Dataset Open Access

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

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

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{
"publisher": "Zenodo",
"DOI": "10.5281/zenodo.804338",
"author": [
{
"family": "Schlachtberger, David"
},
{
"family": "Brown, Tom"
},
{
"family": "Schramm, Stefan"
},
{
"family": "Greiner, Martin"
}
],
"issued": {
"date-parts": [
[
2017,
6,
8
]
]
},
"abstract": "<p>Supplementary Data</p>\n\n<p><em>The Benefits of Cooperation in a Highly Renewable European Electricity Network</em><br>\n<em>doi:10.1016/j.energy.2017.06.004</em><br>\n<em>arXiv:1704.05492</em></p>\n\n<p>The files in this record contain the model-specific code, input data, and output data considered in the Benefits of Cooperation paper.</p>\n\n<p>You are welcome to use the provided data under the given open-source licence, and if you do please cite the paper <em>doi:10.1016/j.energy.2017.06.004</em>.<br>\nPlease 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.)</p>\n\n<p>The code that is required to generate the output data consists of</p>\n\n<ul>\n\t<li>the python code opt_ws_network.py that builds and runs the PyPSA [0] model</li>\n\t<li>a SLURM script parameter_batch.py to run the model with different parameters</li>\n\t<li>a YAML file options.yml with the default parameter settings</li>\n</ul>\n\n<p>The code heavily relies on the python package <em>vresutils</em> which is available at https://github.com/FRESNA/vresutils</p>\n\n<p>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:</p>\n\n<ul>\n\t<li><strong>costs</strong>: cost and other input parameter assumptions, see <em>Table 1</em> in the paper.</li>\n\t<li><strong>graph</strong>: the network topology is given by a list of nodes (country names) and a list of edges connecting two nodes. Based on [1,2].</li>\n\t<li><strong>hydro</strong>: hydro generation data provided by the Restore2050 project [3]\n\t<ul>\n\t\t<li>inflow/: contains a csv files with daily inflow data for each country</li>\n\t\t<li>emil_hydro_capas.csv: country-scale power and energy capacity</li>\n\t\t<li>ror_ENTSOe_Restore2050.csv: the share of run-of-river of the total hydro generation, from ENTSO-E [4] or if unavailable from [3]</li>\n\t</ul>\n\t</li>\n\t<li><strong>load</strong>: hourly country-scale consumption for 2011 from ENTSO-E [5]</li>\n\t<li><strong>renewables</strong>: 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:\n\t<ul>\n\t\t<li>store_p_nom_max/: installation potential per technology per region</li>\n\t\t<li>store_o_max_pu_betas/: hourly maximum generation per unit of capacity per technology per region</li>\n\t</ul>\n\t</li>\n</ul>\n\n<p>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</p>\n\n<ul>\n\t<li>costsource = diw2030</li>\n\t<li>CO2costs = 0</li>\n\t<li>timerange = 1_8761</li>\n\t<li>technologies = wWsgrpHb</li>\n\t<li>linevolume = [float], None (line volume constraint of float * 5e8 TWkm, or optimised line volume)</li>\n\t<li>crossover = 0 (deactivated the cross-over phase of the Gurobi optimiser)</li>\n\t<li>formulation = angles, [blank] (power flow formulations: 'angles', or 'cycles')</li>\n\t<li>startdate = time the optimisation was started</li>\n</ul>\n\n<p>Footnotes</p>\n\n<p>[0] https://pypsa.org/ , https://doi.org/10.5281/zenodo.582307</p>\n\n<p>[1] S Becker, Transmission grid extensions in renewable electricity systems, PhD thesis (2015)</p>\n\n<p>[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.</p>\n\n<p>[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</p>\n\n<p>[4] European Transmission System Operators, Installed Capacity per Production Type in 2015, ENTSO-E (2016), https://transparency.entsoe.eu/generation/r2/installedGenerationCapacityAggregation/show</p>\n\n<p>[5] https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package</p>\n\n<p>[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\u20132523. https://doi.org/10.1016/j.renene.2011.02.009</p>\n\n<p>[7] G. B. Andresen, A. A. S\u00f8ndergaard, 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 \u2013 1088. https://doi.org/10.1016/j.energy.2015.09.071</p>\n\n<p>[8] S Saha et al., 2014: The NCEP Climate Forecast System Version 2. J. Climate, 27, 2185\u20132208, https://doi.org/10.1175/JCLI-D-12-00823.1</p>",
"title": "Supplementary Data: The Benefits of Cooperation in a Highly Renewable European Electricity Network",
"type": "dataset",
"id": "804338"
}
2,333
1,022
views
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