• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2023, Vol. 44 ›› Issue (3): 93-104.doi: 10.12204/j.issn.1000-7229.2023.03.010

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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)


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

CLC Number: