Multiobjective Optimization regarding Vehicles and Power Grids

Wireless communications & mobile computing

article

Kaiyang Zhong¹  Ping Wang¹  Jiaming Pei²  Jiyuan Xu²  Zonglin Han³  Jiawen Xu⁴ 

[1] School of Economic Information Engineering, Southwestern University of Finance and Economics;School of Computer Science and technology, Taizhou University;Zhengzhou Electric Power Vocational and Technical College;College of Artificial Intelligence, Tianjin University of Science and Technology

DOI  :  10.1155/2021/5552626

来源: Hindawi Publishing Corporation

【 摘 要 】

Vehicle to Grid (V2G) refers to the optimal management of the charging and discharging behavior of electric vehicles through reasonable strategies and advanced communication. In the process of interaction, there are three stakeholders: the power grid, operators (charging stations), and EV users. In real life, the impact of peak-valley difference caused a lot of power loss when charging. At the same time, the loss of current is also a loss for power grid companies and EV users. In this paper, we propose a multiobjective optimization method to reduce the current loss and determine the relationship between the parameters and the objective function and constraints. This optimization method uses a genetic algorithm for multiobjective optimization. Through the analysis of the number of vehicles and load curve of AC class I and AC class II electric vehicles before and after optimization in each period, we found that the charging load of electric vehicles played a role of valley filling in the low valley price stage and played a peak-cutting role in a peak price period.

【 授权许可】

Unknown