Open Access

OPTIMIZING ELECTRIC VEHICLE CHARGING INFRASTRUCTURE: A MULTI-OBJECTIVE GENETIC ALGORITHM APPROACH FOR SITING AND SIZING

https://doi.org/10.55640/ijnget-v02i03-01

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Dr. Javad Ahmadi ¹ , Dr. Yingjie Zhao ¹ ,

⁴ School of Electrical and Computer Engineering, University of Tehran, Iran

⁴ Department of Automation, Tsinghua University, China

Abstract

The rapid growth of electric vehicles (EVs) necessitates the strategic development of efficient charging infrastructure. This study proposes a multi-objective genetic algorithm (MOGA) approach for optimizing the siting and sizing of EV charging stations. The model incorporates multiple conflicting objectives, including cost minimization, user accessibility, grid stability, and environmental impact. By simulating various urban deployment scenarios, the algorithm identifies optimal solutions that balance these objectives, offering robust and scalable planning strategies. Results from case studies demonstrate that the MOGA-based framework significantly improves the efficiency and sustainability of EV charging infrastructure planning. The approach provides actionable insights for policymakers, urban planners, and utility companies aiming to support EV adoption and smart city initiatives.

Keywords

Electric vehicle charging, charging infrastructure, multi-objective optimization, genetic algorithm

References

Bae, S., Jang, I., Gros, S., Kulcsar, B., & Hellgren, J. (2022). A game approach for charging station placement based on user preferences and crowdedness. IEEE Transactions on Intelligent Transportation Systems, 23(4), 3654–3669. DOI: 10.1109/TITS.2020.3038938

Cai, H. Q., Dai, W., Zhao, J. Y., Wang, C., Zhang, Z. J., & Li, S. Y. (2022). Evaluation method of effective capacity of electric vehicle charging stations based on multi-parameter programming. Electric Power, 55(11), 175–183. DOI: 10.11930/j.issn.1004-9649.202206091

Cao, Q., & Zhu, Y. F. (2023). Location optimization of electric vehicle charging stations: Taking Pudong New Area of Shanghai as an example. Practical technology of automobile, 18–22

Cao, Y. L., Ni, X., & Gong, H. Y. (2024). Research on location of electric freight vehicle charging stations in rural areas based on AP clustering algorithm and interval intuitionistic fuzzy number. Journal of Tianjin University of Technology.1-10

Chen, J., Huang, X., Cao, Y., Li, L., Yan, K., Wu, L., & Liang, K. (2022). Electric vehicle charging schedule considering shared charging pile based on generalized Nash game. International Journal of Electrical Power & Energy Systems, 136, 107579. DOI: 10.1016/j.ijepes.2021.107579

Cheng, J. C. (2023). Research on location and capacity method of electric vehicle charging stations considering charging user behavior. Shanghai Institute of Electric Engineering master's degree thesis.82. DOI: 10.27818/d.cnki.gshdj.2023.000046

Cheng, J. X. (2023). Research on location and capacity planning of urban electric vehicle charging stations. Shanghai Institute of Electric Engineering master's degree thesis.66. DOI: 10.27818/d.cnki.gshdj.2023.000059

Chi, L., Yuan, Y., & Xu, T. T. (2021). Research on site selection and capacity determination of charging facilities based on thermal analysis diagram of EV and charging pile. ICITEE ’20: Proceedings of the 3rd International Conference on Information Technologies and Electrical Engineering, 303–307. DOI: 10.1145/3452940.3452998

Chumbi, W. E., Martinez-Minga, R., Zambrano-Asanza, S., Leite, B., & Franco, J. F. (2024). Suitable site selection of public charging stations: A fuzzy TOPSIS MCDA framework on capacity substation assessment. Energies, 17(14), 3452. DOI: 10.3390/en17143452

Ding, Y. Q. (2024).Location optimization of electric vehicle charging stations based on charging demand. Master's thesis of Anhui Normal University.62. DOI: 10.26920/d.cnki.gansu.2024.000136

Du, J., Jing, H., Choo, K. K. R., Sugumaran, V., & Castro-Lacouture, D. (2020). An ontology and multi-agent based decision support framework for prefabricated component supply chain. Information Systems Frontiers, 22(6), 1467–1485. DOI: 10.1007/s10796-019-09941-x

Fang, Z., Zhang, J., Sun, Y., & Chen, S. Y. (2020). Study on quantity planning of charging piles considering multi-objective constraints. Power System Technology, 44(2), 704–710. DOI: 10.13335/j.1000-3673.pst.2019.0359

Filippi, C., Guastaroba, G., Peirano, L., & Speranza, M. (2023). Iccorporating time-dependent demand patterns in the optimal location of capacitated charging stations. Transportation Research Part C, Emerging Technologies, 152, 104145. DOI: 10.1016/j.trc.2023.104145

Gui, Q., Shi, Y., & Zhou, Y. (2021). Optimal configuration model of photovoltaic and energy storage capacity of charging station considering battery dynamic characteristics. Electric Power Construction, 42(5), 90–99. DOI: 10.12204/j.issn.1000-7229.2021.05.010

Hu, Y. S., Qin, C. Y., Lü, S. H., & Wu, H. J. (2024). Research on location of electric vehicle charging stations considering user satisfaction and range anxiety. Wuhan Ligong Daxue Xuebao.

Jia, R., Gao, Y. D., & Zhao, J. (2024). Low-carbon planning for electric vehicle charging station location and capacity in electro-road coupled network. Journal of Shanghai Electric Power University, 40(2), 109–114.

Li, J., Liu, C., Yi, K., Fan, L., & Wu, Z. (2025). An adaptive NSGA-II for electric vehicle routing problem with charging/discharging based on time-of-use electricity pricing and diverse charging stations. Applied Soft Computing, 170, 112704. DOI: 10.1016/j.asoc.2025.112704

Meng, T., Guo, H. G., & Zhang, C. M. (2024). Research on intelligent location and capacity determination method of electric vehicle charging stations. Journal of Taiyuan University of Science and Technology, 45(4), 342–347.

Mu, L. F., Sugumaran, V., & Wang, F. Y. (2020). A hybrid genetic algorithm for software architecture re-modularization. Information Systems Frontiers, 22(5), 1133–1161. DOI: 10.1007/s10796-019-09906-0

Nandhini, I., Manjula, D., & Sugumaran, V. (2022). Multi-class brain disease classification using modified pre-trained convolutional neural networks model with substantial data augmentation. Journal of Medical Imaging and Health Informatics, 12(2), 168–183. Advance online publication. DOI: 10.1166/jmihi.2022.3936

Suganeshwari, G., Syed Ibrahim, S. I. P. M., & Sugumaran, V. (2023). A graph-based collaborative filtering algorithm combining implicit user preference and explicit time-related feedback. Neural Computing & Applications, 35(36), 25235–25247. DOI: 10.1007/s00521-023-08694-8

Sun, Z. Y., Ning, A. B., & Fu, T. Y. (2024). Reduced-order backtracking algorithm for charging station location based on existing gas station distribution. Operations Research and Management Science.

Verma, R., Sharma, S. K., Singh, P., Bhutto, J. K., & Alharbi, A. R. A. (2022). Analysis and sizing of charging stations in Kota city. Sustainability (Basel), 14(18), 11759. DOI: 10.3390/su141811759

Wang, Y.-M., He, S.-F., García Zamora, D., Pan, X.-H., & Martínez, L. (2023). A large scale group three-way decision-based consensus model for site selection of new energy vehicle charging stations. Expert Systems with Applications, 214, 119107. DOI: 10.1016/j.eswa.2022.119107

Xiao, B., & Gao, F. (2022). Location and capacity optimization method for electric vehicle charging stations with different capacity charging piles. Dianli Zidonghua Shebei, 42(10), 157–166.

Xu, X. (2022). Analysis of charging pile location in Hanjiang District based on GIS. Geographical Research, 11(6), 568–576. DOI: 10.12677/GSER.2022.116058

Zhang, K., & Cheng, L. (2024). Research on optimal site selection for electric vehicle charging stations. PEAI ’24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence, 781–785. DOI:DOI: 10.1145/3674225.3674366

Zhang, Y., Wang, Y., Li, F., Wu, B., Chiang, Y. Y., & Zhang, X. (2021). Efficient deployment of electric vehicle charging infrastructure: Simultaneous optimization of charging station placement and charging pile assignment. IEEE Transactions on Intelligent Transportation Systems, 22(10), 6654–6659. DOI: 10.1109/TITS.2020.2990694

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International Journal of Next-Generation Engineering and Technology

OPTIMIZING ELECTRIC VEHICLE CHARGING INFRASTRUCTURE: A MULTI-OBJECTIVE GENETIC ALGORITHM APPROACH FOR SITING AND SIZING

Abstract

Keywords

References

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