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An open tool for creating battery-electric vehicle time series from empirical data, emobpy

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  • Gaete-Morales, Carlos
  • Kramer, Hendrik
  • Schill, Wolf-Peter

Abstract

There is substantial research interest in how future fleets of battery-electric vehicles will interact with the power sector. Various types of energy models are used for respective analyses. They depend on meaningful input parameters, in particular time series of vehicle mobility, driving electricity consumption, grid availability, or grid electricity demand. As the availability of such data is highly limited, we introduce the open-source tool emobpy. Based on mobility statistics, physical properties of battery-electric vehicles, and other customizable assumptions, it derives time series data that can readily be used in a wide range of model applications. For an illustration, we create and characterize 200 vehicle profiles for Germany. Depending on the hour of the day, a fleet of one million vehicles has a median grid availability between 5 and 7 gigawatts, as vehicles are parking most of the time. Four exemplary grid electricity demand time series illustrate the smoothing effect of balanced charging strategies.

Suggested Citation

  • Gaete-Morales, Carlos & Kramer, Hendrik & Schill, Wolf-Peter, 2021. "An open tool for creating battery-electric vehicle time series from empirical data, emobpy," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 8.
    • Handle: RePEc:zbw:espost:235851
    DOI: 10.1038/s41597-021-00932-9
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    References listed on IDEAS

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    1. Mwasilu, Francis & Justo, Jackson John & Kim, Eun-Kyung & Do, Ton Duc & Jung, Jin-Woo, 2014. "Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 501-516.
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    Cited by:

    1. Wirtz, Marco, 2023. "nPro: A web-based planning tool for designing district energy systems and thermal networks," Energy, Elsevier, vol. 268(C).
    2. Mangipinto, Andrea & Lombardi, Francesco & Sanvito, Francesco Davide & Pavičević, Matija & Quoilin, Sylvain & Colombo, Emanuela, 2022. "Impact of mass-scale deployment of electric vehicles and benefits of smart charging across all European countries," Applied Energy, Elsevier, vol. 312(C).
    3. Hamza Mediouni & Amal Ezzouhri & Zakaria Charouh & Khadija El Harouri & Soumia El Hani & Mounir Ghogho, 2022. "Energy Consumption Prediction and Analysis for Electric Vehicles: A Hybrid Approach," Energies, MDPI, vol. 15(17), pages 1-17, September.
    4. Adeline Gu'eret & Wolf-Peter Schill & Carlos Gaete-Morales, 2024. "Impacts of electric carsharing on a power sector with variable renewables," Papers 2402.19380, arXiv.org, revised Oct 2024.
    5. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    6. 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.
    7. Alexander Roth & Carlos Gaete-Morales & Dana Kirchem & Wolf-Peter Schill, 2023. "Power sector benefits of flexible heat pumps," Papers 2307.12918, arXiv.org, revised Oct 2024.
    8. Viana-Fons, Joan Dídac & Payá, Jorge, 2024. "HVAC system operation, consumption and compressor size optimization in urban buses of Mediterranean cities," Energy, Elsevier, vol. 296(C).

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    Energy modelling; Energy supply and demand;

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