Source-linked AI summary
PyPSA-Eur: An Open Optimisation Model of the European Transmission System
Jonas Hörsch, Fabian Hofmann, David Schlachtberger, Tom Brown
TL;DR
European electricity-system transformation requires a detailed, continental transmission model, while existing openly available data lacks sufficient coverage and geographic detail. The paper constructs PyPSA-Eur from open data, combining network, plant, demand, renewable, and land-use information with an automated pipeline. The resulting dataset is a plausible approximation validated against official statistics, literature, power-flow analysis, and other network models, while remaining open to further improvement.
Problem
Europe’s changing generation, markets, electrification, and transmission constraints require an openly available, geographically detailed model of the full European transmission network.
Method
The paper assembles PyPSA-Eur from open datasets covering network topology, power plants, load and renewable time series, and renewable expansion potentials, using automated processing and network-comparison methods.
Results
6001 HVAC lines, 46 HVDC lines, 3657 substations, and 1320 auxiliary buses comprise the model’s transmission network, which validation finds to be a plausible approximation of Europe’s power system.
Takeaways & Limitations
PyPSA-Eur provides an open, high-resolution model for European generation and transmission expansion planning and can also support current-system operational studies.
Takeaways & Limitations
Line impedances and several grid assets are approximated or unavailable, while load aggregation can misassign assets and overlook local spatial differences.
Abstract
from arXiv · showhide
PyPSA-Eur, the first open model dataset of the European power system at the transmission network level to cover the full ENTSO-E area, is presented. It contains 6001 lines (alternating current lines at and above 220 kV voltage level and all high voltage direct current lines), 3657 substations, a new open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and 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. The restriction to freely available and open data encourages the open exchange of model data developments and eases the comparison of model results. A further novelty of the dataset is the publication of the full, automated software pipeline to assemble the load-flow-ready model from the original datasets, which enables easy replacement and improvement of the individual parts. This paper focuses on the description of the network topology, the compilation of a European power plant database and a top-down load time-series regionalisation. It summarises the derivation of renewable wind and solar availability time-series from re-analysis weather datasets and the estimation of renewable capacity potentials restricted by land-use. Finally, validations of the dataset are presented, including a new methodology to compare geo-referenced network datasets to one another.
1. Introduction
Europe’s electricity transformation requires detailed, continental transmission modelling, but existing openly available data lacks the coverage, licensing, and geographic detail needed for such studies. PyPSA-Eur addresses these gaps with an open, high-resolution model and an automated, reproducible data pipeline.
- Variable renewables, market liberalisation, electrification, and continental energy-market integration are transforming Europe’s electricity system.
- Continental scope captures international trading and long-range renewable smoothing, while high spatial detail represents persistent national grid bottlenecks.Wind generation has a typical correlation length of around 600 km.
- Existing ENTSO-E transmission data is unsuitable for comparison because of restrictive licensing, incomplete geographic coverage, and missing substation geolocations.
- PyPSA-Eur provides an open, detailed model covering the full ENTSO-E area, including grid data, power plants, renewable availability, loads, and expansion potentials.
- The dataset includes an automated pipeline that generates the model from raw data and supports replacement or improvement of individual components.
2. Data sources and methods
PyPSA-Eur assembles an open, detailed European transmission model by extracting and adapting network data, integrating freely available power-plant records, and applying probabilistic matching with explicit assumptions. The resulting dataset includes 6001 HVAC lines, 46 HVDC lines, 3657 substations, and 1320 auxiliary buses.
- Network topology: The network topology is extracted from the ENTSO-E Interactive Map using GridKit rather than a published extract containing duplicated lines and missing short connections.The toolkit was extended to stitch vector tiles, reconnect HVDC lines, split multi-voltage AC lines, and align CSV formats with PyPSA.
- Network topology: The model covers 220 kV, 300 kV, and 380 kV buses and transmission lines in the European landmass or exclusive economic zones.It includes current lines plus selected planned or near-construction lines, which are marked in the dataset.
- Network topology: 6001 HVAC lines and 46 HVDC lines are represented alongside 3657 substations and 1320 auxiliary buses.HVAC line volume is 345.7 TW km, including 17 TW km under construction; HVDC volume is 6.2 TW km, including 2.3 TW km under construction.
- Conventional power plants: Powerplantmatching standardises freely available databases, links records through deduplication and record linkage, and reduces conflicting claims to the most likely attributes.The workflow combines sources including OPSD, ENTSO-E PPL, ESE, GEO, CARMA, and GPD, with Duke calculating record-match probabilities from column similarities.
- Conventional power plants: The matching procedure assumes pairwise conditional independence of comparison features and unbiased 0.5 prior probabilities for matching versus non-matching records.The classifier therefore ignores correlations such as the relationship between plant technology and capacity, while the prior is less realistic than the true matching probability.
- Conventional power plants: 3501 power plants totaling 663 GW are compiled, with a 12% mean absolute error against SO&AF country-wise capacity averages.The deviation is below 27% in every country except Bulgaria and Lithuania after adding unmatched OPSD plants.
- Renewable generation: Wind-generation time-series calibration uses parameters fitted against Danish wind feed-in, while country-specific bias differences identified elsewhere are deferred to a future model version.The reported parameters are η = 0.95, Δu = 1.27 m/s, and σ0 = 2.29 m/s.
3. Validation
Validation compares PyPSA-Eur’s line lengths, network topology, renewable potentials, and peak-load feasibility against reference data and alternative network models. The results show strong agreement for some aggregate measures, but topology-sensitive capacities and uncorrected grid bottlenecks remain important limitations.
- 3.1. Network total line lengths: Mean absolute deviations from ENTSO-E circuit lengths are 15% for 220 kV, 7% for 300 kV, and 9% for 380 kV lines.Differences are attributed mainly to the artistic ENTSO-E map representation, voltage misclassification, and possible network updates.
- 3.2. Network topology: A new k-means methodology jointly clusters geo-located buses before reconnecting network elements for topology comparison.The approach addresses differences in bus and line locations and counts across PyPSA-Eur, osmTGmod, and ELMOD-DE.
- 3.2. Network topology: Aggregate capacities between 80 clusters show bad agreement because clustering can join buses on topologically distinct lines.Using 20 to 40 clusters allows stronger electrical-distance weighting and makes large-zone capacity comparisons more informative.
- 3.2. Network topology: Line volumes within and attached to clusters are robust to problematic associations and highly correlated across the compared networks.ELMOD-DE has proportionally less line volume than osmTGmod and PyPSA-Eur, while retaining approximately the same spatial distribution.
- 3.3. Potentials for expansion of renewables: Germany’s modeled installable potentials are 441 GW for onshore wind, 87 GW for fixed offshore wind, and 350 GW for solar photovoltaics.The comparisons show that potential estimates vary with land-use, environmental, and other constraints.
- 3.4. Model validation: Linear optimal power flow: At 0.51 TW European peak load, the model sheds 20.4 GW because modeled grid bottlenecks prevent delivery despite sufficient generation capacity.Shedding falls to about 6 GW when short-line capacity constraints are lifted and to 1% of peak load after clustering to 1500 buses.
4. Limitations
PyPSA-Eur is a functional, partially validated European transmission-system dataset, but missing data requires numerous approximations that constrain its fidelity. Key limitations affect grid parameters and topology, load and generator allocation, renewable plant representation, hydropower data, and system boundaries.
- Scope: The dataset contains many approximations due to missing data, so the authors present its limitations both as a user warning and as an invitation to improve them.These limitations define the dataset as a plausible working model rather than a complete representation of all system details.
- Grid representation: Missing line impedances require approximations based on line lengths and standard parameters, while busbar, switch, transformer, and reactive-compensation data are unavailable.The grid map also contains small readability distortions, and specific conductor choices are ignored.
- Load and generator allocation: Voronoi aggregation can connect assets to the wrong transmission substation because it ignores distribution-network topology.Country-level load assumptions proportional to population and GDP may also miss local circumstances, while open load data may not represent true vertical load.
- Renewable generation: Existing wind, solar, small hydro, geothermal, marine, and biomass plants are excluded because data are unavailable in many countries.Approximate wind and solar distributions can instead be generated proportional to location-specific capacity factors.
- Hydropower and system boundaries: Hydropower representation uses country-level storage totals and inflow approximations, omitting plant-specific storage, local topography, and basin drainage.The dataset also omits border connections and power flows involving Russia, Belarus, Ukraine, Turkey, and Morocco, as well as some islands.
5. Conclusions
PyPSA-Eur provides an open, high-resolution dataset of the full European transmission system assembled from public data and released with its code and data. Validation indicates that it is a plausible approximation, while its openness supports continued improvement and studies of future system investment and operation.
- Conclusions: PyPSA-Eur covers the full European transmission system with a high-resolution grid, load data, geo-referenced conventional plants, renewable potentials, and availability time series.The dataset and all code are based on publicly available, open datasets.
- Conclusions: Validation combines circuit-length comparisons, renewable-potential checks, optimal power flow, and a new network-topology comparison technique.Together, these validation steps assess several structural and operational aspects of the model.
- Conclusions: The validation results demonstrate that PyPSA-Eur is a plausible approximation of the European power system, although further validation is desirable.The authors explicitly identify additional validation as a way to increase confidence in the model.
- Conclusions: Because PyPSA-Eur is open, research groups can improve it when better data or methodologies become available.The authors also hope open unofficial datasets will encourage data holders to release official datasets for third-party modelling.
- Conclusions: The dataset is designed primarily for optimising future generation and transmission investment and can also be adapted to studies of current-system operation.It is intended to support transparent discussion of future European energy-system needs amid major structural changes.