Active power balance control of wind-photovoltaic-storage power system based on transfer learning double deep Q-network approach - Xiao, Jun; Zhao, Wen; Li, Wei; Zhao, Yankai; Li, Yongzhi; Ma, Xudong; Liu, Yuchao (Frontiers in Energy Research, 2024)
Aiming at the active power (AP) balance control problem of wind-photovoltaic-storage (WPS) power systems in the region with a high proportion of renewable energy (RE) units, this study proposes the transfer learning double deep Q-network (TLDDQN) method for controlling the energy storage device to balance the AP of WPS power systems. The TLDDQN method combines the advantages of transfer learning methods that can rapidly adapt to new environments to improve the double deep Qnetwork algorithm's training speed. In addition, this study proposes a method to combine the adaptive entropy mechanism with the DDQN algorithm and improve the adaptive entropy mechanism to enhancing the training capability of agents. Compared with the AP balance control method based on particle swarm optimization, the AP balance control method based on the proposed TLDDQN can more accurately balance the AP of the WPS power system to reduce the output of thermal power generators. The proposed TLDDQN algorithm is applied to a regional WPS power system for the experimental simulation of AP balance control. Experimental results demonstrate that compared to the DDQN algorithm, the TLDDQN algorithm proposed in this study trains agents faster and the AP balance control method of the TLDDQN-based WPS power system can more accurately control the storage device to reduce the output of thermal power generators, compared with the particle swarm optimization. Overall, the TLDDQN algorithm proposed in this study can provide some insights and theoretical references for research in related fields, especially those requiring decision making.