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Article | Open Access

Optimal urban EV charging station site selection and capacity determination considering comprehensive benefits of vehicle–station–grid

Hongwei Li1, Yufeng Song1, Jiuding Tan2, Yi Cui3, Shuaibing Li2( ), Yongqiang Kang2, Haiying Dong2
School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
School of Engineering, University of Southern Queensland, Springfield 4300, Australia
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Abstract

This paper presents an optimization model for the location and capacity of electric vehicle (EV) charging stations. The model takes the multiple factors of the “vehicle–station–grid” system into account. Then, ArcScene is used to couple the road and power grid models and ensure that the coupling system is strictly under the goal of minimizing the total social cost, which includes the operator cost, user charging cost, and power grid loss. An immune particle swarm optimization algorithm (IPSOA) is proposed in this paper to obtain the optimal coupling strategy. The simulation results show that the algorithm has good convergence and performs well in solving multi-modal problems. It also balances the interests of users, operators, and the power grid. Compared with other schemes, the grid loss cost is reduced by 11.1% and 17.8%, and the total social cost decreases by 9.96% and 3.22%.

Keywords

EVscharging stationsite selection and capacity determinationArcSceneimmune particle swarm optimization algorithm (IPSOA)road electrical coupling

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iEnergy
Volume 3 Issue 3,
September 2024
Pages 162-174
DOI: 10.23919/IEN.2024.0021
Cite this article:
Li H, Song Y, Tan J, et al. Optimal urban EV charging station site selection and capacity determination considering comprehensive benefits of vehicle–station–grid. iEnergy, 2024, 3(3): 162-174. https://doi.org/10.23919/IEN.2024.0021

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Received: 29 July 2024
Revised: 18 September 2024
Accepted: 24 September 2024
Published: 09 October 2024
© The author(s) 2024.

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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