Two-Stage Optimal Scheduling Strategy for Large-Scale Electric Vehicles

• Published in: IEEE access Vol. 8; pp. 13821 - 13832
• Main Authors: Wang, Xiuyun, Sun, Chao, Wang, Rutian, Wei, Tianyuan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Batteries; Charging; Cluster analysis; Clustering; Discharge; Discharges (electric); Electric vehicle charging-discharging strategy; Electric vehicles; Electrical loads; Optimal scheduling; Optimization; Particle swarm optimization; Power generation; Scheduling; Strategy; Thermoelectricity; two-stage optimization; User satisfaction; Vector quantization; vehicle-to-grid
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2966825

This paper proposes a two-stage scheduling strategy for large-scale electric vehicles to reduce the adverse impact of the uncontrolled charging of the electric vehicles on the grid. Based on the statistical data of private car travel, the uncontrolled charging demand of individual electric vehicles and their aggregation are simulated. In the first stage, the electric vehicles and thermal power units are jointly scheduled. To minimize the total cost and standard deviation of the total load curve, the charging and discharging load guiding curve of the electric vehicles and the optimal output plans of the thermal power units in each period of the scheduled day are formulated. In the second stage, the electric vehicle load management and control centre formulates specific charging and discharging plans for the users through rolling optimization to follow the guiding load curve. The cost of vehicle discharge compensation is considered to improve the willingness of users to participate in scheduling and the user satisfaction. To avoid the "dimension disaster" caused by the centralized dispatching of large numbers of electric vehicles, the K-means clustering algorithm is used to divide the vehicles into different groups. Next, each group is scheduled as a unit, and the model is solved by using the particle swarm optimization algorithm. By comparing the optimization results of different scenarios, the feasibility and effectiveness of the proposed strategy are verified.