Supplementary Data The Benefits of Cooperation in a Highly Renewable European Electricity Network doi:10.1016/j.energy.2017.06.004 arXiv:1704.05492 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/j.energy.2017.06.004. 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 https://github.com/FRESNA/atlite 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 opt_ws_network.py that builds and runs the PyPSA [0] model a SLURM script parameter_batch.py 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 https://github.com/FRESNA/vresutils 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 Footnotes [0] https://pypsa.org/ , https://doi.org/10.5281/zenodo.582307 [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, https://www.entsoe.eu/publications/market-reports/ntc-values/ntc-matrix/Pages/default.aspx, 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, https://doi.org/10.5281/zenodo.804244 [4] European Transmission System Operators, Installed Capacity per Production Type in 2015, ENTSO-E (2016), https://transparency.entsoe.eu/generation/r2/installedGenerationCapacityAggregation/show [5] https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package [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. https://doi.org/10.1016/j.renene.2011.02.009 [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. https://doi.org/10.1016/j.energy.2015.09.071 [8] S Saha et al., 2014: The NCEP Climate Forecast System Version 2. J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1