IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v342y2023ics0306261923004804.html
   My bibliography  Save this article

Optimal expansion planning of electric vehicle fast charging stations

Author

Listed:
  • Woo, Hyeon
  • Son, Yongju
  • Cho, Jintae
  • Kim, Sung-Yul
  • Choi, Sungyun

Abstract

With the penetration rate of electric vehicles (EVs) increasing exponentially, the high charging load may cause new issues in future power system, e.g., voltage drop. Besides, EV Charging stations (EVCSs) located in inadequate places exacerbate these issues, causing charging demand to concentrate on a few EVCSs. Therefore, the charging demand estimation for EVCSs and their strategic placement is essential for the system operators and charging station owner. The paper proposes a method for optimal EVCS placement to achieve charging demand dispersion considering not only installation cost but also drivers’ preferences and existing charging stations. To optimally place EVCSs, this work first estimates the charging demand of existing EVCSs based on kernel density estimation. The charging demand for new and existing EVCSs is modeled using the nearest neighbor search to consider drivers’ preference for the nearer station. Next, the EVCS placement problem is formulated to minimize the peak charging demand using integer nonlinear programming, a non-convex problem. To tackle this non-convex problem, a minimax genetic algorithm is proposed, which is genetic algorithm combined with game theory. The validity and effectiveness of the proposed method are demonstrated through simulations based on real data from Jeju Island. After applying the proposed method, new EVCS placement is determined by analyzing the tradeoff between the degree of charging demand dispersion and installation cost. As a result, the charging demand concentrated on a particular EVCS is suitably dispersed.

Suggested Citation

  • Woo, Hyeon & Son, Yongju & Cho, Jintae & Kim, Sung-Yul & Choi, Sungyun, 2023. "Optimal expansion planning of electric vehicle fast charging stations," Applied Energy, Elsevier, vol. 342(C).
    • Handle: RePEc:eee:appene:v:342:y:2023:i:c:s0306261923004804
    DOI: 10.1016/j.apenergy.2023.121116
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923004804
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121116?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Guo, Sen & Zhao, Huiru, 2015. "Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective," Applied Energy, Elsevier, vol. 158(C), pages 390-402.
    2. Vazifeh, Mohammad M. & Zhang, Hongmou & Santi, Paolo & Ratti, Carlo, 2019. "Optimizing the deployment of electric vehicle charging stations using pervasive mobility data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 75-91.
    3. Liu, Jin-peng & Zhang, Teng-xi & Zhu, Jiang & Ma, Tian-nan, 2018. "Allocation optimization of electric vehicle charging station (EVCS) considering with charging satisfaction and distributed renewables integration," Energy, Elsevier, vol. 164(C), pages 560-574.
    4. Bi, Zicheng & Keoleian, Gregory A. & Ersal, Tulga, 2018. "Wireless charger deployment for an electric bus network: A multi-objective life cycle optimization," Applied Energy, Elsevier, vol. 225(C), pages 1090-1101.
    5. Huiru Zhao & Nana Li, 2016. "Optimal Siting of Charging Stations for Electric Vehicles Based on Fuzzy Delphi and Hybrid Multi-Criteria Decision Making Approaches from an Extended Sustainability Perspective," Energies, MDPI, vol. 9(4), pages 1-22, April.
    6. Vasileiou, Margarita & Loukogeorgaki, Eva & Vagiona, Dimitra G., 2017. "GIS-based multi-criteria decision analysis for site selection of hybrid offshore wind and wave energy systems in Greece," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 745-757.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Meijuan & Yan, Qingyou & Guan, Yajuan & Ni, Da & Agundis Tinajero, Gibran David, 2024. "Joint planning of residential electric vehicle charging station integrated with photovoltaic and energy storage considering demand response and uncertainties," Energy, Elsevier, vol. 298(C).
    2. Rehman, Anis Ur & Ullah, Zia & Shafiq, Aqib & Hasanien, Hany M. & Luo, Peng & Badshah, Fazal, 2023. "Load management, energy economics, and environmental protection nexus considering PV-based EV charging stations," Energy, Elsevier, vol. 281(C).
    3. Abid, Md. Shadman & Apon, Hasan Jamil & Hossain, Salman & Ahmed, Ashik & Ahshan, Razzaqul & Lipu, M.S. Hossain, 2024. "A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning," Applied Energy, Elsevier, vol. 353(PA).
    4. Haiqing Gan & Wenjun Ruan & Mingshen Wang & Yi Pan & Huiyu Miu & Xiaodong Yuan, 2024. "Bi-Level Planning of Electric Vehicle Charging Stations Considering Spatial–Temporal Distribution Characteristics of Charging Loads in Uncertain Environments," Energies, MDPI, vol. 17(12), pages 1-30, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Danijela Tuljak-Suban & Patricija Bajec, 2022. "A Hybrid DEA Approach for the Upgrade of an Existing Bike-Sharing System with Electric Bikes," Energies, MDPI, vol. 15(21), pages 1-23, October.
    2. Ömer Kaya & Kadir Diler Alemdar & Tiziana Campisi & Ahmet Tortum & Merve Kayaci Çodur, 2021. "The Development of Decarbonisation Strategies: A Three-Step Methodology for the Suitable Analysis of Current EVCS Locations Applied to Istanbul, Turkey," Energies, MDPI, vol. 14(10), pages 1-21, May.
    3. Wang, Hua & Zhao, De & Cai, Yutong & Meng, Qiang & Ong, Ghim Ping, 2021. "Taxi trajectory data based fast-charging facility planning for urban electric taxi systems," Applied Energy, Elsevier, vol. 286(C).
    4. Zhang, Lihui & Zhao, Zhenli & Yang, Meng & Li, Songrui, 2020. "A multi-criteria decision method for performance evaluation of public charging service quality," Energy, Elsevier, vol. 195(C).
    5. Vytautas Palevičius & Askoldas Podviezko & Henrikas Sivilevičius & Olegas Prentkovskis, 2018. "Decision-Aiding Evaluation of Public Infrastructure for Electric Vehicles in Cities and Resorts of Lithuania," Sustainability, MDPI, vol. 10(4), pages 1-17, March.
    6. Betul Yagmahan & Hilal Yılmaz, 2023. "An integrated ranking approach based on group multi-criteria decision making and sensitivity analysis to evaluate charging stations under sustainability," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(1), pages 96-121, January.
    7. Sikder, Sujit Kumar & Nagarajan, Magesh & Mustafee, Navonil, 2023. "Augmenting EV charging infrastructure towards transformative sustainable cities: An equity-based approach," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    8. 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).
    9. Metais, M.O. & Jouini, O. & Perez, Y. & Berrada, J. & Suomalainen, E., 2022. "Too much or not enough? Planning electric vehicle charging infrastructure: A review of modeling options," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    10. Anastasios Tsakalidis & Andreea Julea & Christian Thiel, 2019. "The Role of Infrastructure for Electric Passenger Car Uptake in Europe," Energies, MDPI, vol. 12(22), pages 1-18, November.
    11. Wilson Enrique Chumbi & Roger Martínez-Minga & Sergio Zambrano-Asanza & Jonatas B. Leite & John Fredy Franco, 2024. "Suitable Site Selection of Public Charging Stations: A Fuzzy TOPSIS MCDA Framework on Capacity Substation Assessment," Energies, MDPI, vol. 17(14), pages 1-27, July.
    12. Erbaş, Mehmet & Kabak, Mehmet & Özceylan, Eren & Çetinkaya, Cihan, 2018. "Optimal siting of electric vehicle charging stations: A GIS-based fuzzy Multi-Criteria Decision Analysis," Energy, Elsevier, vol. 163(C), pages 1017-1031.
    13. Yunna Wu & Chao Xie & Chuanbo Xu & Fang Li, 2017. "A Decision Framework for Electric Vehicle Charging Station Site Selection for Residential Communities under an Intuitionistic Fuzzy Environment: A Case of Beijing," Energies, MDPI, vol. 10(9), pages 1-25, August.
    14. Paweł Ziemba & Jarosław Wątróbski & Magdalena Zioło & Artur Karczmarczyk, 2017. "Using the PROSA Method in Offshore Wind Farm Location Problems," Energies, MDPI, vol. 10(11), pages 1-20, November.
    15. Andrea Mazza & Angela Russo & Gianfranco Chicco & Andrea Di Martino & Cristian Giovanni Colombo & Michela Longo & Paolo Ciliento & Marco De Donno & Francesca Mapelli & Francesco Lamberti, 2024. "Categorization of Attributes and Features for the Location of Electric Vehicle Charging Stations," Energies, MDPI, vol. 17(16), pages 1-32, August.
    16. Mikołaj Schmidt & Paweł Zmuda-Trzebiatowski & Marcin Kiciński & Piotr Sawicki & Konrad Lasak, 2021. "Multiple-Criteria-Based Electric Vehicle Charging Infrastructure Design Problem," Energies, MDPI, vol. 14(11), pages 1-34, May.
    17. Shafqat Jawad & Junyong Liu, 2020. "Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends," Energies, MDPI, vol. 13(13), pages 1-24, July.
    18. Nock, Destenie & Baker, Erin, 2019. "Holistic multi-criteria decision analysis evaluation of sustainable electric generation portfolios: New England case study," Applied Energy, Elsevier, vol. 242(C), pages 655-673.
    19. Yang, Jun & Guo, Fang & Zhang, Min, 2017. "Optimal planning of swapping/charging station network with customer satisfaction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 174-197.
    20. Davidov, Sreten, 2020. "Optimal charging infrastructure planning based on a charging convenience buffer," Energy, Elsevier, vol. 192(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:342:y:2023:i:c:s0306261923004804. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.