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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

References

[1]
Feng, Y., Zhao, X., Ren, G., Zhao, J. (2018). Planning method for urban centralized charging stations. Proceedings of the CSU-EPSA, 30(8): 58–61+67. (in Chinese)
[2]
Jia, L., Hu, Z., Song, Y. (2016). An integrated planning of electric vehicle charging facilities for urban area considering different types of charging demands. Power System Technology, 40(9): 2579–2587. (in Chinese)
[3]
Ge, S., Feng, L., Liu, H., Wang, L. (2013). Planning of charging stations considering traffic flow and capacity constraints of distribution network. Power System Technology, 37(3): 582–589. (in Chinese)
[4]
Huang, W. (2023). Research on electric vehicle charging load forecasting and charging station siting and sizing. Master's Thesis, Nanjing University of Posts and Telecommunications, China. (in Chinese)
[5]
Guo, D., Li, J., Zheng, C., Ma, P., Zhang, T., Li, C. (2023). Layout planning method for emergency charging facilities of electric vehicles. Automation of Electric Power Systems, 47(16): 66–75. (in Chinese)
[6]
Xu, Q., Cai, T., Liu Y., Yao, L., Zeng, P. (2016). Location planning of charging stations for electric vehicles based on drivers’ behaviours and travel chain. Automation of Electric Power Systems, 40(4): 59–65+77. (in Chinese)
[7]

Wu, F., Sioshansi, R. (2017). A stochastic flow-capturing model to optimize the location of fast-charging stations with uncertain electric vehicle flows. Transportation Research Part D: Transport and Environment, 53: 354–376.
Crossref Google Scholar
[8]

Kchaou-Boujelben, M., Gicquel, C. (2020). Locating electric vehicle charging stations under uncertain battery energy status and power consumption. Computers & Industrial Engineering, 149: 106752.
Crossref Google Scholar
[9]

Arslan, O., Karaşan, O. E. (2016). A benders decomposition approach for the charging station location problem with plug-in hybrid electric vehicles. Transportation Research Part B: Methodological, 93: 670–695.
Crossref Google Scholar
[10]

Lee, C., Han, J. (2017). Benders-and-price approach for electric vehicle charging station location problem under probabilistic travel range. Transportation Research Part B: Methodological, 106: 130–152.
Crossref Google Scholar
[11]

Hodgson, M. J. (1990). A flow-capturing location-allocation model. Geographical Analysis, 22: 270–279.
Crossref Google Scholar
[12]
Wang, H., Wang, G., Zhao, J., Wen, F., Li, J. (2013). Optimal planning for electric vehicle charging stations considering traffic network flows. Automation of Electric Power Systems, 37(13): 63–69+98. (in Chinese)
[13]
Yao, W., Zhao, J., Wen, F., Xue, Y., Dong, Z. (2015). Coordinated planning for power distribution system and electric vehicle charging infrastructures. Automation of Electric Power Systems, 39(9): 10–18. (in Chinese)
[14]
Huang, X., Yang, H., Chen, J., Jang, L., Cao, Y. (2015). Optimal planning of electric vehicle charging stations based on life cycle cost and quantum genetic algorithm. Automation of Electric Power Systems, 39(17): 176–182. (in Chinese)
[15]
Lian, J., Li, X., Zhao, W., Ji, J., Zhao, W. (2008). Design and implementation of 3D modeling and visualization system based on ArcScene. Geospatial Information, 1: 83–86. (in Chinese)
[16]
Zhang, L., Zhou, C., Ma, M., Liu, X. (2004). Solutions of multi-objective optimization problems based on particle swarm optimization. Journal of Computer Research and Development, 07: 1286–1291. (in Chinese)
[17]
Wang, L., Pan, J., Jiao, L. (2000). The immune algorithm. Acta Electronica Sinica, 23(7): 74–78. (in Chinese)
[18]
Lin, J., Wang, X. (2009). Reactive power optimization based on adaptive immune algorithm. International Journal of Emerging Electric Power Systems, 10: Article 1.
Crossref
[19]

Yuan, J., Zhao, Z., Chen, B., Li, C., Wang, J., Tian, C., Chen, Y. (2015). An immune-algorithm-based dead-time elimination PWM control strategy in a single-phase inverter. IEEE Transactions on Power Electronics, 30: 3964–3975.
Crossref Google Scholar
[20]
Mei, Q. (2020). Research on optimization of cold chain logistics distribution route based on immune particle swarm optimization algorithm. Master’s Thesis, Xidian University, China.
[21]
Gao, Y., Xie, S. (2004). Particle swarm optimization algorithms with immunity. Computer Engineering and Applications, 2004(06): 4–6+33. (in Chinese)
[22]

Yang, B. (2019). Dynamic risk identification safety model based on fuzzy support vector machine and immune optimization algorithm. Safety Science, 118: 205–211.
Crossref Google Scholar
[23]

Xue, X. (2018). Evaluation of concrete compressive strength based on an improved PSO-LSSVM model. Computers and Concrete, 21: 505–511.
Google Scholar
[24]
Zhang, C., Li, Q., Wang. W., Chen, P., Feng, Y. (2017). Immune particle swarm optimization algorithm based on the adaptive search strategy. Chinese Journal of Engineering, 39(1): 125–132. (in Chinese)
[25]
Li, H., Song, Y., Li, S., Zhu, Y., Kang, Y., Dong, H. (2023). Electric vehicle charging load prediction based on ArcGIS road network structure and traffic congestion analysis. Power System Technology, https://doi.org/10.13335/j.1000-3673.pst.2023.1420. (in Chinese)
Crossref
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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