A Novel Framework for Spatial‐Temporal Impact of Electric Vehicles Charging on Hosting Risk in Urban Distribution System: A Case Study of Shanghai

ABSTRACT Electric vehicles (EVs) integration into urban distribution system is surging globally, leading to an increase in the demand for EV charging load, which brought significant hosting risk in urban power systems, such as transformer overload. This study aims to characterize and model the spatial‐temporal impact of EV charging on hosting risk of urban power systems across various urban scales considering the recent development of fast charging stations. To achieve this, it proposes a novel framework for calculating the hosting risk. This provides sufficient guiding information for maintaining reliability in urban power systems. In detail, we systematically model the EV charging patterns by using high‐resolution real‐world EV trajectory and charging record data. K‐means clustering is utilized to identify typical charging patterns in various urban scales. Then, the novel hosting risk indicators considering the fast charging station development and the EV growth based on urban EV charging pattern distribution are proposed, where random forest is used to model the critical parameters. Finally, the hosting risk indicators under different urban scale and different EV charging conditions are illustrated. The finding indicates that the hosting risk of urban distribution system is closely related to the EV charging patterns, showing significant regional differences. In Shanghai, the overall city charging pattern exhibits a double‐peak structure with morning and evening peaks at 10:14 and 21:04, respectively. The maximum 24‐h hosting risk indicators for different charging types vary significantly, with Type 2 charging patterns showing the highest risk. The spatial‐temporal changes of which can provide significant information for future plans of EV charging stations and reliability maintenance schemes.