Research on Decision-Making for New Power Systems under the Virtual Power Plant Mechanism

The increasing demand for energy and the widespread use of renewable energy sources have led to growing interest in virtual power plants, which leverage grid cloud technology. These facilities integrate power production from various technologies to enhance flexibility, efficiency, and facilitate free-market power trading. However, operating and controlling virtual power plants pose complex challenges, including energy management, load balancing, and energy trading decisions. To address these challenges, reinforcement learning, particularly Deep Reinforcement Learning (DRL), has been applied to virtual power plant control. DRL combines deep learning's representation capabilities with reinforcement learning's decision optimization, making it suitable for solving complex decision-making problems in continuous action spaces. Twin Delayed Deep Deterministic Policy Gradient (TD3), an efficient DRL algorithm, is especially well-suited for such problems. This study aims to optimize decision-making strategies in virtual power plants by applying the TD3 algorithm for efficient energy management and optimization. By establishing a new power system control model based on virtual power plants and integrating TD3, we achieve more flexible and dynamic energy scheduling and control. Compared to traditional control methods, TD3 adapts better to the continuous changes within virtual power plants, resulting in higher energy utilization efficiency and stable operation. This research provides valuable insights for advancing the intelligence of virtual power plants, contributing to sustainable development and intelligent energy management.