当前位置: X-MOL 学术Wirel. Commun. Mob. Comput. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Multiobjective Optimization regarding Vehicles and Power Grids
Wireless Communications and Mobile Computing ( IF 2.146 ) Pub Date : 2021-06-14 , DOI: 10.1155/2021/5552626
Kaiyang Zhong 1 , Ping Wang 1 , Jiaming Pei 2 , Jiyuan Xu 2 , Zonglin Han 3 , Jiawen Xu 4
Affiliation  

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.

中文翻译:

车辆和电网的多目标优化

车辆到电网(V2G)是指通过合理的策略和先进的通信对电动汽车的充放电行为进行优化管理。在交互过程中,存在三个利益相关者:电网、运营商(充电站)和电动汽车用户。在现实生活中,峰谷差的影响在充电时造成了很大的功率损耗。同时,电流的损失也是电网公司和电动汽车用户的损失。在本文中,我们提出了一种多目标优化方法来减少电流损失并确定参数与目标函数和约束之间的关系。这种优化方法使用遗传算法进行多目标优化。
更新日期:2021-06-14
down
wechat
bug