Study on Optimization and Scheduling Strategies of Distributed Energy Storage Based on Digital Twin

With the increasing penetration of distributed energy sources such as photovoltaic (PV) power, the power grid faces stability challenges arising from load fluctuations and variability in renewable energy generation. To address these issues, this paper proposes an optimized energy storage scheduling strategy for microgrids by combining digital twin technology and deep reinforcement learning (DQN). This strategy leverages the flexibility of energy storage to store excess energy during periods of low demand and release it during peak demand times, thus improving grid stability and enhancing renewable energy utilization. Simulation results show that the energy storage scheduling strategy based on digital twin technology and deep reinforcement learning significantly improves scheduling accuracy, reduces decision-making time, and optimizes peak-shaving and valley-filling. The optimized system has achieved significant improvements in reducing grid operation costs and enhancing the efficiency of renewable energy integration. This study demonstrates the great potential of combining digital twin technology and reinforcement learning for optimizing microgrid scheduling, providing strong support for more efficient energy management.