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Coordinated Control Optimization of Nuclear Steam Supply Systems via Multi-Agent Reinforcement Learning

Author

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  • Tianhao Zhang

    (Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advanced Nuclear Energy Technology of China, Tsinghua University, Beijing 100084, China)

  • Zhonghua Cheng

    (Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advanced Nuclear Energy Technology of China, Tsinghua University, Beijing 100084, China)

  • Zhe Dong

    (Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advanced Nuclear Energy Technology of China, Tsinghua University, Beijing 100084, China)

  • Xiaojin Huang

    (Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advanced Nuclear Energy Technology of China, Tsinghua University, Beijing 100084, China)

Abstract

Nuclear steam supply systems (NSSSs) are critical to achieving safe, efficient, and flexible nuclear power generation. While deep reinforcement learning (DRL) has shown potential in optimizing NSSS control, existing single-agent approaches apply the same optimization strategies to all subsystems. This simplification ignores subsystem-specific control requirements and limits both optimization efficacy and adaptability. To resolve this gap, we propose a multi-agent reinforcement learning (MARL) framework that independently optimizes each subsystem while ensuring global coordination. Our approach extends the current NSSS optimization framework from a single-agent model to a multi-agent one and introduces a novel MARL method to foster effective exploration and stability during optimization. Experimental findings demonstrate that our method significantly outperforms DRL-based approaches in optimizing thermal power and outlet steam temperature control. This research pioneers the application of MARL to NSSS optimization, paving the way for advanced nuclear power control systems.

Suggested Citation

  • Tianhao Zhang & Zhonghua Cheng & Zhe Dong & Xiaojin Huang, 2025. "Coordinated Control Optimization of Nuclear Steam Supply Systems via Multi-Agent Reinforcement Learning," Energies, MDPI, vol. 18(9), pages 1-13, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:9:p:2223-:d:1643871
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    References listed on IDEAS

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    1. Jiang, Di & Dong, Zhe, 2020. "Dynamic matrix control for thermal power of multi-modular high temperature gas-cooled reactor plants," Energy, Elsevier, vol. 198(C).
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