Model Factsheet

Overview / Analysis of the Grid Capacity for Electric Vehicles (GridEVA)

Model Analysis of the Grid Capacity for Electric Vehicles (GridEVA)

General Information
Name Analysis of the Grid Capacity for Electric Vehicles
Acronym GridEVA
Methodical Focus Grid Capacity Analysis , Electric Vehicle Integration , Smart Charging
Institution(s) elenia Institute for High Voltage Technology and Power Systems, OFFIS e.V., Leibniz University Hannover, University of Applied Science Emden/Leer
Author(s) (institution, working field, active time period) Henrik Wagner, Fernando Peñaherrera V., Sarah Fayed, Sarah Eckhoff, Oliver Werth
Current contact person Henrik Wagner
Contact (e-mail) henrik.wagner@tu-braunschweig.de
Website https://gitlab.com/zdin-zle/scenarios/grid-capacity-for-electric-mobility
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Primary Purpose The developed tool GridEVA (Grid Electric Vehicle Analysis) aims to analyze the effects and impacts of an increasing EV penetration rate on the low-voltage grid in districts and identify the maximum possible grid capacity for EV charging using the co-simulation framework mosaik 3.x. A concrete use case is the existing residential district "Am Ölper Berge" in Brunswick, Lower Saxony, Germany.
Primary Outputs Grid-stable penetration rates for electric vehicles
Support / Community / Forum
Framework mosaik
Link to User Documentation -
Link to Developer/Code Documentation https://zdin-zle.gitlab.io/scenarios/grid-capacity-for-electric-mobility/
Documentation quality expandable
Source of funding Lower Saxony Ministry of Science and Culture under grant number 11-76251-13-3/19 – ZN3488 (ZLE)
Number of developers less than 10
Number of users less than 10
Openness
Open Source
License GNU Lesser General Public License v3.0
Source code available
GitHub
Access to source code https://gitlab.com/zdin-zle/scenarios/grid-capacity-for-electric-mobility
Data provided example data
Collaborative programming
Software
Modelling software Python
Internal data processing software
External optimizer
Additional software
GUI
Coverage
Modeled energy sectors (final energy) electricity
Modeled demand sectors Households
Modeled technologies: components for power generation or conversion
Renewables PV
Conventional -
Modeled technologies: components for transfer, infrastructure or grid
Electricity distribution
Gas -
Heat -
Properties electrical grid AC load flow
Modeled technologies: components for storage battery
User behaviour and demand side management
Changes in efficiency
Market models -
Geographical coverage
Geographic (spatial) resolution districts, households
Time resolution 15 min
Comment on geographic (spatial) resolution
Observation period <1 year, 1 year
Additional dimensions (sector) -
Mathematical Properties
Model class (optimisation) -
Model class (simulation) -
Other
Short description of mathematical model class
Mathematical objective -
Approach to uncertainty -
Suited for many scenarios / monte-carlo
typical computation time -
Typical computation hardware -
Technical data anchored in the model -
Model Integration
Interfaces
Model file format Other
Input data file format .csv
Output data file format Other
Integration with other models
Integration of other models
References
Citation reference https://ieeexplore.ieee.org/document/10049829
Citation DOI 10.1049/icp.2022.2713
Reference Studies/Models -
Example research questions -
Model usage -
Model validation -
Example research questions -
further properties
Model specific properties -

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grid capacity