Real-time scheduling and optimization model of electric vehicles based on fuzzy evaluation of demand response potential

被引：0

作者：

Zhou X. ^{[1
]}

Huang X. ^{[1
]}

Zhang Y. ^{[1
]}

Tang Y. ^{[1
]}

Yao L. ^{[1
]}

Yang J. ^{[2
]}

机构：

[1] School of Electric Power, South China University of Technology, Guangzhou

[2] Digital Grid Research Institute of China Southern Power Grid Co., Ltd., Guangzhou

来源：

Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2022年 / 42卷 / 10期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

demand response potential; electric vehicles; fuzzy reasoning; incentive mechanism; real-time optimization;

D O I：

10.16081/j.epae.202207030

中图分类号：

学科分类号：

摘要：

Aiming at the overload problem of distribution network caused by large-scale access of EVs(Electric Vehicles) in fast charging places, a fuzzy evaluation method of demand response potential of EVs and a real-time scheduling optimization model are proposed. Firstly, based on the constraints of safety capacity of EV battery, EV charging demand, rated power of charging piles, a constraint model of user objective response capacity is proposed, and an evaluation model of user subjective response willingness considering incentive level is proposed. Secondly, combining objective response capacity and subjective response willingness, an evaluation model of EV user response potential is proposed, and the influences of charging price, current capacity demand and remaining residence time on user response willingness are determined by fuzzy reasoning. Then, a two-level optimization model of incentive type real-time demand response and its solution method are proposed. The upper optimization model optimizes the incentive price of EV aggregator with the goal of minimizing the incentive cost of EV aggregator, and the lower optimization model optimizes the charging and discharging power of EVs with the goal of the highest average charging satisfaction of users, thus fully taping the response potential of users and taking into account the interests of grid company, EV aggregator and users. Finally, the effectiveness of the proposed model and method is verified by several groups of simulations. © 2022 Electric Power Automation Equipment Press. All rights reserved.

引用

页码：30 / 37

页数：7

共 18 条

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