| Name | DataProcessing – Data Model of the German Electricity System (part of the open_eGo project) |
| Acronym | eGoDP |
| Methodical Focus | None |
| Institution(s) | ZNES Flensburg (ZNES), Reiner Lemoine Institut (RLI), DLR Institute of Networked Energy Systems (DLR), Otto-von-Guericke-Universität Magdeburg (OvGU) |
| Author(s) (institution, working field, active time period) | Ludwig Hülk (RLI), Guido Pleßmann (RLI), Ulf Müller (ZNES), Ilka Cußmann (ZNES), Lukas Wienholt (DLR), Martin Glauer (OvGU), GitHub community |
| Current contact person | Ludwig Hülk |
| Contact (e-mail) | ludwig.huelk@rl-institut.de |
| Website | https://openegoproject.wordpress.com/ |
| Logo |
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| Primary Purpose | The DataProcessing is a collection of scripts to process input data to be used for the other eGo-Tools. The resulting input data is published and available. |
| Primary Outputs | Main outputs of eGoDP are Substations (EHV/HV, HV/MV, MV/LV), Grid districts (EHV, MV, LV) and Loadareas. |
| Support / Community / Forum | |
| Framework | |
| Link to User Documentation | - |
| Link to Developer/Code Documentation | https://github.com/openego/data_processing |
| Documentation quality | not available |
| Source of funding | BMWi project open_eGo (6. Forschungsprogramm) |
| Number of developers | less than 10 |
| Number of users | less than 10 |
| Open Source | |
| License | Affero General Public License Version 3 |
| Source code available | |
| GitHub | |
| Access to source code | https://github.com/openego/data_processing |
| Data provided | none |
| Collaborative programming |
| GitHub Organisation |
| Modelling software | Python, PostgreSQL, pandas, workalender, oemof.db, demandlib, ego.io, geoalchemy2 |
| Internal data processing software | Python, 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 | Germany and neighboring countries | ||||||
| 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 | Data scripts |
| Short description of mathematical model class | |
| Mathematical objective | Data scripts |
| Approach to uncertainty | Deterministic |
| Suited for many scenarios / monte-carlo | |
| typical computation time | more than a day |
| Typical computation hardware | PostgreSQL Database Server (OEP) |
| Technical data anchored in the model | - |
| Interfaces | |
| Model file format | .sql, .py |
| Input data file format | oedb |
| Output data file format | oedb |
| Integration with other models | |
| Integration of other models |
| Citation reference | https://openegoproject.wordpress.com/publications/ |
| Citation DOI | See above |
| Reference Studies/Models | See above |
| Example research questions | How can open data be accessed to generate a data set to compare various models? How can data be extracted with ensured quality? |
| Model usage | - |
| Model validation |
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| Example research questions | How can open data be accessed to generate a data set to compare various models? How can data be extracted with ensured quality? |
| further properties | Data schemes available: http://oep.iks.cs.ovgu.de/dataedit/schemas |
| Model specific properties | - |