Possibilities of reinforcement learning for nuclear power plants: Evidence on current applications and beyond

• Published in: Nuclear engineering and technology 2024, 56(6), , pp.1959-1974
• Main Authors: Gong, Aicheng, Chen, Yangkun, Zhang, Junjie, Li, Xiu
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Complex system; Control; Nuclear Power Plant; Reinforcement learning; 원자력공학
• ISSN: 1738-5733; 2234-358X
• DOI: 10.1016/j.net.2024.01.003

Nuclear energy plays a crucial role in energy supply in the 21st century, and more and more Nuclear Power Plants (NPPs) will be in operation to contribute to the development of human society. However, as a typical complex system engineering, the operation and development of NPPs require efficient and stable control methods to ensure the safety and efficiency of nuclear power generation. Reinforcement learning (RL) aims at learning optimal control policies via maximizing discounted long-term rewards. The reward-oriented learning paradigm has witnessed remarkable success in many complex systems, such as wind power systems, electric power systems, coal fire power plants, robotics, etc. In this work, we try to present a systematic review of the applications of RL on these complex systems, from which we believe NPPs can borrow experience and insights. We then conduct a block-by-block investigation on the application scenarios of specific tasks in NPPs and carried out algorithmic research for different situations such as power startup, collaborative control, and emergency handling. Moreover, we discuss the possibilities of further application of RL methods on NPPs and detail the challenges when applying RL methods on NPPs. We hope this work can boost the realization of intelligent NPPs, and contribute to more and more research on how to better integrate RL algorithms into NPPs.