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GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles

Author

Listed:
  • Yue Zhang

    (China University of Petroleum)

  • Qi Zhang

    (China University of Petroleum)

  • Arash Farnoosh

    (IFPEN - IFP Energies nouvelles)

  • Siyuan Chen

    (China University of Petroleum)

  • Yan Li

    (China University of Petroleum)

Abstract

The rapid development of electric vehicles can greatly alleviate the environmental problems and energy tension. However, the lack of public supporting facilities has become the biggest problem hinders its development. How to reasonably plan the placement of charging stations to meet the needs of electric vehicles has become an urgent situation in China. Different from private charging piles, charging station could help to break the limitation of short range. It also has a special dual attribute of public service and high investment. Therefore, a mathematically optimal model with two objective functions is developed to analyze the relationship between upfront investments and operating costs and service coverage of charging station system and it was solved by Particle Swarm Optimization. Besides, we take into account the conveniences of stations for charging vehicles and their influences on the loads of the power grid. Geographic Information System is used to overlay the traffic system diagram on power system diagram to find the alternative construction sites. In this study, a district in Beijing is analyzed using the proposed method and model. And the following suggestions are given: government should lead the construction of charging station; service ability needs to be enhanced; it is better to make more investment at earlier stage; constructions of charging stations can facilitate EV's development.

Suggested Citation

  • Yue Zhang & Qi Zhang & Arash Farnoosh & Siyuan Chen & Yan Li, 2019. "GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles," Post-Print hal-02009151, HAL.
    • Handle: RePEc:hal:journl:hal-02009151
    Note: View the original document on HAL open archive server: https://ifp.hal.science/hal-02009151
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    Cited by:

    1. Sadeghi, Mohammad & Yaghoubi, Saeed, 2024. "Optimization models for cloud seeding network design and operations," European Journal of Operational Research, Elsevier, vol. 312(3), pages 1146-1167.
    2. Zhao, Xiang & Cai, Bocheng & He, Jianhua & Kong, Xuesong, 2024. "Identifying potential rural residential areas for land consolidation using a data driven agent-based model," Land Use Policy, Elsevier, vol. 145(C).
    3. Lin, Xinyou & Li, Yalong & Zhang, Guangji, 2022. "Bi-objective optimization strategy of energy consumption and shift shock based driving cycle-aware bias coefficients for a novel dual-motor electric vehicle," Energy, Elsevier, vol. 249(C).
    4. Christos Karolemeas & Stefanos Tsigdinos & Panagiotis G. Tzouras & Alexandros Nikitas & Efthimios Bakogiannis, 2021. "Determining Electric Vehicle Charging Station Location Suitability: A Qualitative Study of Greek Stakeholders Employing Thematic Analysis and Analytical Hierarchy Process," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    5. Yuan-Yuan Wang & Yuan-Ying Chi & Jin-Hua Xu & Jia-Lin Li, 2021. "Consumer Preferences for Electric Vehicle Charging Infrastructure Based on the Text Mining Method," Energies, MDPI, vol. 14(15), pages 1-20, July.
    6. Morro-Mello, Igoor & Padilha-Feltrin, Antonio & Melo, Joel D. & Heymann, Fabian, 2021. "Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory," Energy, Elsevier, vol. 235(C).
    7. Youssef Amry & Elhoussin Elbouchikhi & Franck Le Gall & Mounir Ghogho & Soumia El Hani, 2022. "Electric Vehicle Traction Drives and Charging Station Power Electronics: Current Status and Challenges," Energies, MDPI, vol. 15(16), pages 1-30, August.
    8. 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).
    9. Lukáš Dvořáček & Martin Horák & Michaela Valentová & Jaroslav Knápek, 2020. "Optimization of Electric Vehicle Charging Points Based on Efficient Use of Chargers and Providing Private Charging Spaces," Energies, MDPI, vol. 13(24), pages 1-28, December.
    10. Du, Zhili & Zheng, Lirong & Lin, Boqiang, 2024. "Influence of charging stations accessibility on charging stations utilization," Energy, Elsevier, vol. 298(C).
    11. Chen, Chong & Liu, Ying & Sun, Xianfang & Cairano-Gilfedder, Carla Di & Titmus, Scott, 2021. "An integrated deep learning-based approach for automobile maintenance prediction with GIS data," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    12. Essam H. Houssein & Sanchari Deb & Diego Oliva & Hegazy Rezk & Hesham Alhumade & Mokhtar Said, 2021. "Performance of Gradient-Based Optimizer on Charging Station Placement Problem," Mathematics, MDPI, vol. 9(21), pages 1-16, November.
    13. Gönül, Ömer & Duman, A. Can & Güler, Önder, 2024. "A comprehensive framework for electric vehicle charging station siting along highways using weighted sum method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    14. Mohd Bilal & Ibrahim Alsaidan & Muhannad Alaraj & Fahad M. Almasoudi & Mohammad Rizwan, 2022. "Techno-Economic and Environmental Analysis of Grid-Connected Electric Vehicle Charging Station Using AI-Based Algorithm," Mathematics, MDPI, vol. 10(6), pages 1-40, March.

    More about this item

    Keywords

    Electric vehicle; Charging station; Multi-objective particle swarm optimization; GIS;
    All these keywords.

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