Name DIstribution Network GeneratOr (DINGO)
Acronym DINGO
Methodical Focus None
Institution(s) Reiner Lemoine Institut
Author(s) (institution, working field, active time period) Jonathan Amme; Transformation of Energy Systems; 2016-2017, Guido Pleßmann; Transformation of Energy Systems; 2016-2017
Current contact person Jonathan Amme
Contact (e-mail) jonathan.amme@rl-institut.de
Website https://github.com/openego/ding0/
Logo /media/logos/DING0_Logo_300px.png
Primary Purpose Generate distribution grid data
Primary Outputs Distribution grid data
Support / Community / Forum
Framework
Link to User Documentation https://dingo.readthedocs.io/en/dev/
Link to Developer/Code Documentation https://dingo.readthedocs.io/en/dev/
Documentation quality expandable
Source of funding Funded by the German Federal Ministry for Economic Affairs and Energy in context of the funding initiative „Optimization of the Energy Supply System“ (Funding ID: 0325881B)
Number of developers less than 10
Number of users less than 10
Open Source
License GNU Affero General Public License v3.0
Source code available
GitHub
Access to source code https://github.com/openego/dingo
Data provided some
Collaborative programming
GitHub Organisation
GitHub Contributions Graph
Modelling software Python3
Internal data processing software Python3 (PyPSA; Pandas; and more); PostgreSQL
External optimizer
Additional software
GUI
Modeled energy sectors (final energy) electricity
Modeled demand sectors Households, Industry, Commercial sector
Modeled technologies: components for power generation or conversion
Renewables PV, Wind, Hydro
Conventional -
Modeled technologies: components for transfer, infrastructure or grid
Electricity distribution
Gas -
Heat -
Properties electrical grid AC load flow
Modeled technologies: components for storage -
User behaviour and demand side management
Changes in efficiency
Market models -
Geographical coverage
Geographic (spatial) resolution districts
Time resolution -
Comment on geographic (spatial) resolution Coverage of the resulting dataset is Germany, whereas underlying data (input and output) has a resolution of up to single generation unit level.
Observation period -
Additional dimensions (sector) -
Model class (optimisation) -
Model class (simulation) -
Other
Short description of mathematical model class The model uses Capacitated Vehicle Routing Problems to describe the route of a MV grid ring topology that is solved by meta-heuristics.
Mathematical objective -
Approach to uncertainty -
Suited for many scenarios / monte-carlo
typical computation time more than a day
Typical computation hardware Single core
Technical data anchored in the model -
Interfaces
Model file format .py
Input data file format OpenEnergy Database
Output data file format OpenEnergy Database; Python pickle
Integration with other models eGo
Integration of other models
Citation reference J. Amme, G. Pleßmann, J. Bühler, L. Hülk, E. Kötter, P. Schwaegerl: The eGo grid model: An open-source and open-data based synthetic medium-voltage grid model for distribution power supply systems, IOP Conf. Series: Journal of Physics: Conf. Series 977, 2018
Citation DOI 10.1088/1742-6596/977/1/012007
Reference Studies/Models -
Example research questions -
Model usage -
Model validation -
Example research questions -
further properties
Model specific properties -

If you find bugs or if you have ideas to improve the Open Energy Platform, you are welcome to add your comments to the existing issues on GitHub.
You can also fork the project and get involved.

Please note that the platform is still under construction and therefore the design of this page is still highly volatile!


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grid open_eGo Germany