Electric vehicle charging schedule considering user's charging selection from economics. Issue 15 (16th July 2019)

• Record Type: Journal Article
• Authors: Chen, Jie; Huang, Xiaoqing; Tian, Shiming; Cao, Yijia; Huang, Bicheng; Luo, Xiaoyue; Yu, Wenlong
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 15(2019)
• Page Start: 3388
• Page End: 3396
• Publication Date: 2019-07-16
• Subjects: particle swarm optimisation -- distribution networks -- battery powered vehicles -- scheduling -- optimisation -- electric vehicles -- power distribution economics -- power grids; electric vehicle charging schedule considering user -- large‐scale EV applications -- optimal scheduling -- EV users -- deviation -- power grid schedule -- multiobjective scheduling method -- optimal EV charging -- charging selection -- user constraint -- charging preferences -- scheduling model -- ideal scheduling -- actual scheduling -- EV user; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2019.0154
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Electric vehicle (EV) charging will bring new challenges to the coordination of grid and EV load. To facilitate large‐scale EV applications, optimal scheduling of EVs has become essential. However, the power grid has no power to force EV users to charge or give up charging. The actual type and mode of user's charging, may deviate from the expectations of grid. When the number of deviation's EV is sufficient, the power grid schedule will not achieve the expected results. In this context, a multi‐objective scheduling method considering user's charging selection is proposed to determine the optimal EV charging. The novel contributions of this paper lie in the exploitation of the user's preferences and their criterion of charging selection in form of functions based on concepts from economics. Three types of charging preferences for EV users including radical, conservative and balanced are used to influence the charging cost of EVs and load variance of grid. Finally, the ideal scheduling and actual scheduling are used to simulate various scenarios. The extensive results show that by introducing a method considering the influence of EV user's selection, the efficacy of the proposed scheduling can be further improved.