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Analyzing the Charging Flexibility Potential of Different Electric Vehicle Fleets Using Real-World Charging Data

Author

Listed:
  • Vincent Barthel

    (Energy Informatics, Computer Science 7, Friedrich-Alexander-University Erlangen-Nürnberg, 91058 Erlangen, Germany)

  • Jonas Schlund

    (Energy Informatics, Computer Science 7, Friedrich-Alexander-University Erlangen-Nürnberg, 91058 Erlangen, Germany)

  • Philipp Landes

    (The Mobility House, 81669 Munich, Germany)

  • Veronika Brandmeier

    (The Mobility House, 81669 Munich, Germany)

  • Marco Pruckner

    (Energy Informatics, Computer Science 7, Friedrich-Alexander-University Erlangen-Nürnberg, 91058 Erlangen, Germany)

Abstract

A successful transformation of the energy and transportation sector is one of the main targets for our society today. Battery electric vehicles can play a key role in future renewable-based energy supply systems because of their ability to store electrical power. Additionally, they provide significant charging flexibility due to the long parking durations. In this paper, we provide insights into the temporal and power-specific flexibility behavior of three different vehicle fleets. These fleets are pool vehicles of office employees, a public authority, and a logistics company. Several parameters, such as the average charging power per charging event or the average plug-in duration per charging event, are discussed. Additionally, we investigate different charging rates and their impact on the temporal flexibility of the charging events. The data analysis shows that the logistics site has the most homogeneous charging profile as well as high charging flexibility, in contrast to the office and public agency site. The results are of significant importance for future applications in the field of smart charging and ancillary services provision.

Suggested Citation

  • Vincent Barthel & Jonas Schlund & Philipp Landes & Veronika Brandmeier & Marco Pruckner, 2021. "Analyzing the Charging Flexibility Potential of Different Electric Vehicle Fleets Using Real-World Charging Data," Energies, MDPI, vol. 14(16), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4961-:d:613695
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    References listed on IDEAS

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    1. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M. & Jenkins, Nick & Carroll, Steve & Barker, Myles, 2016. "A data-driven approach for characterising the charging demand of electric vehicles: A UK case study," Applied Energy, Elsevier, vol. 162(C), pages 763-771.
    2. Gonzalez Venegas, Felipe & Petit, Marc & Perez, Yannick, 2021. "Active integration of electric vehicles into distribution grids: Barriers and frameworks for flexibility services," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    3. Manu Lahariya & Dries F. Benoit & Chris Develder, 2020. "Synthetic Data Generator for Electric Vehicle Charging Sessions: Modeling and Evaluation Using Real-World Data," Energies, MDPI, vol. 13(16), pages 1-18, August.
    4. Martin Spitzer & Jonas Schlund & Elpiniki Apostolaki-Iosifidou & Marco Pruckner, 2019. "Optimized Integration of Electric Vehicles in Low Voltage Distribution Grids," Energies, MDPI, vol. 12(21), pages 1-19, October.
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    Cited by:

    1. Afentoulis, Konstantinos D. & Bampos, Zafeirios N. & Vagropoulos, Stylianos I. & Keranidis, Stratos D. & Biskas, Pantelis N., 2022. "Smart charging business model framework for electric vehicle aggregators," Applied Energy, Elsevier, vol. 328(C).
    2. Ali Saadon Al-Ogaili & Ali Q. Al-Shetwi & Hussein M. K. Al-Masri & Thanikanti Sudhakar Babu & Yap Hoon & Khaled Alzaareer & N. V. Phanendra Babu, 2021. "Review of the Estimation Methods of Energy Consumption for Battery Electric Buses," Energies, MDPI, vol. 14(22), pages 1-28, November.
    3. Amra Jahic & Felix Heider & Maik Plenz & Detlef Schulz, 2022. "Flexibility Quantification and the Potential for Its Usage in the Case of Electric Bus Depots with Unidirectional Charging," Energies, MDPI, vol. 15(10), pages 1-18, May.

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