This repository contains modified scripts to use PyPSA-Eur for sufficiency scenario studies. The demand data used in the scenarios are based on the CLEVER scenario (https://clever-energy-scenario.eu/). The data folder in the repository contains CSV files considered for demands in the scenarios, which can be freely used and utilized for reproduction purposes or further improvement of sufficiency scenarios. The scripts folder also includes a script to convert CLEVER sufficiency data for 28 countries into csv files which can also be freely utilized. The Config folder also contains config files used for 4 scenarios in the current study. For further information, please feel free to contact Sylvain Quoilin (squoilin@uliege.be) and Muhammad Umair Tareen (muhammadumair.tareen@uliege.be).
Quick Usage:
- Download repository
- Install the pypsa environment and activate pypsa-eur. conda env create -f envs/environment.yaml conda activate pypsa-eur
First run of the model: During the first run, all data bundles must be downloaded. In the config file, set the retrieve options as such:
- retrieve: auto
- prepare_links_p_nom: true
- retrieve_databundle: true
- retrieve_sector_databundle: true
- retrieve_cost_data: true
- build_cutout: false
- retrieve_cutout: true
- build_natura_raster: false
- retrieve_natura_raster: true
- custom_busmap: false
Be aware that, depending on your connection speed, download time may be several hours!
You can then run:
snakemake -call all
After running the whole snakemake, the options can be set back to:
- retrieve: auto
- prepare_links_p_nom: false
- retrieve_databundle: false
- retrieve_sector_databundle: false
- retrieve_cost_data: false
- build_cutout: false
- retrieve_cutout: false
- build_natura_raster: false
- retrieve_natura_raster: true
- custom_busmap: false
Package to be added to the environment
conda install plotly
pip install -U kaleido
Selection of the scenario:
- To run the default workflow, activate pypsa-eur and run, this will run all scenarios in a sequence:
snakemake -s Snakefile_master -call run_all_scenarios
- Tu run a different scenario/workflow, use the dedicated Snakefile, e.g:
snakemake -s Snakefile_suff -call all
- The Sankey codes for all scenarios are included in the repository to analyze the results.
myopic scenarios:
- The myopic scenarios perform the optimization for successive years defined in the config file. They can be run using the dedicated Snakefile.
PyPSA-Eur is an open model dataset of the European energy system at the transmission network level that covers the full ENTSO-E area. 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 model is described in the documentation and in the paper PyPSA-Eur: An Open Optimisation Model of the European Transmission System, 2018, arXiv:1806.01613. The model building routines are defined through a snakemake workflow. Please see the documentation for installation instructions and other useful information about the snakemake workflow. The model is designed to be imported into the open toolbox PyPSA.
WARNING: PyPSA-Eur is under active development and has several limitations which you should understand before using the model. The github repository issues collect known topics we are working on (please feel free to help or make suggestions). The documentation remains somewhat patchy. You can find showcases of the model's capabilities in the Joule paper The potential role of a hydrogen network in Europe, another paper in Joule with a description of the industry sector, or in a 2021 presentation at EMP-E. We do not recommend to use the full resolution network model for simulations. At high granularity the assignment of loads and generators to the nearest network node may not be a correct assumption, depending on the topology of the underlying distribution grid, and local grid bottlenecks may cause unrealistic load-shedding or generator curtailment. We recommend to cluster the network to a couple of hundred nodes to remove these local inconsistencies. See the discussion in Section 3.4 "Model validation" of the paper.
The dataset consists of:
- A grid model based on a modified GridKit extraction of the ENTSO-E Transmission System Map. The grid model contains 6763 lines (alternating current lines at and above 220kV voltage level and all high voltage direct current lines) and 3642 substations.
- The open power plant database powerplantmatching.
- Electrical demand time series from the OPSD project.
- Renewable time series based on ERA5 and SARAH, assembled using the atlite tool.
- Geographical potentials for wind and solar generators based on land use (CORINE) and excluding nature reserves (Natura2000) are computed with the atlite library.
A sector-coupled extension adds demand and supply for the following sectors: transport, space and water heating, biomass, industry and industrial feedstocks, agriculture, forestry and fishing. This completes the energy system and includes all greenhouse gas emitters except waste management and land use.
This diagram gives an overview of the sectors and the links between them:
Each of these sectors is built up on the transmission network nodes from PyPSA-Eur:
For computational reasons the model is usually clustered down to 50-200 nodes.
Already-built versions of the model can be found in the accompanying Zenodo repository.
We strongly welcome anyone interested in contributing to this project. If you have any ideas, suggestions or encounter problems, feel invited to file issues or make pull requests on GitHub.
- In case of code-related questions, please post on stack overflow.
- For non-programming related and more general questions please refer to the mailing list.
- To discuss with other PyPSA users, organise projects, share news, and get in touch with the community you can use the discord server.
- For bugs and feature requests, please use the PyPSA-Eur Github Issues page.
The code in PyPSA-Eur is released as free software under the
MIT License, see LICENSE.txt.
However, different licenses and terms of use may apply to the various
input data.