Grid-Connected Optical Storage Charging Station Capacity Allocation Method Based on Particle Swarm Algorithm

For optical storage charging stations, the optimization of photovoltaic, energy storage, and charging facilities is an important factor affecting the economic efficiency of the charging station. This article first simulates the charging situation of electric vehicles in a charging station within a day, and obtains the daily charging curve of the charging station. Subsequently, based on the characteristics of photovoltaic output in a certain area and combined with the time-of-use electricity price, taking into account the factors of investors and users, a capacity optimization configuration model for optical storage charging stations was proposed with the objective function of minimizing total social cost and constraints such as device utilization and queuing time. Particle swarm optimization algorithm was used to solve the problem, and the photovoltaic and energy storage capacity added to the charging station was configured. Based on queuing theory, various operational indicators of the system were obtained, Finally, verify the rationality of the configuration results from multiple perspectives such as the trend of various costs changing with capacity. The calculation results and case analysis indicate that the method proposed in this article can achieve reasonable configuration of photovoltaic, energy storage, and charging facility capacity, thereby effectively reducing the amount of electricity purchased by charging stations from the grid during peak hours and improving the economic efficiency of charging station operation.