Research on Power System Simulation Model in Intelligent Dispatching of Mobile Energy Storage Equipment

This paper proposes an innovative joint optimization model, which aims to optimize the intelligent dispatching strategy of mobile energy storage devices by combining deep learning and reinforcement learning techniques to improve the stability of the power system and the efficiency of resource allocation. The deep learning algorithm is used to accurately predict the state of power load and energy storage equipment, while the reinforcement learning algorithm is used to dynamically adjust the dispatching strategy to achieve the optimal matching of power supply and demand. To verify the validity of the proposed model, a series of simulation experiments are carried out, and the results are compared with the conventional ones (GA, PSO). The joint optimization model has significant advantages in dispatching accuracy, resource utilization and computational efficiency. Specifically, the dispatching accuracy is improved by 15%, the resource utilization is improved by 20%, and the computing time is reduced by about 25% compared with the traditional method. Especially in the complex environment with large load fluctuations and renewable energy grid connection, the joint optimization algorithm shows stronger adaptability and responsiveness.