| Name | DIstribution Network GeneratOr |
| 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 |
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| 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 | none |
| Collaborative programming |
| GitHub Organisation |
| Modelling software | Python3 |
| Internal data processing software | Python3 (PyPSA; Pandas; and more); PostgreSQL |
| 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 | 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) |
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| 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 |
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| Example research questions | - |
| further properties | |
| Model specific properties | - |