Electric vehicle charging station planning based on the development of distribution networks and coupled charging demand

被引：0

作者：

He, Daqing ^{[1
]}

Chen, Yunuo ^{[1
]}

Li, Linwei ^{[1
]}

Chen, Dunchu ^{[2
]}

Li, Wenwu ^{[2
]}

机构：

[1] China Three Gorges University, Hubei, Yichang

[2] Hubei province Key Laboratory of Cascade Hydropower Station Operation and Control, Hubei, Yichang

来源：

International Journal of Information and Communication Technology | 2025年 / 26卷 / 06期

关键词：

article swarm optimisation; bilevel optimisation; charging station planning; clustering; distribution network; distribution network planning; electric vehicles; EVs;

D O I：

10.1504/IJICT.2025.145404

中图分类号：

学科分类号：

摘要：

Current research on charging station planning overlooks the evolution of power distribution networks and oversimplifies charging demand without considering the traffic characteristics of electric vehicles (EVs), leading to voltage deviations due to high charging loads. This paper proposes a bi-level optimisation model to simulate EV charging demand based on road networks. Charging demand is forecasted through road network simulation, and simplified charging needs are clustered with road weights. The model then optimises the power distribution network topology, as well as the location and capacity of charging stations. The upper level optimises the power network structure, while the lower level optimises the layout and capacity of charging stations. Case studies show that the clustering algorithm based on road weights effectively simplifies the data while retaining the spatiotemporal characteristics of charging demand. The proposed bi-level planning model significantly mitigates voltage deviations caused by high charging loads. Copyright © The Author(s) 2025.

引用

页码：62 / 97

页数：35

共 39 条

[1]

Cui M., Research on Real-Time Travel Time Collection Method Based on Simplified Road Network Model, (2013)

[2]

Fesli U., Ozdemir M.B., Electric vehicles: a comprehensive review of technologies, integration, adoption, and optimization, IEEE Access, 12, pp. 140908-140931, (2024)

[3]

Gao T., Huang X., Yang Z., Wang H., Wen X., Zhao Q., Ding H., A grouping strategy and day-ahead scheduling method of electric vehicles for peak shaving, 2022 IEEE 5th International Electrical and Energy Conference (CIEEC), pp. 2676-2681, (2022)

[4]

Gutierrez D.M., Ladisch M.R., Electric vehicles, biofuels, and transitions in transportation energy, Industrial Biotechnology, 20, 1, pp. 21-25, (2024)

[5]

Hou H., Wang Y., Wu X., Et al., Scheduling strategy for electric vehicle charging and discharging considering range anxiety psychological effect on a long time scale, High Voltage Technology, 49, 1, pp. 85-93, (2023)

[6]

Jiang X., Feng Y.T., Xiong H., Wang J., Zeng Q., Electric vehicle charging station planning based on travel probability matrix, Transactions of China Electrotechnical Society, 34, S1, pp. 272-281, (2019)

[7]

Kautish P., Lavuri R., Roubaud D., Grebinevych O., Electric vehicles’ choice behaviour: an emerging market scenario, Journal of Environmental Management, 354, (2024)

[8]

Leou R.C., Su C.L., Teng J.H., Modelling and verifying the load behaviour of electric vehicle charging stations based on field measurements, Generation Transmission and Distribution Iet, 9, 11, pp. 1112-1119, (2015)

[9]

Li H., Du Z., Chen L., Et al., Electric vehicle charging load prediction model based on travel prediction and V2G evaluation, Automation of Electric Power Systems, 43, 21, pp. 88-96, (2019)

[10]

Liu S., Zhou Z., Liu Y., Et al., Degradation stage division of AC contactor contact system based on vibration signals, High Voltage Technology, 49, 12, pp. 4971-4981, (2023)

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