• 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 (12): 161-173.doi: 10.12204/j.issn.1000-7229.2023.12.014

Key Technologies for Interaction between Power Distribution Network and New Load Drivenby Carbon Peaking and Carbon Neutrality Goals ?Hosted by Professor MU Yunfei and Professor-level Senior Engineer SONG Yi? Previous Articles     Next Articles

Overview of Research on Spatiotemporal Distribution Prediction of Electric Vehicle Charging

ZHANG Xiawei1,2(), LIANG Jun1,2,3(), WANG Yaoqiang1,2(), HAN Jing1,2()   

  1. 1. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
    2. Henan Engineering Research Center of Power Electronics and Energy Systems, Zhengzhou 450001, China
    3. School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
  • Received:2023-04-23 Published:2023-12-01 Online:2023-11-29
  • Supported by:
    National Natural Science Foundation of China(51507155);Henan Province Key R&D and Promotion Special Project(222102520001)


With national targets for carbon peaking and carbon neutrality, electric vehicles (EVs) are gaining popularity owing to their advantages of being green, low-carbon, energy-saving, and environmentally friendly. EVs have both load and energy storage characteristics, and their charge-discharge behavior is random and fluctuates in time and space. Accurate prediction of the spatiotemporal distribution of EV charging and discharging loads is the basis for studying the influence of EV entering the grid, power grid planning and operation, and interaction with the power grid. The main factors influencing the prediction of the spatiotemporal distribution of the EV charging load are analyzed. The modeling of the charging load and prediction method for the spatial and temporal distributions are systematically described. Considering that electric vehicles can be used as mobile energy-storage devices to participate in grid interactions, the discharge potential is evaluated, and the research scenario of V2G technology is reviewed. Finally, the challenges faced by existing research methods are summarized and discussed.

Key words: electric vehicle, load prediction, affecting factors, spatiotemporal distribution, V2G

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