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• W4307103674 abstract "To support power grid operators to detect and evaluate potential power grid congestions due to the electrification of urban private cars, accurate models are needed to determine the charging energy and power demand of battery electric vehicles (BEVs) with high spatial and temporal resolution. Typically, e-mobility traffic simulations are used for this purpose. In particular, activity-based mobility models are used because they individually model the activity and travel patterns of each person in the considered geographical area. In addition to inaccuracies in determining the spatial distribution of BEV charging demand, one main limitation of the activity-based models proposed in the literature is that they rely on data describing traffic flow in the considered area. However, these data are not available for most places in the world. Therefore, this paper proposes a novel approach to develop an activity-based model that overcomes the spatial limitations and does not require traffic flow data as an input parameter. Instead, a route assignment procedure assigns a destination to each BEV trip based on the evaluation of all possible destinations. The basis of this evaluation is the travel distance and speed between the origin of the trip and the destination, as well as the car-access attractiveness and the availability of parking spots at the destinations. The applicability of this model is demonstrated for the urban area of Berlin, Germany, and its 448 sub-districts. For each district in Berlin, both the required daily BEV charging energy demand and the power demand are determined. In addition, the load shifting potential is investigated for an exemplary district. The results show that peak power demand can be reduced by up to 31.7% in comparison to uncontrolled charging." @default.
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• W4307103674 date "2023-02-01" @default.
• W4307103674 modified "2023-10-17" @default.
• W4307103674 title "Forecasting the spatial and temporal charging demand of fully electrified urban private car transportation based on large-scale traffic simulation" @default.
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• W4307103674 doi "https://doi.org/10.1016/j.geits.2022.100039" @default.
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