Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/235851 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Scientific Data [ISSN:] 2052-4463 [Volume:] 8 [Article No.:] 152 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2021
Verlag: 
Springer, Berlin, Heidelberg
Zusammenfassung: 
There is substantial research interest in how future fleets of battery-electric vehicles will interact with the power sector. Various types of energy models are used for respective analyses. They depend on meaningful input parameters, in particular time series of vehicle mobility, driving electricity consumption, grid availability, or grid electricity demand. As the availability of such data is highly limited, we introduce the open-source tool emobpy. Based on mobility statistics, physical properties of battery-electric vehicles, and other customizable assumptions, it derives time series data that can readily be used in a wide range of model applications. For an illustration, we create and characterize 200 vehicle profiles for Germany. Depending on the hour of the day, a fleet of one million vehicles has a median grid availability between 5 and 7 gigawatts, as vehicles are parking most of the time. Four exemplary grid electricity demand time series illustrate the smoothing effect of balanced charging strategies.
Schlagwörter: 
Energy modelling
Energy supply and demand
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Open Access funding enabled and organized by Projekt DEAL
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