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Electric Charging Demand Location Model—A User- and Destination-Based Locating Approach for Electric Vehicle Charging Stations

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  • Raphaela Pagany

    (Institute for Applied Informatics, Deggendorf Institute of Technology, Grafenauer Straße 22, 94078 Freyung, Germany
    Interfaculty Department of Geoinformatics Z_GIS, University of Salzburg, Schillerstraße 50, 5020 Salzburg, Austria)

  • Anna Marquardt

    (Institute for Applied Informatics, Deggendorf Institute of Technology, Grafenauer Straße 22, 94078 Freyung, Germany)

  • Roland Zink

    (Institute for Applied Informatics, Deggendorf Institute of Technology, Grafenauer Straße 22, 94078 Freyung, Germany)

Abstract

In recent years, with the increased focus on climate protection, electric vehicles (EVs) have become a relevant alternative to conventional motorized vehicles. Even though the market share of EVs is still comparatively low, there are ongoing considerations for integrating EVs in transportation systems. Along with pushing EV sales numbers, the installation of charging infrastructure is necessary. This paper presents a user- and destination-based approach for locating charging stations (CSs) for EVs—the electric charging demand location (ECDL) model. With regard to the daily activities of potential EV users, potential positions for CSs are derived on a micro-location level in public and semipublic spaces using geographic information systems (GIS). Depending on the vehicle users’ dwell times and visiting frequencies at potential points of interest (POIs), the charging demand at such locations is calculated. The model is mainly based on a survey analyzing the average time spent per daily activity, regional data about driver and vehicle ownership numbers, and the georeferenced localization of regularly visited POIs. Optimal sites for parking and charging EVs within the POIs neighborhood are selected based on walking distance calculations, including spatial neighborhood effects, such as the density of POIs. In a case study in southeastern Germany, the model identifies concrete places with the highest overall demand for CSs, resulting in an extensive coverage of the electric energy demand while considering as many destinations within the acceptable walking distance threshold as possible.

Suggested Citation

  • Raphaela Pagany & Anna Marquardt & Roland Zink, 2019. "Electric Charging Demand Location Model—A User- and Destination-Based Locating Approach for Electric Vehicle Charging Stations," Sustainability, MDPI, vol. 11(8), pages 1-15, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:8:p:2301-:d:223512
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    References listed on IDEAS

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    Cited by:

    1. Mylène van der Koogh & Emile Chappin & Renée Heller & Zofia Lukszo, 2021. "Are We Satisfying the Right Conditions for the Mobility Transition? A Review and Evaluation of the Dutch Urban Mobility Policies," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    2. Yi, Tao & Cheng, Xiaobin & Peng, Peng, 2022. "Two-stage optimal allocation of charging stations based on spatiotemporal complementarity and demand response: A framework based on MCS and DBPSO," Energy, Elsevier, vol. 239(PC).
    3. Bolong Yun & Daniel (Jian) Sun & Yingjie Zhang & Siwen Deng & Jing Xiong, 2019. "A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users," Sustainability, MDPI, vol. 11(20), pages 1-23, October.
    4. Mario Porru & Alessandro Serpi & Mario Mureddu & Alfonso Damiano, 2020. "A Multistage Design Procedure for Planning and Implementing Public Charging Infrastructures for Electric Vehicles," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
    5. Shuping Wu & Zan Yang, 2020. "Availability of Public Electric Vehicle Charging Pile and Development of Electric Vehicle: Evidence from China," Sustainability, MDPI, vol. 12(16), pages 1-14, August.
    6. Roland Zink & Javier Valdes & Jane Wuth, 2020. "Prioritizing the Chicken or Egg? Electric Vehicle Purchase and Charging Infrastructure Subsidies in Germany," Politics and Governance, Cogitatio Press, vol. 8(3), pages 185-198.
    7. Emilia M. Szumska & Rafał S. Jurecki, 2021. "Parameters Influencing on Electric Vehicle Range," Energies, MDPI, vol. 14(16), pages 1-23, August.
    8. 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.

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