RPGV

How to Distribute New Solar Systems in Europe to Reduce multi-day Power Generation Variability (RPGV).

Code underlying analysis performed in

Mühlemann, D., Folini, D., Pfenninger, S., Wild, M., Wohland, J., 2021. Meteorologically-Informed Spatial Planning of European PV Deployment to Reduce Multiday Generation Variability (link to DOI when paper is published)

If you content of this repository or code derived from it in academic work, please cite the above publication.

Input Data

Different source data are needed to run the scripts and recreate the results and plots. Here you can find an overview of these data with a short description of what was used within this work and where you can download it.

ERA5

We use ERA5 hourly data on pressure levels from Januray 1979 to June 2020. The geographical area is 80°W to 40°E, 30°N to 90°N. The data are downloaded per season and are used in the first script '1_gph-daily-mean-calc.py':

file1 = data_folder / 'gph-djf-all.nc'
file2 = data_folder / 'gph-mam-all.nc'
file3 = data_folder / 'gph-jja-all.nc'
file4 = data_folder / 'gph-son-all.nc'

Renewables.ninja

Country-level PV power generation is taken from Renewables.ninja v1.1. The explicit used dataset can be found here. We use European country-specific capacity factors based on the reanalyse dataset MERRA-2 covering 1985-2016. The dataset is first used in the script '7_wr-ninja-combi.py':

filename = data_folder / 'ninja/ninja_europe_pv_v1.1/ninja_pv_europe_v1.1_merra2.csv'

Installed PV capacities

To compute actual national PV power generation from current capacity factors, we use installed capacities from the International Renewable Energy Agency. Since these numbers are listed in a PDF we provide the created/used csv file in the folder 'sources' with the name 'installed_capacities_IRENA.csv'. The dataset is used in the scripts '10_2030-all-scenarios.py' and '11_2050-all-scenarios.py':

ic_file = data_folder / 'source/installed_capacities_IRENA.csv'

NECPs

To assess future configurations, we use the National Energy and Climate Plans (NECPs) in which countries define capacity targets until 2030. The actual source to get the planed installed capacities are taken from SolarPower Europe where the NECPs are nicely summarized in an interactive map. When NECPs are not available we consider individual national plans or, as a last resort, apply the average PV installed capacity growth rate until the year 2030 from all EU countries to the currently installed PV capacities. The used dataset can be found in the folder sources with the name 'planned-IC-2030.xlsx'. The dataset is used in the scripts '10_2030-all-scenarios.py' and '11_2050-all-scenarios.py':

ic_2030 = data_folder / 'source/planned-IC-2030.xlsx'

Electricity consumption data

We use hourly electricity consumption data from Open Power System Data and fill gaps with data from the statistical office of the European Union. The datasets are used in the scripts '10_2030-all-scenarios.py' and '11_2050-all-scenarios.py':

country_load_file = data_folder / 'source/opsd-time_series-2020-10-06/time_series_60min_singleindex.csv'
eurostat_country_load_file = data_folder / 'source/eurostat_load.xlsx'

Installed capacity potential

The upper bound which is used in the linear least-square problems is always set to the roof-top mounted PV potential per country. The data for the roof-top mounted PV potential is taken by Tröndle et al. (2019). It can also be found on the 'sources' folder with the name 'IC-potential.yaml'. The dataset is used in the scripts '10_2030-all-scenarios.py' and '11_2050-all-scenarios.py':

with open(data_folder / 'source/IC-potential.yaml') as file:
    ic_pot_file = yaml.safe_load(file)

Figure overview

Figure	Filename	Creating python script
Figure 1	approach-overview.tif	generated externally
Figure 2	wr_and_cf.tiff	9_plot-wr-and-cf.py
Figure 3	2030_ic-distribution_additional.tiff	10_2030-all-scenarios.py
Figure 4	2030_tot_variability.tiff	10_2030-all-scenarios.py
Figure 5	2050_ic-distribution_additional.tiff	11_2050-all-scenarios.py
Figure 6	2050_tot_variability.tiff	11_2050-all-scenarios.py
Supplementary material Figure S1	anomalies.tiff	12_plot_weather_regimes-gph.py, 13_plot_weather_regimes_irradiance.py, 14_plot_weather_regimes_temp.py
Supplementary material Figure S2	2030_ic-distribution_absolut.tiff	10_2030-all-scenarios.py
Supplementary material Figure S3	2030_variability.tiff	10_2030-all-scenarios.py
Supplementary material Figure S4	2050_ic-distribution_absolut.tiff	11_2050-all-scenarios.py
Supplementary material Figure S5	2050_variability.tiff	11_2050-all-scenarios.py

License

MIT License - See LICENSE for more detail.
This excludes all contents from International Renewable Energy Agency and Tröndle et al. (2019) provided in the source folder which remains the property of the respective copyright holders.