Model Factsheets
| Name | Python for Power System Analysis (PyPSA) - European Sector-Coupled Energy System (PyPSA-Eur) |
| Acronym | PyPSA-Eur |
| Methodical Focus | energy system optimisation |
| Institution(s) | Technical University of Berlin, Karlsruhe Institute of Technology (KIT), Frankfurt Institute for Advanced Studies, Aarhus University |
| Author(s) (institution, working field, active time period) | Tom Brown; Marta Victoria; Fabian Neumann; Elisabeth Zeyen; Fabian Hofmann; Jonas Hörsch |
| Current contact person | Tom Brown |
| Contact (e-mail) | t.brown@tu-berlin.de |
| Website | https://www.pypsa.org/ |
| Logo |
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| Primary Purpose | PyPSA-Eur is an open model dataset of the European energy system at the transmission network level that covers the full ENTSO-E area. It covers demand and supply for all energy sectors. The electricity system representation contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, geographic potentials for the expansion of wind and solar power. The model is suitable both for operational studies and generation and transmission expansion planning studies. The continental scope and highly resolved spatial scale enables a proper description of the long-range smoothing effects for renewable power generation and their varying resource availability. A sector-coupled extension (previously known as PyPSA-Eur-Sec, which is now deprecated) adds demand and supply for the following sectors: transport, space and water heating, biomass, energy consumption in the agriculture, industry and industrial feedstocks, carbon management, carbon capture and usage/sequestration. This completes the energy system and includes all greenhouse gas emitters except waste management, agriculture, forestry and land use. |
| Primary Outputs | Flows in energy networks, energy system dispatch, energy system optimal investment (generation, transmission, storage, conversion), carbon cycles, shadow prices (e.g. CO2 price) |
| Support / Community / Forum | |
| Framework | Python for Power System Analysis (PyPSA) |
| Link to User Documentation | https://pypsa-eur.readthedocs.io |
| Link to Developer/Code Documentation | https://pypsa.readthedocs.io/en/latest/developers.html |
| Documentation quality | good |
| Source of funding | university basic funding, BMBF Project CoNDyNet |
| Number of developers | less than 20 |
| Number of users | less than 1000 |
| Open Source | |
| License | MIT |
| Source code available | |
| GitHub | |
| Access to source code | https://github.com/PyPSA/pypsa-eur |
| Data provided | none |
| Collaborative programming |
| GitHub Organisation |
| Modelling software | Python |
| Internal data processing software | Python |
| 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 | smart charging, V2G, P2X | ||||||
| Changes in efficiency | yes, can be time or weather-dependent | ||||||
| Market models | fundamental model | ||||||
| Geographical coverage | EU27+UK+NO+CH+Balkan-CY-MT | ||||||
| Geographic (spatial) resolution |
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| Time resolution | annual, hour, 15 min | ||||||
| Comment on geographic (spatial) resolution | User decides the resolution between single-node per country to transmission node level (thousands of nodes) | ||||||
| Observation period | <1 year, 1 year, >1 year | ||||||
| Additional dimensions (sector) |
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| Model class (optimisation) | LP, MILP |
| Model class (simulation) | - |
| Other | |
| Short description of mathematical model class | Optimisation is LP or MILP; load flow is nonlinear. |
| Mathematical objective | CO2, costs, RE-share |
| Approach to uncertainty | Deterministic |
| Suited for many scenarios / monte-carlo | |
| typical computation time | less than a day |
| Typical computation hardware | HPC for larger problems, smaller problems can be computed on local |
| Technical data anchored in the model | - |
| Interfaces | |
| Model file format | .nc |
| Input data file format | .csv, .nc |
| Output data file format | .csv, .nc |
| Integration with other models | open_Ego; DINGO |
| Integration of other models |
| Citation reference | Journal of Open Research Software, 2018, 6 (1) |
| Citation DOI | https://dx.doi.org/https://doi.org/10.5334/jors.188 |
| Reference Studies/Models | https://www.pypsa.org/publications/index.html |
| Example research questions | https://www.pypsa.org/publications/index.html |
| Model usage | - |
| Model validation |
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| Example research questions | https://www.pypsa.org/publications/index.html |
| further properties | |
| Model specific properties | - |