Review of Application on Optimization Strategies for New-Type Power System Based on Reinforcement Learning

Abstract

ObjectivesAs 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.MethodsThis 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.ResultsCompared 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.ConclusionsReinforcement 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