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Stochastic short-term maintenance scheduling of GENCOs in an oligopolistic electricity market

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  • Fotouhi Ghazvini, Mohammad Ali
  • Canizes, Bruno
  • Vale, Zita
  • Morais, Hugo

Abstract

In the proposed model, the independent system operator (ISO) provides the opportunity for maintenance outage rescheduling of generating units before each short-term (ST) time interval. Long-term (LT) scheduling for 1 or 2years in advance is essential for the ISO and the generation companies (GENCOs) to decide their LT strategies; however, it is not possible to be exactly followed and requires slight adjustments. The Cournot-Nash equilibrium is used to characterize the decision-making procedure of an individual GENCO for ST intervals considering the effective coordination with LT plans. Random inputs, such as parameters of the demand function of loads, hourly demand during the following ST time interval and the expected generation pattern of the rivals, are included as scenarios in the stochastic mixed integer program defined to model the payoff-maximizing objective of a GENCO. Scenario reduction algorithms are used to deal with the computational burden. Two reliability test systems were chosen to illustrate the effectiveness of the proposed model for the ST decision-making process for future planned outages from the point of view of a GENCO.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:101:y:2013:i:c:p:667-677
    DOI: 10.1016/j.apenergy.2012.07.009
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    References listed on IDEAS

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    Cited by:

    1. Sadeghian, Omid & Mohammadpour Shotorbani, Amin & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Risk-averse maintenance scheduling of generation units in combined heat and power systems with demand response," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. 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.
    3. 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).
    4. Canizes, Bruno & Soares, João & Faria, Pedro & Vale, Zita, 2013. "Mixed integer non-linear programming and Artificial Neural Network based approach to ancillary services dispatch in competitive electricity markets," Applied Energy, Elsevier, vol. 108(C), pages 261-270.
    5. 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.
    6. 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.
    7. Motalleb, Mahdi & Ghorbani, Reza, 2017. "Non-cooperative game-theoretic model of demand response aggregator competition for selling stored energy in storage devices," Applied Energy, Elsevier, vol. 202(C), pages 581-596.

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