah & Montazeri, Mina & Fink, Olga, 2023. "Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    9. Rokhforoz, Pegah & Gjorgiev, Blazhe & Sansavini, Giovanni & Fink, Olga, 2021. "Multi-agent maintenance scheduling based on the coordination between central operator and decentralized producers in an electricity market," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    10. Motalleb, Mahdi & Annaswamy, Anuradha & Ghorbani, Reza, 2018. "A real-time demand response market through a repeated incomplete-information game," Energy, Elsevier, vol. 143(C), pages 424-438.
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    12. Huiru Zhao & Yuwei Wang & Mingrui Zhao & Qingkun Tan & Sen Guo, 2017. "Day-Ahead Market Modeling for Strategic Wind Power Producers under Robust Market Clearing," Energies, MDPI, vol. 10(7), pages 1-27, July.
    13. Zhou, Dengji & Yu, Ziqiang & Zhang, Huisheng & Weng, Shilie, 2016. "A novel grey prognostic model based on Markov process and grey incidence analysis for energy conversion equipment degradation," Energy, Elsevier, vol. 109(C), pages 420-429.
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