Backbone PV Curve and Improved Genetic Algorithm for Energy Storage Site Selection

Energy storage site selection and volumetric research play an important role in energy storage system, but there is the problem of inaccurate volumetric analysis. The traditional particle swarm algorithm cannot solve the problem of site selection and capacity determination in energy storage system, and the capacity setting effect is not ideal. Therefore, this paper proposes research on energy storage site selection and capacity based on PV curve and improved genetic algorithm, and analyzes the energy storage site selection and capacity research. Firstly, natural selection and genetic theory is used to locate the influencing factors, and the indicators is divided according to the requirements of energy storage site selection and volumetric research, so as to reduce the interference factors in energy storage site selection and volumetric research. Then, natural selection and genetic theory is used to form PV curves and improve the genetic algorithm for energy storage site selection and volumetric research, and the results of energy storage site selection and volumetric determination is comprehensively analyzed. The MATLAB simulation results show that under certain evaluation criteria, the PV curve and improved genetic algorithm is superior to the traditional particle swarm algorithm in terms of the accuracy of energy storage site selection and volumetric research, and the time of influencing factors of energy storage siting and volumetric research.