Preparing for the worst: Long-term and short-term weather extremes in resource adequacy assessment

Code for preprint "Preparing for the worst: Long-term and short-term weather extremes in resource adequacy assessment" (arXiv:2508.05163). Additional data accompanying the paper can be found at the Zenodo repository 10.5281/zenodo.16753688.

Abstract:

Security of supply is a common and important concern when integrating renewables in net-zero power systems. Extreme weather affects both demand and supply leading to power system stress; in Europe this stress spreads continentally beyond the meteorological root cause. We use an approach based on shadow prices to identify periods of elevated stress called system-defining events and analyse their impact on the power system. By classifying different types of system-defining events, we identify challenges to power system operation and planning. Crucially, we find the need for sufficient resilience back-up (power) capacities whose financial viability is precarious due to weather variability. Furthermore, we disentangle short- and long-term resilience challenges with distinct metrics and stress tests to incorporate both into future energy modelling assessments. Our methodology and implementation in the open model PyPSA-Eur can be re-applied to other systems and help researchers and policymakers in building more resilient and adequate energy systems.

Overview

This repository contains the code necessary to reproduce the results and plots. A fork of PyPSA-Eur which was used to run the optimisations is attached as a submodule. The network files as well as various data files (processing data, sensitivity analysis) are stored in the Zenodo repository 10.5281/zenodo.16753688. Detailed descriptions can be found in the Zenodo readme.

The results are analysed in the Jupyter notebooks workflow/notebooks/paper_plots.ipynb and workflow/notebooks/supplementary_material.ipynb; they utilise data from the Zenodo repository. These data were generated with the scripts in workflow/notebooks/.

Installation and set-up

1. Clone this git repository with the --recurse-submodules option in order to also pull the PyPSA-Eur submodule:

   git clone --recurse-submodules [email protected]:aleks-g/stressed-system.git```
   

2. Download the data from Zenodo and place it as described in the readme file, i.e. add the network files to workflow/pypsa-eur/results/.

3. Install the conda environments envs/environment.yaml (for running PyPSA-Eur) and envs/notebook_analysis.yaml (for running the notebooks). You can do this with the following commands:

   conda env create -f envs/environment.yaml
   conda env create -f envs/notebook_analysis.yaml

4. Run the following workflow with the following command to obtain the results of the capacity expansion and the filtered system-defining events (SDEs):

   cd workflow/pypsa-eur
   conda activate stressed-system
   snakemake -call all_difficult_periods --configfile config/stressful-weather.yaml -n #dry-run
   snakemake -call all_difficult_periods --configfile config/stressful-weather.yaml

   This will generate the necessary data files in the processing_data/stressful-weather-sensitivities/ folder.

5. For the validation of the results, run the dispatch optimisation:

   snakemake -call all_operational_years --configfile config/stressful-weather.yaml -n #dry-run
   snakemake -call all_operational_years --configfile config/stressful-weather.yaml

6. For the sensitivity analysis, run:

   snakemake -call all_operational_years --configfile config/stressful-weather-sensitivities.yaml -n #dry-run
   snakemake -call all_operational_years --configfile config/stressful-weather-sensitivities.yaml

   This will generate the necessary data files in the processing_data/stressful-weather-sensitivities/ folder.

Necessary data and inputs

The data provided in the Zenodo repository are necessary to run the workflow.

With the current settings, a Gurobi license is needed, e.g. an academic one. Other solvers can be used to run PyPSA-Eur as well.

If rerunning from scratch, follow the PyPSA-Eur workflow (v.0.13.0) as documented in as documented here following the codebase Run the following scripts afterwards:

• workflow/notebooks/_generate_data_for_analysis.py to generate the data for analysis.
• workflow/notebooks/_dashboard.py to generate the dashboard data.
• workflow/notebooks/_unserved_energy.py to generate the unserved energy data.
• workflow/notebooks/_cluster_sdes.py to generate the clusters.

Contributors

Aleksander Grochowicz, Hannah Bloomfield, Marta Victoria. The filtering of SDEs is based on previous work published in with the codebase available at this repository and copyrights are acknowledged in the relevant scripts.

Licenses

MIT license, unless specified otherwise.