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Comparing Power-System and User-Oriented Battery Electric Vehicle Charging Representation and Its Implications on Energy System Modeling

Author

Listed:
  • Niklas Wulff

    (Department of Energy Systems Analysis, Institute of Engineering Thermodynamics, German Aerospace Center, Pfaffenwaldring 38-40, 70569 Stuttgart, Germany)

  • Felix Steck

    (Department of Passenger Transport, Institute of Transport Research, German Aerospace Center, Rudower Chaussee 7, 12489 Berlin, Germany)

  • Hans Christian Gils

    (Department of Energy Systems Analysis, Institute of Engineering Thermodynamics, German Aerospace Center, Pfaffenwaldring 38-40, 70569 Stuttgart, Germany)

  • Carsten Hoyer-Klick

    (Department of Energy Systems Analysis, Institute of Engineering Thermodynamics, German Aerospace Center, Pfaffenwaldring 38-40, 70569 Stuttgart, Germany)

  • Bent van den Adel

    (Department of Vehicle Systems and Technology Assessment, Institute of Vehicle Concepts, German Aerospace Center, Pfaffenwaldring 38-40, 70569 Stuttgart, Germany)

  • John E. Anderson

    (Department of Passenger Transport, Institute of Transport Research, German Aerospace Center, Rudower Chaussee 7, 12489 Berlin, Germany)

Abstract

Battery electric vehicles (BEV) provide an opportunity to balance supply and demand in future power systems with high shares of fluctuating renewable energy. Compared to other storage systems such as pumped-storage hydroelectricity, electric vehicle energy demand is highly dependent on charging and connection choices of vehicle users. We present a model framework of a utility-based stock and flow model, a utility-based microsimulation of charging decisions, and an energy system model including respective interfaces to assess how the representation of battery electric vehicle charging affects energy system optimization results. We then apply the framework to a scenario study for controlled charging of nine million electric vehicles in Germany in 2030. Assuming a respective fleet power demand of 27 TWh, we analyze the difference between power-system-based and vehicle user-based charging decisions in two respective scenarios. Our results show that taking into account vehicle users’ charging and connection decisions significantly decreases the load shifting potential of controlled charging. The analysis of marginal values of equations and variables of the optimization problem yields valuable insights on the importance of specific constraints and optimization variables. Assumptions on fleet battery availability and a detailed representation of fast charging are found to have a strong impact on wind curtailment, renewable energy feed-in, and required gas power plant flexibility. A representation of fleet connection to the grid in high temporal detail is less important. Peak load can be reduced by 5% and 3% in both scenarios, respectively. Shifted load is robust across sensitivity analyses while other model results such as curtailment are more sensitive to factors such as underlying data years. Analyzing the importance of increased BEV fleet battery availability for power systems with different weather and electricity demand characteristics should be further scrutinized.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1093-:d:327074
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    References listed on IDEAS

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    2. Lauvergne, Rémi & Perez, Yannick & Françon, Mathilde & Tejeda De La Cruz, Alberto, 2022. "Integration of electric vehicles into transmission grids: A case study on generation adequacy in Europe in 2040," Applied Energy, Elsevier, vol. 326(C).
    3. Wohlschlager, Daniela & Kigle, Stephan & Schindler, Vanessa & Neitz-Regett, Anika & Fröhling, Magnus, 2024. "Environmental effects of vehicle-to-grid charging in future energy systems – A prospective life cycle assessment," Applied Energy, Elsevier, vol. 370(C).
    4. Zongfei Wang & Patrick Jochem & Hasan Ümitcan Yilmaz & Lei Xu, 2022. "Integrating vehicle‐to‐grid technology into energy system models: Novel methods and their impact on greenhouse gas emissions," Journal of Industrial Ecology, Yale University, vol. 26(2), pages 392-405, April.
    5. 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).
    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. Strobel, Leo & Schlund, Jonas & Pruckner, Marco, 2022. "Joint analysis of regional and national power system impacts of electric vehicles—A case study for Germany on the county level in 2030," Applied Energy, Elsevier, vol. 315(C).
    8. Tobias Junne & Karl-Kiên Cao & Kim Kira Miskiw & Heidi Hottenroth & Tobias Naegler, 2021. "Considering Life Cycle Greenhouse Gas Emissions in Power System Expansion Planning for Europe and North Africa Using Multi-Objective Optimization," Energies, MDPI, vol. 14(5), pages 1-26, February.

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