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Geographic Information System-assisted optimal design of renewable powered electric vehicle charging stations in high-density cities

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  • Huang, Pei
  • Ma, Zhenjun
  • Xiao, Longzhu
  • Sun, Yongjun

Abstract

The crowded urban environment and busy traffic lead to heavy roadside pollutions in high-density cities, thereby causing health damages to city pedestrians. Electric vehicle (EV) is considered as a promising solution to such street-level air pollutions. Currently, in high-density cities, the number of public charging stations is limited, and they are far from enough to form a complete charging network with a high coverage ratio that can provide easy and convenient charging services for EV users. Concerns and worries on being unable to find a charging port when needed become a major hurdle to EV practical applications. Meanwhile, greener and cheaper renewable energy is recommended to replace fossil fuel-based grid energy that is commonly used in existing charging stations. Thus, this study proposes a novel Geographic Information System (GIS) assisted optimal design method for renewable powered EV charging stations in high-density cities. By selecting the optimal locations and optimal number of the renewable powered charging stations with the considerations of the existing charging stations and renewable potentials, the proposed method is able to minimize the life cycle cost of the charging stations while satisfying a user defined area coverage ratio. Using Hong Kong as an example, case studies have been conducted to verify the proposed design method. The design method can be used in practice to help high-density cities build their public charging networks with cost-effectiveness, which will promote EV practical applications and thus alleviate the roadside air pollutions in high-density cities.

Suggested Citation

  • Huang, Pei & Ma, Zhenjun & Xiao, Longzhu & Sun, Yongjun, 2019. "Geographic Information System-assisted optimal design of renewable powered electric vehicle charging stations in high-density cities," Applied Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:appene:v:255:y:2019:i:c:s0306261919315429
    DOI: 10.1016/j.apenergy.2019.113855
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    Cited by:

    1. Pillot, Benjamin & Al-Kurdi, Nadeem & Gervet, Carmen & Linguet, Laurent, 2020. "An integrated GIS and robust optimization framework for solar PV plant planning scenarios at utility scale," Applied Energy, Elsevier, vol. 260(C).
    2. Tan, Ruipeng & Lin, Boqiang, 2020. "Are people willing to support the construction of charging facilities in China?," Energy Policy, Elsevier, vol. 143(C).
    3. Duan, Ditao & Poursoleiman, Roza, 2021. "Modified teaching-learning-based optimization by orthogonal learning for optimal design of an electric vehicle charging station," Utilities Policy, Elsevier, vol. 72(C).
    4. Syed Taha Taqvi & Ali Almansoori & Azadeh Maroufmashat & Ali Elkamel, 2022. "Utilizing Rooftop Renewable Energy Potential for Electric Vehicle Charging Infrastructure Using Multi-Energy Hub Approach," Energies, MDPI, vol. 15(24), pages 1-21, December.
    5. Bastida-Molina, Paula & Hurtado-Pérez, Elías & Moros Gómez, María Cristina & Vargas-Salgado, Carlos, 2021. "Multicriteria power generation planning and experimental verification of hybrid renewable energy systems for fast electric vehicle charging stations," Renewable Energy, Elsevier, vol. 179(C), pages 737-755.
    6. Mrówczyńska, M. & Skiba, M. & Sztubecka, M. & Bazan-Krzywoszańska, A. & Kazak, J.K. & Gajownik, P., 2021. "Scenarios as a tool supporting decisions in urban energy policy: The analysis using fuzzy logic, multi-criteria analysis and GIS tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    7. 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).
    8. Asadi, Meysam & Ramezanzade, Mohsen & Pourhossein, Kazem, 2023. "A global evaluation model applied to wind power plant site selection," Applied Energy, Elsevier, vol. 336(C).
    9. Zhang, Sheng & Ai, Zhengtao & Lin, Zhang, 2021. "Novel demand-controlled optimization of constant-air-volume mechanical ventilation for indoor air quality, durability and energy saving," Applied Energy, Elsevier, vol. 293(C).
    10. Zhu, Rui & Kondor, Dániel & Cheng, Cheng & Zhang, Xiaohu & Santi, Paolo & Wong, Man Sing & Ratti, Carlo, 2022. "Solar photovoltaic generation for charging shared electric scooters," Applied Energy, Elsevier, vol. 313(C).
    11. Ren, Haoshan & Ma, Zhenjun & Chan, Antoni B. & Sun, Yongjun, 2023. "Optimal planning of municipal-scale distributed rooftop photovoltaic systems with maximized solar energy generation under constraints in high-density cities," Energy, Elsevier, vol. 263(PA).
    12. Andrea Stabile & Michela Longo & Wahiba Yaïci & Federica Foiadelli, 2020. "An Algorithm for Optimization of Recharging Stops: A Case Study of Electric Vehicle Charging Stations on Canadian’s Ontario Highway 401," Energies, MDPI, vol. 13(8), pages 1-19, April.
    13. 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.
    14. Panah, Payam Ghaebi & Bornapour, Mosayeb & Hemmati, Reza & Guerrero, Josep M., 2021. "Charging station Stochastic Programming for Hydrogen/Battery Electric Buses using Multi-Criteria Crow Search Algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    15. Ren, Haoshan & Xu, Chengliang & Ma, Zhenjun & Sun, Yongjun, 2022. "A novel 3D-geographic information system and deep learning integrated approach for high-accuracy building rooftop solar energy potential characterization of high-density cities," Applied Energy, Elsevier, vol. 306(PA).
    16. Antić, Tomislav & Capuder, Tomislav, 2024. "A geographic information system-based modelling, analysing and visualising of low voltage networks: The potential of demand time-shifting in the power quality improvement," Applied Energy, Elsevier, vol. 353(PA).
    17. Ren, Haoshan & Ma, Zhenjun & Ming Lun Fong, Alan & Sun, Yongjun, 2022. "Optimal deployment of distributed rooftop photovoltaic systems and batteries for achieving net-zero energy of electric bus transportation in high-density cities," Applied Energy, Elsevier, vol. 319(C).

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