| Name | GridCal |
| Acronym | - |
| Methodical Focus | power systems software |
| Institution(s) | |
| Author(s) (institution, working field, active time period) | |
| Current contact person | Santiago Peñate-Vera |
| Contact (e-mail) | santiago.penate.vera@gmail.com |
| Website | https://github.com/SanPen/GridCal |
| Logo |
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| 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 Lesser 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 |
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| Modelling software | Python |
| Internal data processing software | |
| External optimizer | |
| Additional software | |
| GUI |
| Modeled energy sectors (final energy) |
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| Modeled demand sectors |
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| Modeled technologies: components for power generation or conversion |
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| Modeled technologies: components for transfer, infrastructure or grid |
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| Properties electrical grid |
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| Modeled technologies: components for storage |
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| User behaviour and demand side management | |||||||
| Changes in efficiency | |||||||
| Market models | - | ||||||
| Geographical coverage | |||||||
| Geographic (spatial) resolution |
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| Time resolution | - | ||||||
| Comment on geographic (spatial) resolution | |||||||
| Observation period | - | ||||||
| Additional dimensions (sector) |
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| 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 |
| Citation reference | - |
| Citation DOI | 10.5281/zenodo.3701717 |
| Reference Studies/Models | - |
| Example research questions | All related to power system planning. |
| Model usage | - |
| Model validation |
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| 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... |