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Supplementary Data: Code, Input Data and Result Summaries: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system

Supplementary Data Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M.

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CreatorBrown, Tom
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Published2018-01-12
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DOI10.5281/zenodo.1146666
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Downloads611
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Licensecc-by-4.0
File Size186.4 MB
Data TypeDataset
Published2018
Licensecc-by-4.0
Total Views3,613
Total Downloads611

Supplementary Data

Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system

Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner

arXiv:1801.05290

The files in this record contain the scripts to build the model, input data and result summaries for the model PyPSA-Eur-Sec-30 described in the above publication.

The full results files (which include the post-processed input data) can be found in a companion Zenodo repository. (The supplementary data was split because of the size of the full results.)

WARNING: A newer, improved version of this model, PyPSA-Eur-Sec, is under construction on GitHub.

Scripts

To use the scripts, you need the following free software Python libraries:

  • PyPSA for the modelling framework
  • vresutils for various helper functions to build the model instance
  • atlite to process weather data into power system data
  • snakemake to organise the execution of the software

and other standard libraries from the Python Package Index (PyPI), such as pandas, pyomo, countrycode, etc.

snakemake requires that all code runs with Python version 3. The code setup is known to work with the following versions: PyPSA 0.12.0, pandas 0.21.1, numpy 0.14.0, scipy 0.19.1, pyomo 5.2. You may need to downgrade your libraries to these versions for the scripts to work. If you insist on using the latest versions, please be aware that you'll need to make at least the following changes:

i) To accommodate changes in pandas versions 0.22 and higher, in scripts/prepare_network.py change "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby("technology").sum()" to "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby(level="technology").sum(min_count=1)".

ii) In later versions of PyPSA the component groups like "pypsa.components.one_port_components" have become network-specific and are stored instead at "network.one_port_components".

To solve the optimisation problem the scripts are coded to use the commercial solver Gurobi. To solve the problems in a reasonable time, you will need Gur

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Supplementary Data: Code, Input Data and Result Summaries:… (Full Dataset)186.4 MB
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Files are hosted on the source repository. Click download to access the full dataset.

Brown, Tom (2018). Supplementary Data: Code, Input Data and Result Summaries: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system. https://doi.org/10.5281/zenodo.1146666