Overview of Research on Spatiotemporal Distribution Prediction of Electric Vehicle Charging

doi:10.12204/j.issn.1000-7229.2023.12.014

Abstract

Abstract:

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

CLC Number:

TM734

ZHANG Xiawei, LIANG Jun, WANG Yaoqiang, HAN Jing. Overview of Research on Spatiotemporal Distribution Prediction of Electric Vehicle Charging[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(12): 161-173.

Figures/Tables 5

Fig.1 Factors affecting the distribution of electric vehicle charging load

Fig.2 Different types of electric vehicle travel models

Fig.3 Road network topology

Fig.4 Schematic diagram of electric vehicle travel chain

Fig.5 A model-driven and data-driven charging load forecasting framework for electric vehicles

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