Collaborative Optimization Scheduling Strategy for Electric Vehicle Charging Stations Considering Spatiotemporal Distribution of Different Power Charging Demands

The rapid growth of electric vehicle (EV) adoption has led to an increased demand for charging infrastructure, creating significant challenges for power grid load management and dispatch optimization. This paper addresses these challenges by proposing a coordinated optimization dispatch strategy for EV charging, which integrates time, space, and varying power requirements. This study develops a dynamic spatiotemporal distribution model that accounts for charging demand at different power levels, traffic network characteristics, and congestion factors, providing a more accurate simulation of charging demand in dynamic traffic conditions. A comprehensive optimization framework is introduced, and is designed to reduce peak congestion, enhance service efficiency, and optimize system performance. This framework dynamically adjusts the selection of charging stations (CSs), charging times, and charging types, with a focus on improving user satisfaction, balancing the grid load, and minimizing electricity purchase costs. To solve the optimization model, a hybrid approach combining particle swarm optimization (PSO) and the TOPSIS method is employed. PSO optimizes the overall objective function, while the TOPSIS method evaluates user satisfaction. The results highlight the effectiveness of the proposed strategy in improving system performance and providing a balanced, efficient EV charging solution.