Climapower

This repository contains scripts to generate weather- and climate-driven power supply and demand time series for power and energy system analyses.

The methodology is described in the following paper:

"Weather- and climate-driven power supply and demand time series for power and energy system analyses", under review.

Installation

To install the package, clone the repository and install the Python packages available in the yaml files in the environments folder:

git clone
cd climapower
conda env create -f environments/environment_for_retrieving_climate_data.yml
conda deactivate
conda env create -f environments/environment_for_converting_climate_data_to_energy.yml
conda deactivate

Note that climapower uses a modifed version of atlite, which at the time of this development was not capable of generating cooling demand. This version of atlite is included in the modified_atlite folder. Atlite can now generate cooling demand profiles on its own.

Usage

Retrieve meteorological data

To retrieve meteorological data, you need to have a CDS API key and actvate the environment for retrieving meteorological data:

conda activate retrieve_climate_data

Then, you can run the script to retrieve the data:

python download_ERA5_data.py

or

python download_CORDEX_data.py

Convert meteorological data to energy time series

To convert meteorological data to energy time series, you need to activate the environment for converting meteorological data to energy:

conda activate climate_to_energy_conversion

Then, you can run the script to convert the data:

python get_wind_resource.py

or

python get_solar_resource.py

or

python get_hydro_resource.py

or

python get_heating_demand.py

or

python get_cooling_demand.py

These scripts will generate time series for wind power, solar power, hydro power, heating demand, and cooling demand for all European countries. Alternatively, you can specify the country name as an argument to generate the time series for a specific country:

python get_wind_resource.py "Germany"

Calibrate energy time series

To calibrate the energy time series, you need to activate the environment for calibrating energy time series:

conda activate climate_to_energy_conversion

Then, you can run the script to calibrate the data:

python calibrate_wind_resource.py

or

python calibrate_solar_resource.py

or

python calibrate_hydro_resource.py