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

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 m2 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%.

Incorporating time-dependent demand patterns in the optimal location of capacitated charging stations

“…Finally, in the residential zone, we assume the presence of a high density of private houses, with a comparatively low demand in the morning and a peak in the evening and early night hours. These assumptions are consistent with several studies regarding the spatial-temporal distribution of the charging demands observed in urban areas (see, e.g., Yi et al, 2020;Straub et al, 2021).…”

Car-Access Attractiveness of Urban Districts Regarding Shopping and Working Trips for Usage in E-Mobility Traffic Simulations

“…E-mobility traffic simulations, mostly in the form of activity-based models, are commonly used for this purpose [5][6][7][8]. In activity-based models, individual full-day travel schedules are generated for all persons or vehicles within the considered geographical area.…”

“…Based on these partial results, it is possible to estimate the spatial distribution of the charging energy and power demand that arises when the individuals solely charge their BEVs at home. This approach has been demonstrated in [5] for the urban area of Berlin, Germany, and its 448 sub-districts. However, these results need further refinement as they neglect the fact that vehicles do not always charge at home but can also charge at, e.g., work and shopping locations.…”

“…Since the car-access attractiveness of the districts is determined solely for shopping and work trips, vehicle routing can only determine the locations for shopping and work activities. However, in addition to the places of residence, these are the locations with the highest charging potential, as they have the highest average car idle times in Berlin (places of residence: 20.9 h per day, workplaces: 1.7 h and shopping locations: 0.1 h) [5].…”

Analysis of Scenarios for the Insertion of Electric Vehicles in Conjunction with a Solar Carport in the City of Curitiba, Paraná—Brazil

“…Knowing the need to decrease pollution (mainly air) around the globe, we must reduce the emissions caused by the transport sector by shifting away from the traditional fossil fuel-based concept to an alternative system [8][9][10]. With numerous objectives to be achieved (and quickly), electric vehicles (EVs) are set to be the key to shift into electric mobility, considering that they have already been playing a significant role in recent years [6,8].…”