Data Siting and Capacity Optimization of Photovoltaic–Storage–Charging Stations Considering Spatiotemporal Charging Demand

To address the charging demand challenges brought about by the widespread adoption of electric vehicles, integrated photovoltaic–storage–charging stations (PSCSs) enhance energy utilization efficiency and economic viability by combining photovoltaic (PV) power generation with an energy storage system (ESS). This paper proposes a two-stage data-driven holistic optimization model for the siting and capacity allocation of charging stations. In the first stage, the location and number of charging piles are determined by analyzing the spatiotemporal distribution characteristics of charging demand using ST-DBSCAN and K -means clustering methods. In the second stage, charging load results from the first stage, photovoltaic generation forecast, and electricity price are jointly considered to minimize the operator’s total cost determined by the capacity of PV and ESS, which is solved by the genetic algorithm. To validate the model, we leverage large-scale GPS trajectory data from electric taxis in Shenzhen as a data-driven source of spatiotemporal charging demand. The research results indicate that the spatiotemporal distribution characteristics of different charging demands determine whether a charging station can become a PSCS and the optimal capacity of PV and battery within the station, rather than a fixed configuration. Stations with high demand volatility can achieve a balance between economic benefits and user satisfaction by appropriately lowering the peak instantaneous satisfaction rate (set between 70 and 80%).