Optimization of Charging-Station Location and Capacity Determination Based on Optical Storage, Charging Integration, and Multi-Strategy Fusion

Abstract

To reduce electric vehicle carbon dioxide emissions while charging and increase charging pile utilization, this study proposes an optimization method for charging-station location and capacity determination based on multi-strategy fusion that considers the optical-storage charging station. By analyzing the characteristics of vehicle trajectory data, the dwell points that support charging are extracted; the center point of the dwell area is obtained through k-means clustering, indicating the candidate site of a charging station and optical-storage charging station. The process for determining demand points and quantities is described as follows. Set the parking lot as the demand point; select the period with the most vehicle stops, and determine the demand according to the proximity principle. Using the investment cost, user time cost, and total carbon dioxide emissions from charging as the targets, a data-driven co-evolutionary model is established. It is solved using the multi-objective particle swarm optimization algorithm. Further, the analytic hierarchy process is used to determine the optimal location and sizing scheme. Empirical analysis is completed using Beijing taxi track data as an example. The experiments show that after constructing an optical-storage charging station, the number of charging piles can be reduced by improving the charging pile utilization rate, and the investment cost can be effectively controlled. The station is built at a location with a large demand, effectively reducing the carbon dioxide emissions caused by charging and indirectly reducing user time cost.