Dataset Open Access

Supplementary Data: The Benefits of Cooperation in a Highly Renewable European Electricity Network

Schlachtberger, David; Brown, Tom; Schramm, Stefan; Greiner, Martin

Supplementary Data

The Benefits of Cooperation in a Highly Renewable European Electricity Network

The files in this record contain the model-specific code, input data, and output data considered in the Benefits of Cooperation paper.

You are welcome to use the provided data under the given open-source licence, and if you do please cite the paper doi:10.1016/
Please note that the derivation of the data in data/renewables/ is not open, because it uses the REatlas software [7] which has a closed source server part. (There is an free software implementation of the REatlas at but it wasn't ready in time to be used for this dataset.)

The code that is required to generate the output data consists of

  • the python code that builds and runs the PyPSA [0] model
  • a SLURM script to run the model with different parameters
  • a YAML file options.yml with the default parameter settings

The code heavily relies on the python package vresutils which is available at

The record also contains the input data in the data/ directory. They are described in detail in the paper, but a short summary is provided here:

  • costs: cost and other input parameter assumptions, see Table 1 in the paper.
  • graph: the network topology is given by a list of nodes (country names) and a list of edges connecting two nodes. Based on [1,2].
  • hydro: hydro generation data provided by the Restore2050 project [3]
    • inflow/: contains a csv files with daily inflow data for each country
    • emil_hydro_capas.csv: country-scale power and energy capacity
    • ror_ENTSOe_Restore2050.csv: the share of run-of-river of the total hydro generation, from ENTSO-E [4] or if unavailable from [3]
  • load: hourly country-scale consumption for 2011 from ENTSO-E [5]
  • renewables: generation potentials for the renewable technologies onshore wind, offshore wind, and solar per country based on historic weather data [6]. The jupyter-notebook europe_renewables_potentials.ipynb describes the data generation and uses the REatlas software [7] which has open-source client but closed-source server software. The used cutout can therefore not be made available here, but is solely based on data from [8]. The processed data are in:
    • store_p_nom_max/: installation potential per technology per region
    • store_o_max_pu_betas/: hourly maximum generation per unit of capacity per technology per region

The output data generated by the model is in sub-folders of the results/ directory following the naming scheme [costsource]-CO[CO2costs]-T[timerange]-[technologies]-LV[linevolume]_c[crossover]_base_[costsource]_solar1_7_[formulation]-[startdate]/, where

  • costsource = diw2030
  • CO2costs = 0
  • timerange = 1_8761
  • technologies = wWsgrpHb
  • linevolume = [float], None (line volume constraint of float * 5e8 TWkm, or optimised line volume)
  • crossover = 0 (deactivated the cross-over phase of the Gurobi optimiser)
  • formulation = angles, [blank] (power flow formulations: 'angles', or 'cycles')
  • startdate = time the optimisation was started


[0] ,

[1] S Becker, Transmission grid extensions in renewable electricity systems, PhD thesis (2015)

[2] ENTSO-E, Indicative values for Net Transfer Capacities (NTC) in Continental Europe. European Transmission System Operators, 2011,, accessed Jul 2014.

[3] A Kies, K Chattopadhyay, L von Bremen, E Lorenz, D Heinemann, Simulation of renewable feed-in for power system studies, RESTORE 2050 project report,

[4] European Transmission System Operators, Installed Capacity per Production Type in 2015, ENTSO-E (2016),


[6] D. Heide, M. Greiner, L. Von Bremen, C. Hoffmann, Reduced storage and balancing needs in a fully renewable European power system with excess wind and solar power generation, Renewable Energy 36 (9) (2011) 2515–2523.

[7] G. B. Andresen, A. A. Søndergaard, M. Greiner, Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis, Energy 93, Part 1 (2015) 1074 – 1088.

[8] S Saha et al., 2014: The NCEP Climate Forecast System Version 2. J. Climate, 27, 2185–2208,

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