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  4. Modeling Spatial Charging Demands Related to Electric Vehicles for Power Grid Planning Applications
 
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2020
  • Zeitschriftenaufsatz

Titel

Modeling Spatial Charging Demands Related to Electric Vehicles for Power Grid Planning Applications

Abstract
The electrification of the transport sector together with an increasing share of renewable energies has the potential to reduce CO2 emissions significantly. This transformation requires the rollout of charging infrastructure, which has an impact on power grids. For grid planning and dimensioning purposes, it is crucial to assess this rapidly growing impact. We present an approach using socio-economic data such as income levels together with a model for demographic changes to estimate where electric mobility is likely to be concentrated, especially during the transformation phase. We present a total-cost-of-ownership approach for the ramp-up of electric mobility, considering an increased penetration of renewable energies. With the city of Wiesbaden in Germany as an example for an application area, the possible expansion of vehicle ownership and charging points is modeled on the level of individual buildings. Compared to a simpler approach, the detailed model results in more consistent charging point allocations, higher line/transformer loadings and lower bus voltages for the investigated grids. Predicting future distributions of charging points with such a level of detail in terms of ramp-up and spatial resolution proves potentially beneficial for grid analysis and planning purposes, especially in urban areas, where infrastructure changes are expensive and time-consuming.
Author(s)
Gauglitz, Philip
Ulffers, Jan
Thomsen, Gyde
Frischmuth, Felix
Geiger, David
Scheidler, Alexander
Zeitschrift
ISPRS International Journal of Geo-Information
Project(s)
LI2.0
Funder
Bundesministerium für Wirtschaft und Energie BMWi (Deutschland)
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DOI
10.3390/ijgi9120699
Externer Link
Externer Link
Language
Englisch
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IEE
Tags
  • charging demands

  • electric vehicles

  • spatial allocation

  • scenario

  • grid planning

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