发电技术2025，Vol.46Issue(3)：508-520,13.DOI:10.12096/j.2096-4528.pgt.24227
基于强化学习的新型电力系统优化策略应用综述
Review of Application on Optimization Strategies for New-Type Power System Based on Reinforcement Learning
摘要
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.Therefore,the performance of RL in practical electrical applications is analyzed,and the possible research directions in the future are prospected,so as to provide assistance for 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 system.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 system.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 system.However,achieving large-scale application still needs to overcome a series of technical and practical challenges.This study provides references and insights for researchers and practitioners in electrical engineering.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/人工智能(AI)Key words
new-type power system/reinforcement learning(RL)/deep reinforcement learning(DRL)/intelligent grid/strategy optimization/energy management/situational awareness/optimized scheduling/artificial intelligence(AI)分类
能源科技引用本文复制引用
闫正义,赵康,王凯..基于强化学习的新型电力系统优化策略应用综述[J].发电技术,2025,46(3):508-520,13.基金项目
国家自然科学基金项目(12374088,51877113) （12374088,51877113）
山东省高等学校青年创新技术项目(2022KJ139).Project Supported by National Natural Science Foundation of China(12374088,51877113) （2022KJ139）
Youth Innovation Technology Project of Higher School in Shandong Province(2022KJ139). （2022KJ139）
