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

Florian Straub, Simon Streppel and Dietmar Göhlich
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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

Energies, 2021, vol. 14, issue 8, 1-21

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%.

Keywords: electric vehicle; activity-based simulation; transportation electrification; charging power demand; spatial temporal distribution; open data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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