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Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units

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  • Rokhforoz, Pegah
  • Montazeri, Mina
  • Fink, Olga

Abstract

This paper proposes a safe reinforcement learning algorithm for generation bidding decisions and unit maintenance scheduling in a competitive electricity market environment. In this problem, each unit aims to find a bidding strategy that maximizes its revenue while concurrently retaining its reliability by scheduling preventive maintenance. The maintenance scheduling provides some safety constraints which should be satisfied at all times. Meeting the critical safety and reliability requirements when the generation units have incomplete information regarding each other’s bidding strategy is a challenging problem. Bi-level optimization and reinforcement learning are state-of-the-art approaches for solving this type of problem. However, neither bi-level optimization nor reinforcement learning can handle the challenges of incomplete information and critical safety constraints. To tackle these challenges, we propose the safe deep deterministic policy gradient reinforcement learning algorithm, which is based on a combination of reinforcement learning and a predicted safety filter. The case study demonstrates that the proposed approach can yield a higher profit compared to other state-of-the-art methods while concurrently satisfying the system safety constraints. Moreover, the case study shows that the reward of the learning algorithm with incomplete information can converge to a reward of the complete information game.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:reensy:v:232:y:2023:i:c:s0951832022006962
    DOI: 10.1016/j.ress.2022.109081
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    References listed on IDEAS

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    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. BULUT, Merve & ÖZCAN, Evrencan, 2021. "A new approach to determine maintenance periods of the most critical hydroelectric power plant equipment," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    3. Volkanovski, Andrija & Mavko, Borut & Boševski, Tome & Čauševski, Anton & Čepin, Marko, 2008. "Genetic algorithm optimisation of the maintenance scheduling of generating units in a power system," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 779-789.
    4. 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.
    5. Mohammadi, Reza & He, Qing, 2022. "A deep reinforcement learning approach for rail renewal and maintenance planning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    6. 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).
    7. Zhou, Yifan & Li, Bangcheng & Lin, Tian Ran, 2022. "Maintenance optimisation of multicomponent systems using hierarchical coordinated reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    8. Jagtap, Hanumant P. & Bewoor, Anand K. & Kumar, Ravinder & Ahmadi, Mohammad Hossein & Chen, Lingen, 2020. "Performance analysis and availability optimization to improve maintenance schedule for the turbo-generator subsystem of a thermal power plant using particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    9. Ishai Menache & Shie Mannor & Nahum Shimkin, 2005. "Basis Function Adaptation in Temporal Difference Reinforcement Learning," Annals of Operations Research, Springer, vol. 134(1), pages 215-238, February.
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    Cited by:

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    2. Yang, Sen & Zhang, Yi & Lu, Xinzheng & Guo, Wei & Miao, Huiquan, 2024. "Multi-agent deep reinforcement learning based decision support model for resilient community post-hazard recovery," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

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