关键词: 新型电力系统, 强化学习(RL), 深度强化学习(DRL), 智能电网, 优化策略, 能源管理, 态势感知, 优化调度

Abstract: [Objectives]　As power systems evolve toward higher levels of intelligence and automation, reinforcement learning (RL), a key technology in artificial intelligence, shows great potential in the intelligent development of the power sector. Enhancing research methods for RL applications is crucial for fully exploring its potential in power system operation, control, and optimization. By analyzing the performance of RL in practical electrical applications and exploring future research directions, it provides contributing to the intelligent transformation of power systems. [Methods]　This study provides a systematic review of RL applications across diverse fields of electrical engineering. It systematically introduces the fundamental principles and landmark algorithms of RL, detailing how these algorithms are applied to address practical problems in new-type power systems. The study categorizes mainstream RL algorithms in current research and analyzes the advantages and disadvantages of structural improvements made to these algorithms. [Results]　Compared to traditional algorithms, RL significantly enhances the intelligence level of new-type power systems. It achieves remarkable success in various application scenarios, particularly in addressing system complexity and uncertainty. However, despite many successful cases, several urgent issues still exist in this sector, such as high computational costs, long training times, and limited generalization abilities. [Conclusions]　Reinforcement learning provides novel solutions for the intelligent development of new-type power systems. However, achieving large-scale application still needs to overcome a series of technical and practical challenges. This study provide valuable references and insights for researchers and practitioners in electrical engineering.

Key words: new-type power system, reinforcement learning (RL), deep reinforcement learning (DRL), intelligent grid, strategy optimization, energy management, situational awareness, optimized scheduling