Code explores impacts based on a brownfield network exported from PyPSA-Eur-Sec.

The methodology is the same as in the Princeton 24/7 Study.

Google sponsored both this project and the Princeton study.

Traditional Power Purchase Agreements (PPAs) for renewable energy have seen rapid growth in recent years, but they only match supply and demand on average over a longer period such as a year. There is increasing interest from corporations such as Google to match their demand with clean energy supply on a truly 24/7 basis, whether that is using variable renewables paired with storage, or using dispatchable clean sources such as geothermal power. In 2020 Google committed to operating entirely on 24/7 carbon-free energy (CFE) at all of its data centres and campuses worldwide by 2030. In this project we will explore different designs for a 24/7 carbon-free PPA, and how their deployment affects the rest of the energy system.

Further information:

• Google's 24/7 CFE concept
• Google's 24/7 CFE metrics and methodology
• UN's 24/7 Carbon-free Energy Compact

The folder data should contain PyPSA networks exported from PyPSA-Eur-Sec built with myopic setting to get brownfield networks for 2025/2030. To get started, you can use sample networks from the input folder.

Parallel to the repository you should also clone the technology-data repository.

The code is known to work with PyPSA 0.18.1, pandas 1.2.4, numpy 1.19.0, vresutils 0.3.1 and gurobi 9.1.2.

The complete list of package requirements is in the envs/environment.yml file. The environment can be installed and activated using:

.../247-cfe % conda env create -f envs/environment.yml
.../247-cfe % conda activate 247-cfe

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

Copyright 2022 Tom Brown, Iegor Riepin.

This code is licensed under the open source MIT License.