Code for the paper "On the means, costs, and system-level impacts of 24/7 carbon-free energy procurement"
This repository contains the code to reproduce the complete workflow behind the manuscript.
Working paper in arXiv: https://arxiv.org/abs/2403.07876
Peer-reviewed paper in Energy Strategy Reviews (open access): https://doi.org/10.1016/j.esr.2024.101488
A growing number of public and private energy buyers are interested in 24/7 carbon-free energy (CFE) procurement, which means that every kilowatt-hour of electricity consumption is met by carbon-free sources at all times. It has the potential to overcome the limitations of established procurement schemes, such as the temporal mismatch between clean electricity supply and buyers' demand that is inherent to "volumetric" matching. Yet it is unclear how 24/7 CFE procurement affects the rest of the power system, and whether this effect is consistent across regional contexts and different levels of system cleanness. We use a mathematical model to systematically examine different designs, optimal procurement strategies, costs, and impacts of the 24/7 CFE matching, both for participating buyers and for regions where voluntary procurement occurs. We examine mechanisms driving system-level emissions reduction and how they vary across regions and over time. Our results indicate that clean energy procurement commitments have consistent beneficial effects on participants and the electricity system. Even as grids become cleaner over time, the hourly matching strategy contributes significantly to system-level emissions reduction. In addition, voluntary commitments to 24/7 CFE have a further transformative effect on electricity systems through accelerated innovation and early deployment of advanced energy technologies.
- Clone the repository:
git clone [email protected]:Irieo/247-procurement-paper.git
- Install the necessary dependencies using
environment.ymlfile. The following commands will do the job:
conda env create -f envs/environment.yml
conda activate 247-cfe
- To run all the scenarios from the paper, run the snakemake worflow:
snakemake -call
NB This call requires a high-performance computing environment.
It is possible to run a smaller version of the model by adjusting the settings in config.yaml. For example, changing the config setting area from "EU" to "regions" reduces the regional coverage of the model, making the size of the problem feasible to solve on a private laptop with 8GB RAM.
When the workflow is complete, the results will be stored in results directory. The directory will contain solved networks, plots, summary csvs and logs.
- At this point, you can also compile the LaTeX project
/manuscript/manuscript.texto reproduce the paper .pdf file.
The workflow is based on PyPSA networks exported from PyPSA-Eur built with myopic setting to get brownfield networks for 2025/2030. By default, the workflow uses already compiled networks located in the input folder.
Parallel to the repository you should also clone the technology-data repository:
git clone [email protected]:PyPSA/technology-data.git
The code is known to work with PyPSA 0.25.2, pandas 1.5.3, numpy 1.24.2, vresutils 0.3.1 and gurobi 10.0.1. The complete list of dependencies is in the envs/environment.yml file.
If you have troubles with a slow conda installation, we recommend to install mamba:
conda install -c conda-forge mamba
and then install the environment with a fast drop-in replacement via
mamba env create -f envs/environment.yml
Different licenses apply to different parts of the repository. See specifications here.