Deep Reinforcement Learning in Power Systems Resilience: A Review

Power systems are well-engineered systems designed to supply power generation and services to end users. They are considered as the most crucial infrastructures in modern societies as numerous facilities such as telecommunication, transportation, public health, and emergency services heavily rely on a continuous power supply for their normal operation. The complex structure and interconnection with other facilities make power systems vulnerable to external threats such as natural disasters, extreme weather, and vandalism. On the other hand, with the advancements of artificial intelligence in the recent years, various cutting-edge control and optimization methods are emerged. Among them, deep reinforcement learning (DRL) is the most promoted for enhancing power system resilience. In this context, this article performs an in-depth review of various DRL methods and their applications in resilience optimization and enhancement of power systems. The review offers a structured examination of DRL methodology, along with a systematic categorization of existing literature into three themes (proactive actions, emergency response, and restoration and recovery) according to their contingency stages. The strengths and limitations of DRL-based resilience enhancement strategies are discussed across robustness, scalability, interpretability, and safety. A research roadmap is provided to highlight possible avenues for further exploration.