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

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

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. Th...

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Bibliographic Details
Main Authors: Tianhao Zhang, Zhonghua Cheng, Zhe Dong, Xiaojin Huang
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Energies
Subjects:
nuclear steam supply system
coordinated control optimization
multi-agent reinforcement learning
thermal power
outlet steam temperature
Online Access:https://www.mdpi.com/1996-1073/18/9/2223
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