Electric Vehicle Optimal Scheduling Method Considering Charging Piles Matching Based on Edge Intelligence

Abstract

To adress the problems of insufficient consideration of charging pile resource limitations, discrete-time scheduling methods that do not meet the actual demand and insufficient descriptions of peak-shaving response capability in current electric vehicle (EV) optimization scheduling, edge intelligence-oriented electric vehicle optimization scheduling and charging station peak-shaving response capability assessment methods are proposed on the basis of the consideration of electric vehicle and charging pile matching. First, an edge-intelligence-oriented electric vehicle regulation frame for charging stations is proposed. Second, continuous time variables are used to represent the available charging periods, establish the charging station controllable EV load model and the future available charging pile mathematical model, and establish the EV and charging pile matching matrix and constraints. Then, with the goal of maximizing the user charging demand and reducing the charging cost, the charging station EV optimal scheduling model is established, and the EV peak response capacity assessment model is further established by considering the EV load shifting constraints under different peak response capacities. Finally, a typical scenario of a real charging station is taken as an example for the analysis of optimal EV scheduling and peak shaving response capacity, and the proposed method is compared with the traditional method to verify the effectiveness and practicality of the proposed method.