Active power balance control of wind-photovoltaic-storage power system based on transfer learning double deep Q-network approach

Abstract

IntroductionThis study addresses the challenge of active power (AP) balance control in wind-photovoltaic-storage (WPS) power systems, particularly in regions with a high proportion of renewable energy (RE) units. The goal is to effectively manage the AP balance to reduce the output of thermal power generators, thereby improving the overall efficiency and sustainability of WPS systems.MethodsTo achieve this objective, we propose the transfer learning double deep Q-network (TLDDQN) method for controlling the energy storage device within WPS power systems. The TLDDQN method leverages the benefits of transfer learning to quickly adapt to new environments, thereby enhancing the training speed of the double deep Q-network (DDQN) algorithm. Additionally, we introduce an adaptive entropy mechanism integrated with the DDQN algorithm, which is further improved to enhance the training capability of agents.ResultsThe proposed TLDDQN algorithm was applied to a regional WPS power system for experimental simulation of AP balance control. The results indicate that the TLDDQN algorithm trains agents more rapidly compared to the standard DDQN algorithm. Furthermore, the AP balance control method based on TLDDQN can more accurately manage the storage device, thereby reducing the output of thermal power generators more effectively than the particle swarm optimization-based method.DiscussionOverall, the TLDDQN algorithm proposed in this study can provide some insights and theoretical references for research in related fields, especially those requiring decision making.