Research on Intelligent Dispatching Optimization Model of Mobile Energy Storage Equipment for User-Side Services

In order to improve the scheduling efficiency and precision of mobile energy storage devices, a new combination of GA and ACA is presented in this paper. The GA is responsible for the global search, through the optimization of the global resource allocation and the scheduling strategy. The Ant Colony Algorithm emphasizes the local search, the optimization of the dispatching route and the operation state of the energy storage device in detail. The combination of the two enables the model to efficiently complete the intelligent dispatching of energy storage equipment under variable power demand. On the power system simulation platform, this study verifies the effectiveness of the joint optimization model through a series of experiments. The experimental results show that compared with traditional dispatching algorithms (such as genetic algorithms, particle swarm optimization, etc.), the model improves the dispatching accuracy by about 20%, the resource utilization rate by about 15%, and the calculation time is reduced by 30%. Especially in the case of large fluctuations in power demand, the joint optimization model can effectively cope with load changes and improve the response speed and stability of the system. In summary, the joint optimization model proposed in this paper not only has significant advantages in improving dispatching efficiency, accuracy and resource utilization, but also has strong practical application potential.