Open Access

ARTICLE

Distribution Network Hosting Capacity Evaluation for Electric Vehicle Charging Stations Considering User Selection Preferences

Xiangchao Li¹, Xiaobo Mao^1,*, Ni Tang¹, Zhi Rui², Yanming Zhu²
1 State Grid WuXi Power Supply Company, Wuxi, China
2 Key Laboratory of Control of Power Transmission and Conversion, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
* Corresponding Author: Xiaobo Mao. Email:
(This article belongs to the Special Issue: Digital and Intelligent Planning and Operation Technologies for Flexible Distribution Network)

Energy Engineering https://doi.org/10.32604/ee.2026.087044

Received 09 June 2026; Accepted 09 September 2026; Published online 17 September 2026

Abstract

The rapid growth of electric vehicles (EVs) and fast-charging infrastructure imposes a substantial charging load on the distribution network (DN), posing significant challenges for secure operation and long-term planning. The modeling of EV charging load is essential for DN hosting capacity assessment, yet the spatial-temporal uncertainty arising from traffic-vehicle-grid interactions and heterogeneous user behavior makes EV load simulation difficult. This paper proposes an integrated framework for EV charging load simulation and distribution-network hosting capacity evaluation with explicit consideration of user charging station selection preferences. First, a spatial-temporal charging demand simulation is developed by integrating trip modeling, traffic network dynamics and Monte Carlo sampling. Then, a multinomial logit user decision model is established to capture heterogeneous charging station choices through user sensitivities to driving distance, facility scale, charging time and price. Finally, the station-level charging loads are mapped to the corresponding DN buses and incorporated into a time-series optimal power flow model to determine the maximum admissible number of EVs. Case studies demonstrate that the proposed method effectively captures user preference heterogeneity and its impacts on charging load distribution, revealing substantial variations in hosting capacity under different charging station configurations. The proposed framework provides a systematic approach for investigating the impacts of user preferences and charging-station configurations on distribution-network hosting capacity.

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Keywords

Electric vehicle; charging load simulation; spatial-temporal distribution; user preference; distribution network hosting capacity

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