Coordinated Capacity Planning and Charging Scheduling for Multiple EV Charging Stations Considering Time-of-Use Pricing and Energy Storage

Abstract

With the rapid growth of electric vehicles (EVs), high charging demand has increased uneven station utilization and pressure on distribution networks. Therefore, this paper proposes a collaborative method for capacity planning and charging scheduling of multiple charging stations (CSs) considering time-of-use (TOU) pricing and energy storage systems (ESSs). A total system cost model is established, including transformer and ESS costs. Secondly, a deep neural network-guided improved sparrow search algorithm (DNN-ISSA) is proposed to optimize the number of chargers and parking spaces by predicting the initial capacity center. Furthermore, a charging scheduling algorithm is proposed to optimize user charging time by introducing a TOU price response function to modify charging probabilities. A case study of 36 CSs in Jinan shows that the proposed method reduces average charging time by 15.7, 15.4, and 15.2 min for 1000, 5000, and 10,000 demand points, while lowering the total system cost from 73.92 to 70.36 million yuan. The convergence value of DNN-ISSA reduces by 15.05%, 21.67%, and 11.61% compared with the improved sparrow search algorithm (ISSA), particle swarm optimization algorithm (PSO), and sparrow-particle swarm optimization algorithm (SSA-PSO), respectively. The proposed method enhances energy utilization, mitigates peak loads, and supports low-carbon EV charging operation.