Research on Collaborative Control Strategy of Virtual Power Plant Based on Deep Reinforcement Learning Framework - Yue, Longfei; Liang, Xianxia; Sun, Litao; Li, Yupeng; Cheng, Longxue (Distributed Generation & Alternative Energy Journal, 2025)
To address the limitations of the traditional Deep Q-Network (DQN) in VPPs collaborative dispatch, such as large state space, low computational efficiency, and poor generalization, an improved DQN (IDQN) is proposed. Firstly, consider various factors of VPPs and optimize its dispatch process to enhance its prediction accuracy. Secondly, a new reward mechanism is designed to guide agents to consider both long-term stability and rational allocation of resources to improve energy utilization. Then, IDQN is employed to optimize the VPPs cooperative scheduling, which can improve the scheduling efficiency and control its cost. Finally, based on the improved DQN, a multi-objective decision-making framework is proposed for collaborative optimization scheduling of VPP to improve system stability. Compared with the classical Q-learning algorithm, the introduced IDQN has lower rejection rate, lower cost and higher scheduling efficiency when used in VPP collaborative scheduling.