A charging plan generation method for EV clusters considering the operation of regional distribution network. (December 2022)

• Record Type: Journal Article
• Authors: Tian, Jiang; Lv, Yang; Zhao, Qi; Gong, Yucheng; Li, Chun; Ding, Hongen; Yu, Yu
• Is Part Of: Energy reports. Volume 8(2022)Supplement 14
• Page Start: 135
• Page End: 143
• Publication Date: 2022-12
• Subjects: EV charging -- Day-ahead plan generation -- Cluster control -- Particle swarm algorithm -- Distribution network operation; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.10.117
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: The continuous development of large-scale electric vehicles (EVs) has provided abundant dispatchable resources for the power grid. However, the existing control methods are not enough to support the effective interaction of the EVs and the grid. This paper proposes a method for generating a charging plan of EV clusters considering the stable operation of the regional distribution network, which fully considers the load regulation requirements of the grid and the charging economy of the EV clusters. Combined with the improved Particle Swarm Optimization based on Genetic Algorithm, a day-ahead charging plan for multiple EV clusters can be figured out. It can be proved by the case analysis that, compared with the disordered charging behavior and the participation of demand response with peak and valley electricity price, the charging plan generated by the method proposed can reduce the comprehensive cost of EV charging while optimizing the operation parameters of the distribution network, and it can reduce the calculation dimension in the process of plan generation.