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

Tianhao Zhang (), Zhonghua Cheng, Zhe Dong () and Xiaojin Huang
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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

Energies, 2025, vol. 18, issue 9, 1-13

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.

Keywords: nuclear steam supply system; coordinated control optimization; multi-agent reinforcement learning; thermal power; outlet steam temperature (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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