Research on Design Model of Intelligent Scheduling System for Mobile Energy Storage Equipment Based on Deep Learning

In this paper, a new Transform-Proximal Genetic Optimization (T-PGO) model is proposed and applied to the design of intelligent scheduling system for mobile energy storage equipment based on deep learning. T-PGO model combines the advantages of near-end Strategy optimization (PPO), Genetic algorithm (GA) and Transformer. PPO is used for reinforcement learning training to improve the decision-making ability of mobile energy storage equipment scheduling strategy. GA helps to approach the optimal solution in the search space by simulating natural selection and genetic variation. Transformer processes input data with its powerful attention mechanism, improving the model's ability to understand and process data. The model aims to realize the efficient utilization and optimal configuration of mobile energy storage equipment through intelligent scheduling system, so as to improve the energy efficiency and the overall performance of the system. In the experiment, we compare T-PGO with traditional scheduling system. The results show that the T-PGO model has a remarkable optimization effect in the intelligent scheduling system of mobile energy storage equipment, and provides a new idea and method for energy management and intelligent scheduling. This fully verifies the feasibility and effectiveness of T-PGO architecture in this paper, and provides a new solution and technical support for building a more intelligent and efficient energy management system.