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Methodology for Estimating the Spatial and Temporal Power Demand of Private Electric Vehicles for an Entire Urban Region Using Open Data

Author

Listed:
  • Florian Straub

    (Chair for Methods of Product Development and Mechatronics, Technical University of Berlin, Strasse des 17. Juni 135, 10623 Berlin, Germany)

  • Simon Streppel

    (Chair for Methods of Product Development and Mechatronics, Technical University of Berlin, Strasse des 17. Juni 135, 10623 Berlin, Germany)

  • Dietmar Göhlich

    (Chair for Methods of Product Development and Mechatronics, Technical University of Berlin, Strasse des 17. Juni 135, 10623 Berlin, Germany)

Abstract

With continuous proliferation of private battery electric vehicles (BEVs) in urban regions, the demand for electrical energy and power is constantly increasing. Electrical grid infrastructure operators are facing the question of where and to what extent they need to expand their infrastructure in order to meet the additional demand. Therefore, the aim of this paper is to develop an activity-based mobility model that supports electrical grid operators in detecting and evaluating possible overloads within the electrical grid, deriving from the aforementioned electrification. We apply our model, which fully relies on open data, to the urban area of Berlin. In addition to a household travel survey, statistics on the population density, the degree of motorisation, and the household income in fine spatial resolution are key data sources for generation of the model. The results show that the spatial distribution of the BEV charging energy demand is highly heterogeneous. The demand per capita is higher in peripheral areas of the city, while the demand per m 2 area is higher in the inner city. For reference areas, we analysed the temporal distribution of the BEV charging power demand, by assuming that the vehicles are solely charged at their residential district. We show that the households’ power demand peak in the evening coincide with the BEV power demand peak while the total power demand can increase up to 77.9%.

Suggested Citation

  • Florian Straub & Simon Streppel & Dietmar Göhlich, 2021. "Methodology for Estimating the Spatial and Temporal Power Demand of Private Electric Vehicles for an Entire Urban Region Using Open Data," Energies, MDPI, vol. 14(8), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2081-:d:532678
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    References listed on IDEAS

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    Cited by:

    1. Antonello Cammarano & Vincenzo Varriale & Francesca Michelino & Mauro Caputo, 2022. "Open and Crowd-Based Platforms: Impact on Organizational and Market Performance," Sustainability, MDPI, vol. 14(4), pages 1-26, February.
    2. Ana Carolina Kulik & Édwin Augusto Tonolo & Alberto Kisner Scortegagna & Jardel Eugênio da Silva & Jair Urbanetz Junior, 2021. "Analysis of Scenarios for the Insertion of Electric Vehicles in Conjunction with a Solar Carport in the City of Curitiba, Paraná—Brazil," Energies, MDPI, vol. 14(16), pages 1-15, August.
    3. Florian Straub & Otto Maier & Dietmar Göhlich, 2021. "Car-Access Attractiveness of Urban Districts Regarding Shopping and Working Trips for Usage in E-Mobility Traffic Simulations," Sustainability, MDPI, vol. 13(20), pages 1-29, October.
    4. Jiang, Qinhua & Zhang, Ning & Yueshuai He, Brian & Lee, Changju & Ma, Jiaqi, 2024. "Large-scale public charging demand prediction with a scenario- and activity-based approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).

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