Quantifying the relative importance of the spatial and temporal resolution in energy systems optimisation model

This repository is for the paper

Quantifying the relative importance of the spatial and temporal resolution in energy systems optimisation model

Nandi Moksnes (1) *, William Usher (1)

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.

Modelled input parameters

Python dependencies

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):

1. Add the conda-forge channel: conda config --add channels conda-forge
2. Create a new Python environment: conda env create -f environment.yml
3. Activate the new environment: conda activate GSA

R

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")

Required accounts (free to register)

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 the model

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.