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Vehicle Energy Consumption in Python (VencoPy): Presenting and Demonstrating an Open-Source Tool to Calculate Electric Vehicle Charging Flexibility

Author

Listed:
  • Niklas Wulff

    (Department of Energy Systems Analysis, Institute of Networked Energy Systems, German Aerospace Center (DLR), Curiestr. 4, 70563 Stuttgart, Germany)

  • Fabia Miorelli

    (Department of Energy Systems Analysis, Institute of Networked Energy Systems, German Aerospace Center (DLR), Curiestr. 4, 70563 Stuttgart, Germany)

  • Hans Christian Gils

    (Department of Energy Systems Analysis, Institute of Networked Energy Systems, German Aerospace Center (DLR), Curiestr. 4, 70563 Stuttgart, Germany)

  • Patrick Jochem

    (Department of Energy Systems Analysis, Institute of Networked Energy Systems, German Aerospace Center (DLR), Curiestr. 4, 70563 Stuttgart, Germany
    Institute for Industrial Production, Karlsruhe Institute of Technology, Hertzstr. 16, 76187 Karlsruhe, Germany)

Abstract

As electric vehicle fleets grow, rising electric loads necessitate energy systems models to incorporate their respective demand and potential flexibility. Recently, a small number of tools for electric vehicle demand and flexibility modeling have been released under open source licenses. These usually sample discrete trips based on aggregate mobility statistics. However, the full range of variables of travel surveys cannot be accessed in this way and sub-national mobility patterns cannot be modeled. Therefore, a tool is proposed to estimate future electric vehicle fleet charging flexibility while being able to directly access detailed survey results. The framework is applied in a case study involving two recent German national travel surveys (from the years 2008 and 2017) to exemplify the implications of different mobility patterns of motorized individual vehicles on load shifting potential of electric vehicle fleets. The results show that different mobility patterns, have a significant impact on the resulting load flexibilites. Most obviously, an increased daily mileage results in higher electricty demand. A reduced number of trips per day, on the other hand, leads to correspondingly higher grid connectivity of the vehicle fleet. VencoPy is an open source, well-documented and maintained tool, capable of assessing electric vehicle fleet scenarios based on national travel surveys. To scrutinize the tool, a validation of the simulated charging by empirically observed electric vehicle fleet charging is advised.

Suggested Citation

  • Niklas Wulff & Fabia Miorelli & Hans Christian Gils & Patrick Jochem, 2021. "Vehicle Energy Consumption in Python (VencoPy): Presenting and Demonstrating an Open-Source Tool to Calculate Electric Vehicle Charging Flexibility," Energies, MDPI, vol. 14(14), pages 1-23, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4349-:d:597053
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    References listed on IDEAS

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    4. Niklas Wulff & Felix Steck & Hans Christian Gils & Carsten Hoyer-Klick & Bent van den Adel & John E. Anderson, 2020. "Comparing Power-System and User-Oriented Battery Electric Vehicle Charging Representation and Its Implications on Energy System Modeling," Energies, MDPI, vol. 13(5), pages 1-41, March.
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