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A Spatio-Temporal Microsimulation Framework for Charging Impact Analysis of Electric Vehicles in Residential Areas: Sensitivity Analysis and Benefits of Model Complexity

Author

Listed:
  • Stefan Schmalzl

    (Institute of Vehicle System Technology, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany)

  • Michael Frey

    (Institute of Vehicle System Technology, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany)

  • Frank Gauterin

    (Institute of Vehicle System Technology, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany)

Abstract

The increasing share of electric vehicles (EVs) offers many advantages, including a reduced CO 2 footprint over the vehicles’ lifetime and improved resource efficiency through the recycling of high-voltage batteries. At the same time, the growing EV share presents challenges, such as ensuring sufficient power supply for the simultaneous charging of EVs within existing distribution grids. The scientific community has conducted numerous studies on the interaction between EVs and distribution grids, employing increasingly complex modeling techniques. However, the benefits of more complex modeling are rarely quantified. This study aims to address this gap by evaluating the impact of modeling complexity on transformer peak loads and busbar voltage for three communities with real-world distribution grid data. Since numerous stochastic factors influence EV charging patterns, this paper introduces a modular framework that accounts for the interconnection of these factors through microsimulation. The framework models charging events of battery electric vehicles (BEVs) and comprises modules for synthetic population generation, weekly mobility pattern assignment, and energy demand modeling based on vehicle class and ambient conditions. The findings reveal that cost-optimized charging strategies and seasonal factors, such as cold weather, have a significantly greater impact on the distribution grid than the detailed modeling of sociodemographic mobility patterns or detailed modeling of a diversified vehicle fleet.

Suggested Citation

  • Stefan Schmalzl & Michael Frey & Frank Gauterin, 2025. "A Spatio-Temporal Microsimulation Framework for Charging Impact Analysis of Electric Vehicles in Residential Areas: Sensitivity Analysis and Benefits of Model Complexity," Energies, MDPI, vol. 18(13), pages 1-30, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3530-:d:1694522
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    References listed on IDEAS

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    1. Fischer, David & Harbrecht, Alexander & Surmann, Arne & McKenna, Russell, 2019. "Electric vehicles’ impacts on residential electric local profiles – A stochastic modelling approach considering socio-economic, behavioural and spatial factors," Applied Energy, Elsevier, vol. 233, pages 644-658.
    2. Michael von Bonin & Elias Dörre & Hadi Al-Khzouz & Martin Braun & Xian Zhou, 2022. "Impact of Dynamic Electricity Tariff and Home PV System Incentives on Electric Vehicle Charging Behavior: Study on Potential Grid Implications and Economic Effects for Households," Energies, MDPI, vol. 15(3), pages 1-28, February.
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