Pricing-based coordinated scheduling for multiple EV charging stations considering capacity prediction and service radius

Abstract

Electric vehicle (EV) charging station scheduling can maximize profits by optimizing charging prices. Many existing scheduling methods emphasize aggregator profits and still have limited consideration of inter-station coordination and the dynamic service radius. The prediction accuracy of schedulable capacity indirectly affects the profits of aggregators. In addition, the prediction accuracy of schedulable capacity is affected by the uncertainty of station selection, which has also been neglected. To address these issues, a pricing-based coordinated scheduling framework for multiple charging stations is proposed. The propose framework incorporates a dynamic service radius and schedulable capacity prediction models. The framework includes an analysis of EV station selection behaviour under joint decision-making and the development of a dynamic service radius model for charging stations. Additionally, a schedulable capacity prediction model is constructed by integrating physical modelling with a data-driven approach based on long short-term memory networks. Compared with the peak-valley pricing-based schedule method and Stackelberg-based pricing method, the aggregator profit is enhanced by the application of the proposed framework.