Name GridCal
Acronym -
Methodical Focus power systems software
Institution(s)
Author(s) (institution, working field, active time period)
Current contact person Santiago PeƱate-Vera
Contact (e-mail) santiago.penate.vera@gmail.com
Website https://github.com/SanPen/GridCal
Logo /media/logos/GridCal_round_icon.png
Primary Purpose Power System planning, as much of it as possible
Primary Outputs
Support / Community / Forum
Framework
Link to User Documentation {https://gridcal.readthedocs.io/en/latest/}
Link to Developer/Code Documentation https://gridcal.readthedocs.io/en/latest/
Documentation quality expandable
Source of funding -
Number of developers less than 10
Number of users less than 1000
Open Source
License GNU General Public License v3.0
Source code available
GitHub
Access to source code https://github.com/SanPen/GridCal
Data provided example data
Collaborative programming
GitHub Organisation
GitHub Contributions Graph
Modelling software Python
Internal data processing software
External optimizer
Additional software
GUI
Citation reference -
Citation DOI 10.5281/zenodo.3701717
Reference Studies/Models -
Example research questions All related to power system planning.
Model usage -
Model validation cross-checked with other models
Example research questions All related to power system planning.
further properties
Model specific properties Excellent power flow solvers (Multiterminal AC/DC) Stochastic power Flow, Nice GUI, etc...
Modeled energy sectors (final energy) -
Modeled demand sectors -
Modeled technologies: components for power generation or conversion
Renewables -
Conventional -
Modeled technologies: components for transfer, infrastructure or grid
Electricity -
Gas -
Heat -
Properties electrical grid -
Modeled technologies: components for storage -
User behaviour and demand side management
Changes in efficiency
Market models -
Geographical coverage
Geographic (spatial) resolution -
Time resolution -
Comment on geographic (spatial) resolution
Observation period -
Additional dimensions (sector) -
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 -
Interfaces API and Graphical user interface
Model file format .py
Input data file format .csv
Output data file format .csv
Integration with other models Reads Plexos CSV files, Internally coupled with the different simulation modes
Integration of other models Export in a very simle JSON format that contains most of the relevant information

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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demand grid Renewable Europe Electricity Transport Solar Wind Storage GPL-3.0 time series