Economic Optimization Research of Hourly-Level Intelligent Charging Stations Based on PV-Storage Charging Microgrid

With the continuous development of clean energy technologies, the PV-storage charging microgrid has become an emerging application model for electric vehicle charging stations. This paper develops an hourly-level economic optimization model based on photovoltaic generation, energy storage systems, and electric vehicle charging demand. First, the Random Forest algorithm is used to predict the photovoltaic power generation for the next 24 hours, and the Transformer model is employed to forecast the charging load of electric vehicles. Based on these predictions, an energy storage scheduling strategy is designed, and the SLSQP algorithm is used to solve the model to maximize the total revenue of the charging station. Meanwhile, system constraints, such as storage capacity limits and charge-discharge rate limits, are considered. Finally, the proposed strategy is validated with a case study of a charging station in Wuhan. Simulation results show that the optimized scheduling strategy effectively improves photovoltaic utilization, reduces the operational cost of the charging station, and enhances the overall economic efficiency of the system.