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Particle swarm optimization based on data driven for EV charging station siting

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  • Yin, Linfei
  • Zhang, Yifan

Abstract

Charging stations are an important support facility for electric vehicles (EVs). Nowadays, the EV industry is developing rapidly, however, the imperfect construction of charging stations or the unreasonable choice of location has seriously reduced the desire of people to buy EVs and led to the problem of difficult charging of EVs in many areas. At present, the research field of EV charging station siting suffers from the inability to quickly and accurately calculate the optimal solution for charging station siting. In this regard, a particle swarm optimization based on deep neural networks modified boundaries (DNNMBPSO) is proposed for solving the problem. DNNMBPSO reduces the convergence value of the objective function by applying deep learning to modify the boundary of the particle swarm optimization. DNNMBPSO is an algorithm that combines heuristic and data driven. In this study, DNNMBPSO is applied for siting study in a system having 50 alternative points, 500 alternative points, and 1000 alternative points and a case of siting of electric vehicle charging stations in Nanning, Guangxi, China. The convergence value of the DNNMBPSO-based objective function is found to be at least 5.5 %, 1.7 %, 8.23 % and 14.7 %, lower compared to genetic algorithms, African vulture optimization algorithm, particle swarm optimization, and grey wolf optimization algorithms, respectively. Traditional heuristic optimization algorithms cannot find optimal solutions in large-scale systems, while DNNMBPSO shows feasibility in large-scale systems.

Suggested Citation

  • Yin, Linfei & Zhang, Yifan, 2024. "Particle swarm optimization based on data driven for EV charging station siting," Energy, Elsevier, vol. 310(C).
    • Handle: RePEc:eee:energy:v:310:y:2024:i:c:s0360544224029724
    DOI: 10.1016/j.energy.2024.133197
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    1. Gharibi, Mohamad Amin & Nafisi, Hamed & Askarian-abyaneh, Hossein & Hajizadeh, Amin, 2023. "Deep learning framework for day-ahead optimal charging scheduling of electric vehicles in parking lot," Applied Energy, Elsevier, vol. 349(C).
    2. Sulaiman, Mohd Herwan & Mustaffa, Zuriani & Zakaria, Nor Farizan & Saari, Mohd Mawardi, 2023. "Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle," Energy, Elsevier, vol. 279(C).
    3. Ren, Fei & Tian, Chenlu & Zhang, Guiqing & Li, Chengdong & Zhai, Yuan, 2022. "A hybrid method for power demand prediction of electric vehicles based on SARIMA and deep learning with integration of periodic features," Energy, Elsevier, vol. 250(C).
    4. Zhou, Guangyou & Zhu, Zhiwei & Luo, Sumei, 2022. "Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm," Energy, Elsevier, vol. 247(C).
    5. Xing, Zongyi & Zhu, Junlin & Zhang, Zhenyu & Qin, Yong & Jia, Limin, 2022. "Energy consumption optimization of tramway operation based on improved PSO algorithm," Energy, Elsevier, vol. 258(C).
    6. Men, Jinkun & Zhao, Chunmeng, 2024. "A Type-2 fuzzy hybrid preference optimization methodology for electric vehicle charging station location," Energy, Elsevier, vol. 293(C).
    7. shafiei, Mohammad & Ghasemi-Marzbali, Ali, 2023. "Electric vehicle fast charging station design by considering probabilistic model of renewable energy source and demand response," Energy, Elsevier, vol. 267(C).
    8. Zhang, Xiaoshun & Guo, Zhengxun & Pan, Feng & Yang, Yuyao & Li, Chuansheng, 2023. "Dynamic carbon emission factor based interactive control of distribution network by a generalized regression neural network assisted optimization," Energy, Elsevier, vol. 283(C).
    9. Li, Yanbin & Wang, Jiani & Wang, Weiye & Liu, Chang & Li, Yun, 2023. "Dynamic pricing based electric vehicle charging station location strategy using reinforcement learning," Energy, Elsevier, vol. 281(C).
    10. Yin, Wanjun & Liang, Wenbin & Ji, Jianbo, 2024. "Study on charge and discharge control strategy of improved PSO for EV," Energy, Elsevier, vol. 304(C).
    11. Jaikumar Shanmuganathan & Aruldoss Albert Victoire & Gobu Balraj & Amalraj Victoire, 2022. "Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    12. Wu, Ji & Su, Hao & Meng, Jinhao & Lin, Mingqiang, 2023. "Electric vehicle charging scheduling considering infrastructure constraints," Energy, Elsevier, vol. 278(PA).
    13. Bo Zhang & Meng Zhao & Xiangpei Hu, 2023. "Location planning of electric vehicle charging station with users’ preferences and waiting time: multi-objective bi-level programming model and HNSGA-II algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 61(5), pages 1394-1423, March.
    14. Du, Zhili & Zheng, Lirong & Lin, Boqiang, 2024. "Influence of charging stations accessibility on charging stations utilization," Energy, Elsevier, vol. 298(C).
    15. Bampos, Zafeirios N. & Laitsos, Vasilis M. & Afentoulis, Konstantinos D. & Vagropoulos, Stylianos I. & Biskas, Pantelis N., 2024. "Electric vehicles load forecasting for day-ahead market participation using machine and deep learning methods," Applied Energy, Elsevier, vol. 360(C).
    16. Fang, Xiaolun & Wang, Yubin & Dong, Wei & Yang, Qiang & Sun, Siyang, 2023. "Optimal energy management of multiple electricity-hydrogen integrated charging stations," Energy, Elsevier, vol. 262(PB).
    17. Zhou, Siyu & Han, Yang & Mahmoud, Karar & Darwish, Mohamed M.F. & Lehtonen, Matti & Yang, Ping & Zalhaf, Amr S., 2023. "A novel unified planning model for distributed generation and electric vehicle charging station considering multi-uncertainties and battery degradation," Applied Energy, Elsevier, vol. 348(C).
    18. Yin, Linfei & Lin, Chen, 2024. "Matrix Wasserstein distance generative adversarial network with gradient penalty for fast low-carbon economic dispatch of novel power systems," Energy, Elsevier, vol. 298(C).
    19. Huasheng Liu & Yu Li & Chongyu Zhang & Jin Li & Xiaowen Li & Yuqi Zhao, 2022. "Electric Vehicle Charging Station Location Model considering Charging Choice Behavior and Range Anxiety," Sustainability, MDPI, vol. 14(7), pages 1-19, April.
    20. Roy, Avipsa & Law, Mankin, 2022. "Examining spatial disparities in electric vehicle charging station placements using machine learning," SocArXiv hvw2t, Center for Open Science.
    21. Rajani, B. & Kommula, Bapayya Naidu, 2022. "An optimal energy management among the electric vehicle charging stations and electricity distribution system using GPC-RERNN approach," Energy, Elsevier, vol. 245(C).
    22. Chen, Yuanyi & Hu, Simon & Zheng, Yanchong & Xie, Shiwei & Hu, Qinru & Yang, Qiang, 2024. "Coordinated expansion planning of coupled power and transportation networks considering dynamic network equilibrium," Applied Energy, Elsevier, vol. 360(C).
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