The Application Status and Prospect of Deep Reinforcement Learning in the Smart Grid

Abstract

Deep reinforcement learning has strong capabilities in data analysis, prediction, and autonomous learning, and it is highly compatible with the demand for big data applications in various aspects of the smart grid. Firstly, this paper summarizes the basic ideas of deep learning, introduces the basic principles and typical algorithms of deep reinforcement learning, and outlines its application characteristics. It reviews the current status of the application of deep reinforcement learning in the smart grid system in aspects such as fault diagnosis, load and new energy power prediction, and power dispatch. In view of the technical characteristics of deep reinforcement learning, combined with the various production links of the power system, an application framework of deep reinforcement learning technology in the power system is established. Finally, the application of deep reinforcement learning is prospected from aspects such as the operation control of multi-energy systems, system security analysis, fault diagnosis of flexible equipment, and privacy information security protection.