• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

电力建设 ›› 2023, Vol. 44 ›› Issue (3): 93-104.doi: 10.12204/j.issn.1000-7229.2023.03.010

• 双碳驱动下配电网与新型负荷互动关键技术·栏目主持 穆云飞教授、宋毅教授级高工· • 上一篇    下一篇


姚寅(), 朱烨冬(), 李东东(), 周波(), 林顺富()   

  1. 上海电力大学电气工程学院,上海市 200082
  • 收稿日期:2022-11-22 出版日期:2023-03-01 发布日期:2023-03-02
  • 通讯作者: 周波(1980),男,硕士,讲师,主要研究方向为智能用电、综合能源系统,E-mail:zhoubo@shiep.edu.cn
  • 作者简介:姚寅(1986),男,博士,讲师,主要研究方向为电动汽车智能并网,E-mail:yin.yao@shiep.edu.cn
  • 基金资助:

Multi-scenario Demand Response Strategy Based on DEMATEL-AISM for Electric Vehicles

YAO Yin(), ZHU Yedong(), LI Dongdong(), ZHOU Bo(), LIN Shunfu()   

  1. College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200082, China
  • Received:2022-11-22 Online:2023-03-01 Published:2023-03-02
  • Supported by:
    National Natural Science Foundation of China(51977127)


随着可再生能源渗透率的持续提升,电网波动性与随机性日益增加,需求侧可调资源将愈发重要。电动汽车(electric vehicle, EV)在需求侧资源中占比较大,但现有研究较少考虑电动汽车参与需求响应过程中个人与社会因素的多因素交互影响。因此,文章提出了一种基于决策实验室算法-对抗解释结构模型(decision-making trial and evaluation laboratory-adversarial interpretive structure modeling,DEMATEL-AISM)算法的EV多场景需求响应充电调度策略。首先,通过数据挖掘法分析多场景下充电站运行特性与电动汽车充能特性,构建电动汽车充电负荷特性模型;其次,使用DEMATEL-AISM算法对多场景下影响电动汽车充能行为的多因素耦合关系进行分析,挖掘主导因素;最后,基于多场景主导因素分析,制定多因素影响下的用户调控策略。通过仿真分析,验证了所提方法能有效平抑负荷峰谷水平,降低节点电压波动,提高电力系统需求侧的稳定性与经济性。

关键词: 电动汽车, 需求响应, 决策实验室算法-对抗解释结构模型(DEMATEL-AISM), 多因素影响度评估


With the continuous increase of renewable energy penetration, the volatility and randomness of the power grid are increasing, and demand-side resource supply will become more and more important. Electric vehicles (EVs) account for a relatively large share of demand-side resources, but existing studies have rarely considered the multi-factor interaction of individual and social factors in the process of EV participating in demand response. Therefore, an EV multi-scenario demand-response charging scheduling strategy based on the decision-making trial and evaluation laboratory-adversarial interpretive structure modeling method (DEMATEL-AISM) is constructed in this paper. Firstly, a data mining method is used to analyze the operating characteristics of charging stations and EV charging characteristics in multiple scenarios, and an EV charging load characteristic model is constructed. Secondly, the DEMATEL-AISM algorithm is used to analyze the multi-factor coupling relationship affecting EV charging behavior in multiple scenarios, and the dominant factors are explored. Finally, according to the analysis of the dominant factors in multiple scenarios, a user regulation strategy is formulated under the influence of multiple factors. Through simulation, it is verified that the method proposed in this paper can effectively smooth out the peak and valley levels of load, node voltage fluctuations is reduced, the stability and economy of the demand side of the power system is improved.

Key words: electric vehicles, demand response, decision-making trial and evaluation laboratory-adversarial interpretive structure modeling method (DEMATEL-AISM), multi-factor impact degree assessment