Quantifying the relative importance of the spatial and temporal resolution in energy systems optimisation model
This repository is for the paper
Nandi Moksnes (1) *, William Usher (1)
- KTH Royal Institute of Technology
To be able to run the model you need to have approx 256 GB RAM. The workflow is only tested on a Windows computer, therefore, there might be small adjustements needed for other OS.
The workflow has a number packages that needs to be installed.
The easiest way to install the Python packages is to use miniconda.
Obtain the miniconda package (https://docs.conda.io/en/latest/miniconda.html):
- Add the conda-forge channel: conda config --add channels conda-forge
- Create a new Python environment: conda env create -f environment.yml
- Activate the new environment: conda activate GSA
To download the capacityfactors for solar and wind you need to have R on your computer. You can download R for free https://www.r-project.org/ You also need to install the package "curl" which you install through the R commander
install.packages("curl")To run the code you need to create accounts in the following places:
- https://www.renewables.ninja/ and get the token to download several files per hour
- https://payneinstitute.mines.edu/eog/nighttime-lights/ and the password is entered in the first cell in the notebook
Run first the src/build_initial_countrydata.py and make sure that the base files look as expected. Then run the src/scenario_builder.py to build all the scenarios.