Spatiotemporal Forecasting of Electric Vehicle Charging Patterns: A Research Overview
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In alignment with national goals for achieving carbon peaking and carbon neutrality, electric vehicles (EVs) are becoming increasingly popular due to their environmentally friendly, low-carbon, energy-efficient, and sustainable attributes. EVs possess dual characteristics as both loads and energy storage systems, with their charging and discharging behaviors exhibiting randomness and spatiotemporal fluctuations. Accurately predicting the spatiotemporal distribution of EV charging and discharging loads is essential for understanding their impact on power grid integration, grid planning and operation, and interactions with the grid. This study analyzes the key factors influencing the prediction of EV charging load distribution and systematically describes modeling approaches and prediction methods for spatial and temporal load distributions. Furthermore, considering EVs as mobile energy storage devices capable of participating in grid interactions, the potential for discharging is evaluated, and research scenarios for Vehicle-to-Grid (V2G) technology are reviewed. Finally, the challenges associated with existing research methods are summarized and discussed, providing insights for future advancements in this field.