Abstract
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.
Highlights
An emobpy profile consists of four time series: (i) vehicle mobility containing the vehicle’s location and distance travelled, (ii) driving electricity consumption, specifying how much electricity is taken from the battery for driving; (iii) battery-electric vehicles (BEV) grid availability, providing information whether and with which power rating a BEV is connected to the electricity grid at a certain point in time; and (iv) BEV grid electricity demand, specifying the actual charging electricity drawn from the grid, based on different charging strategies
We make three general assumptions: first, we assume that individuals with access to a vehicle carry out all their trips with the same vehicle; second, we assume that future BEV drivers have similar mobility patterns as current drivers of conventional vehicles covered by the underlying mobility statistics; and third, for simplicity and tractability, we assume that there are only four BEV models: Hyundai Kona, Renault Zoe, Tesla Model 3 and Volkswagen ID.[3]
The generated vehicle profiles can be used as inputs for a wide range of model analyses of electrified and decarbonized mobility futures
Summary
An emobpy profile consists of four time series: (i) vehicle mobility containing the vehicle’s location and distance travelled, (ii) driving electricity consumption, specifying how much electricity is taken from the battery for driving; (iii) BEV grid availability, providing information whether and with which power rating a BEV is connected to the electricity grid at a certain point in time; and (iv) BEV grid electricity demand, specifying the actual charging electricity drawn from the grid, based on different charging strategies Such profiles are core input data for a wide range of model applications in energy, environmental, and economic studies on BEV. Many model-based analyses investigate potential power sector interactions of future BEV fleets[3,4,5,6] and depend on a meaningful representation of electric vehicles’ mobility patterns
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