Research on Algorithm Design Simulation Model in Intelligent Dispatching of Mobile Energy Storage Equipment

This paper proposes an intelligent dispatching algorithm combining RL and GA to optimize the dispatching strategy of mobile energy storage equipment. Reinforcement learning algorithm can adaptively learn the best dispatching strategy through interaction with the environment, while genetic algorithm finds the optimal solution in the dispatching decision space through global optimization. By combining these algorithms, the paper can make full use of the advantages of the two methods to increase the efficiency and reliability of dispatching. The experiment results show that this algorithm can increase the dispatch efficiency by 15%, and the response rate of the system is up to 30%. Moreover, it is proved that the system is robust and adaptable, and can deal with the dispatching problems due to the variation of grid load. Moreover, the performance of this method is especially outstanding in large scale energy storage equipment dispatching, and it has a good expansibility. The results of the experiment indicate that the method not only can improve the efficiency of energy storage equipment, but also reduce the energy wastage and enhance the stability of the grid. So, the intelligent dispatching method is a feasible and effective solution for the efficient management of the mobile energy storage devices.