Placement Optimal of Electric Vehicle Charging Stations Considering Location Constraints in Distribution Networks Integrated Distributed Generation
Published online: 03/02/2026
Corressponding author's email:
vndieu@hcmut.edu.vnDOI:
https://doi.org/10.54644/jte.2026.1765Keywords:
Electric vehicle, Symbiotic Organisms Search, EV charging station, Land cost index, Distribution networkAbstract
Electric vehicles (EVs) are receiving significant attention from countries worldwide due to their outstanding environmental advantages. However, this development has led to the rapid increase and uneven geographical distribution of electric vehicle charging stations (EVCS), which puts pressure on and causes some negative impacts on the power system, particularly the distribution network. Optimizing the placement of EVCS is crucial as it directly affects the efficiency and stability of the power grid, including energy losses, voltage quality, and harmonic distortion. This study focuses on optimizing the placement of EVCS in the distribution network, taking into account cost constraints and EV traffic density, with the objective of minimizing power losses, supporting investment decisions, and ensuring operational indicators of the system. An improved meta-heuristic method combining the Symbiotic Organisms Search (SOS) algorithm and a Chaotic search function (CSOS) is proposed to enhance the search efficiency for solving the problem. The IEEE 34 bus standard distribution network was used to test and evaluate the method through simulations performed in Matlab R2022a. The results from CSOS were assessed and compared with previous studies to demonstrate the superiority and effectiveness of the proposed solution.
Downloads: 0
References
H. Zhang, Z. Hu, Z. Xu, and Y. Song, “An Integrated Planning Framework for Different Types of PEV Charging Facilities in Urban Area,” IEEE Trans. Smart Grid, vol. 7, no. 5, pp. 2273–2284, Sep. 2016, doi: 10.1109/TSG.2015.2436069. DOI: https://doi.org/10.1109/TSG.2015.2436069
M. S. K. Reddy and K. Selvajyothi, “Optimal placement of electric vehicle charging station for unbalanced radial distribution systems,” Energy Sources, Part A Recover. Util. Environ. Eff., pp. 1–15, Feb. 2020, doi: 10.1080/15567036.2020.1731017. DOI: https://doi.org/10.1080/15567036.2020.1731017
W. S. T. Fokui, M. J. Saulo, and L. Ngoo, “Optimal Placement of Electric Vehicle Charging Stations in a Distribution Network With Randomly Distributed Rooftop Photovoltaic Systems,” IEEE Access, vol. 9, pp. 132397–132411, 2021, doi: 10.1109/ACCESS.2021.3112847. DOI: https://doi.org/10.1109/ACCESS.2021.3112847
T. J. Ai and V. Kachitvichyanukul, “A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery,” Computers & Operations Research, vol. 36, no. 5, pp. 1693-1702, 2009, doi: 10.1016/j.cor.2008.04.003. DOI: https://doi.org/10.1016/j.cor.2008.04.003
M. R. Mozafar, M. H. Moradi, and M. H. Amini, “A simultaneous approach for optimal allocation of renewable energy sources and electric vehicle charging stations in smart grids based on improved GA-PSO algorithm,” Sustain. Cities Soc., vol. 32, pp. 627–637, 2017, doi: 10.1016/j.scs.2017.05.007. DOI: https://doi.org/10.1016/j.scs.2017.05.007
K. Singh, K. D. Mistry, and H. G. Patel, “Optimal Placement of Electric Vehicle Charging Station and DG in a Distribution System for Loss Minimization,” 2023 IEEE 3rd Int. Conf. Sustain. Energy Futur. Electr. Transp. SeFet 2023, no. February, pp. 1–6, 2023, doi: 10.1109/SeFeT57834.2023.10244845. DOI: https://doi.org/10.1109/SeFeT57834.2023.10244845
L. Chen, C. Xu, H. Song, and K. Jermsittiparsert, “Optimal sizing and sitting of EVCS in the distribution system using metaheuristics: A case study,” Energy Reports, vol. 7, pp. 208–217, 2021, doi: 10.1016/j.egyr.2020.12.032. DOI: https://doi.org/10.1016/j.egyr.2020.12.032
F. Ahmad, M. Marzband, A. Iqbal, I. Ashraf, and I. Khan, “Placement of Electric Vehicle Fast Charging Stations using Grey Wolf Optimization in Electrical Distribution Network,” PESGRE 2022 - IEEE Int. Conf. “Power Electron. Smart Grid, Renew. Energy,” no. March 2023, 2022, doi: 10.1109/PESGRE52268.2022.9715842. DOI: https://doi.org/10.1109/PESGRE52268.2022.9715842
F. Ahmad, I. Ashraf, and A. Iqbal, “Placement of FCS Considering Power Loss, Land Cost, and EV Population,” in 2023 International Conference on Power, Instrumentation, Energy and Control, PIECON 2023, Feb. 2023, pp. 1–6. doi: 10.1109/PIECON56912.2023.10085898. DOI: https://doi.org/10.1109/PIECON56912.2023.10085898
A. E. Ezugwu and D. Prayogo, “Symbiotic Organisms Search Algorithm: theory, recent advances and applications,” Expert Systems with Applications, vol. 119. pp. 184–209, Apr. 2019. doi: 10.1016/j.eswa.2018.10.045. DOI: https://doi.org/10.1016/j.eswa.2018.10.045
M. Y. Cheng and D. Prayogo, “Symbiotic organisms search: a new metaheuristic optimization algorithm,” Comput. Struct., vol. 139, pp. 98–112, 2014. DOI: https://doi.org/10.1016/j.compstruc.2014.03.007
J. Ji, S. Gao, S. Wang, Y. Tang, H. Yu, and Y. Todo, “Self-adaptive gravitational search algorithm with a modified chaotic local search,” Ieee Access, vol. 5, pp. 17881–17895, 2017. DOI: https://doi.org/10.1109/ACCESS.2017.2748957
G. G. Wang, L. Guo, A. H. Gandomi, G. S. Hao, and H. Wang, “Chaotic Krill Herd algorithm,” Inf. Sci. (Ny)., vol. 274, pp. 17–34, 2014, doi: 10.1016/j.ins.2014.02.123. DOI: https://doi.org/10.1016/j.ins.2014.02.123
F. Ahmad, A. Iqbal, I. Ashraf, M. Marzband, and I. Khan, “Placement of electric vehicle fast charging stations in distribution network considering power loss, land cost, and electric vehicle population,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 44, no. 1, pp. 1693–1709, 2022, doi: 10.1080/15567036.2022.2055233. DOI: https://doi.org/10.1080/15567036.2022.2055233
Downloads
Published
How to Cite
License
Copyright (c) 2026 Journal of Technical Education Science

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright © JTE.


