A Spatio-Temporal Microsimulation Framework for Charging Impact Analysis of Electric Vehicles in Residential Areas: Sensitivity Analysis and Benefits of Model Complexity
The increasing share of electric vehicles (EVs) offers many advantages, including a reduced CO<sub>2</sub> 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, s...
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2025-07-01
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| author | Stefan Schmalzl Michael Frey Frank Gauterin |
| author_facet | Stefan Schmalzl Michael Frey Frank Gauterin |
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| description | The increasing share of electric vehicles (EVs) offers many advantages, including a reduced CO<sub>2</sub> 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. |
| format | Article |
| id | doaj-art-f1c42279dfd94ebfa61e2beb1e4ee903 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Energies |
| spelling | doaj-art-f1c42279dfd94ebfa61e2beb1e4ee9032025-08-20T02:35:47ZengMDPI AGEnergies1996-10732025-07-011813353010.3390/en18133530A Spatio-Temporal Microsimulation Framework for Charging Impact Analysis of Electric Vehicles in Residential Areas: Sensitivity Analysis and Benefits of Model ComplexityStefan Schmalzl0Michael Frey1Frank Gauterin2Institute of Vehicle System Technology, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, GermanyInstitute of Vehicle System Technology, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, GermanyInstitute of Vehicle System Technology, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, GermanyThe increasing share of electric vehicles (EVs) offers many advantages, including a reduced CO<sub>2</sub> 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.https://www.mdpi.com/1996-1073/18/13/3530microsimulationelectric vehicle chargingBattery Electric Vehiclesensitivity analysischarging behaviorcharging strategy |
| spellingShingle | Stefan Schmalzl Michael Frey Frank Gauterin A Spatio-Temporal Microsimulation Framework for Charging Impact Analysis of Electric Vehicles in Residential Areas: Sensitivity Analysis and Benefits of Model Complexity Energies microsimulation electric vehicle charging Battery Electric Vehicle sensitivity analysis charging behavior charging strategy |
| title | A Spatio-Temporal Microsimulation Framework for Charging Impact Analysis of Electric Vehicles in Residential Areas: Sensitivity Analysis and Benefits of Model Complexity |
| title_full | A Spatio-Temporal Microsimulation Framework for Charging Impact Analysis of Electric Vehicles in Residential Areas: Sensitivity Analysis and Benefits of Model Complexity |
| title_fullStr | A Spatio-Temporal Microsimulation Framework for Charging Impact Analysis of Electric Vehicles in Residential Areas: Sensitivity Analysis and Benefits of Model Complexity |
| title_full_unstemmed | A Spatio-Temporal Microsimulation Framework for Charging Impact Analysis of Electric Vehicles in Residential Areas: Sensitivity Analysis and Benefits of Model Complexity |
| title_short | A Spatio-Temporal Microsimulation Framework for Charging Impact Analysis of Electric Vehicles in Residential Areas: Sensitivity Analysis and Benefits of Model Complexity |
| title_sort | spatio temporal microsimulation framework for charging impact analysis of electric vehicles in residential areas sensitivity analysis and benefits of model complexity |
| topic | microsimulation electric vehicle charging Battery Electric Vehicle sensitivity analysis charging behavior charging strategy |
| url | https://www.mdpi.com/1996-1073/18/13/3530 |
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