Capacity factor time series for solar and wind power on a 50 km^2 grid in Europe

This spatio-temporal dataset contains capacity factors timeseries forlocations on a grid with 50km edge lengthin Europe. The data isresolved in one hour timesteps and comprises the years 2000--2016. It has been generated using Renewables.ninja and is based on MERRA-2reanalysis data. For each of the...

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Main Authors: Tröndle, Tim, Pfenninger, Stefan, Pickering, Bryn
Format: Other/Unknown Material
Language:English
Published: Zenodo 2022
Subjects:
Online Access:https://doi.org/10.5281/zenodo.6559895
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record_format openpolar
spelling ftzenodo:oai:zenodo.org:6559895 2024-09-15T18:14:30+00:00 Capacity factor time series for solar and wind power on a 50 km^2 grid in Europe Tröndle, Tim Pfenninger, Stefan Pickering, Bryn 2022-05-18 https://doi.org/10.5281/zenodo.6559895 eng eng Zenodo https://doi.org/10.1016/j.esr.2019.100388 https://doi.org/10.5281/zenodo.3246303 https://doi.org/10.5281/zenodo.3533038 https://zenodo.org/communities/rifs_library https://doi.org/10.5281/zenodo.3891480 https://doi.org/10.5281/zenodo.6559895 oai:zenodo.org:6559895 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode renewable electricity wind power solar power Europe time series info:eu-repo/semantics/other 2022 ftzenodo https://doi.org/10.5281/zenodo.655989510.1016/j.esr.2019.10038810.5281/zenodo.324630310.5281/zenodo.353303810.5281/zenodo.3891480 2024-07-25T22:48:31Z This spatio-temporal dataset contains capacity factors timeseries forlocations on a grid with 50km edge lengthin Europe. The data isresolved in one hour timesteps and comprises the years 2000--2016. It has been generated using Renewables.ninja and is based on MERRA-2reanalysis data. For each of the ~2700onshore location, it contains onetime series for onshore wind turbinesand fivetime series for PV installations with different orientations and tilts. PV time series exist for (1) installations on open fields, (2) installations on all possible rooftops, (3) south-facing and flat rooftops, (4) east- and west-facing rooftops, (5) north-facing rooftops.For each of the ~2800offshore location there is one timeseries for offshore wind turbines. Two GeoTIFF files contain spatial informationof onshore and offshore locations.For each of the three technologies --onshore wind, offshore wind, and PV -- there is one NetCDF file determining the temporal dimension and containing the data. The GeoTIFF and NetCDFfiles are linked through unique IDs for all locations. This data serves as input data to euro-calliope, a model of the European electricity system. The following parameters have been used to generate the timeseries: <code>resolution-grid: 50 # [km^2] corresponding to MERRA resolution pv-performance-ratio: 0.9 hub-height: onshore: 105 # m, median hub height of V90/2000 in Europe between 2010 and 2018 offshore: 87 # m, median hub height of SWT-3.6-107 in Europe between 2010 and 2018 turbine: onshore: "vestas v90 2000" # most built between 2010 and 2018 in Europe offshore: "siemens swt 3.6 107" # most built between 2010 and 2018 in Europe</code> CHANGELOG: Version 3 (2022-05-18) * Update spatial scope to include Iceland and its offshore EEZ. *Updatetemporal scope to include 2017 and 2018. Effect of increasing spatial scope is a slight change in the spatial position of the data points. Version 2 (2020-06-18) * Add time series for rooftop PV with different orientations. Other/Unknown Material Iceland Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language English
topic renewable electricity
wind power
solar power
Europe
time series
spellingShingle renewable electricity
wind power
solar power
Europe
time series
Tröndle, Tim
Pfenninger, Stefan
Pickering, Bryn
Capacity factor time series for solar and wind power on a 50 km^2 grid in Europe
topic_facet renewable electricity
wind power
solar power
Europe
time series
description This spatio-temporal dataset contains capacity factors timeseries forlocations on a grid with 50km edge lengthin Europe. The data isresolved in one hour timesteps and comprises the years 2000--2016. It has been generated using Renewables.ninja and is based on MERRA-2reanalysis data. For each of the ~2700onshore location, it contains onetime series for onshore wind turbinesand fivetime series for PV installations with different orientations and tilts. PV time series exist for (1) installations on open fields, (2) installations on all possible rooftops, (3) south-facing and flat rooftops, (4) east- and west-facing rooftops, (5) north-facing rooftops.For each of the ~2800offshore location there is one timeseries for offshore wind turbines. Two GeoTIFF files contain spatial informationof onshore and offshore locations.For each of the three technologies --onshore wind, offshore wind, and PV -- there is one NetCDF file determining the temporal dimension and containing the data. The GeoTIFF and NetCDFfiles are linked through unique IDs for all locations. This data serves as input data to euro-calliope, a model of the European electricity system. The following parameters have been used to generate the timeseries: <code>resolution-grid: 50 # [km^2] corresponding to MERRA resolution pv-performance-ratio: 0.9 hub-height: onshore: 105 # m, median hub height of V90/2000 in Europe between 2010 and 2018 offshore: 87 # m, median hub height of SWT-3.6-107 in Europe between 2010 and 2018 turbine: onshore: "vestas v90 2000" # most built between 2010 and 2018 in Europe offshore: "siemens swt 3.6 107" # most built between 2010 and 2018 in Europe</code> CHANGELOG: Version 3 (2022-05-18) * Update spatial scope to include Iceland and its offshore EEZ. *Updatetemporal scope to include 2017 and 2018. Effect of increasing spatial scope is a slight change in the spatial position of the data points. Version 2 (2020-06-18) * Add time series for rooftop PV with different orientations.
format Other/Unknown Material
author Tröndle, Tim
Pfenninger, Stefan
Pickering, Bryn
author_facet Tröndle, Tim
Pfenninger, Stefan
Pickering, Bryn
author_sort Tröndle, Tim
title Capacity factor time series for solar and wind power on a 50 km^2 grid in Europe
title_short Capacity factor time series for solar and wind power on a 50 km^2 grid in Europe
title_full Capacity factor time series for solar and wind power on a 50 km^2 grid in Europe
title_fullStr Capacity factor time series for solar and wind power on a 50 km^2 grid in Europe
title_full_unstemmed Capacity factor time series for solar and wind power on a 50 km^2 grid in Europe
title_sort capacity factor time series for solar and wind power on a 50 km^2 grid in europe
publisher Zenodo
publishDate 2022
url https://doi.org/10.5281/zenodo.6559895
genre Iceland
genre_facet Iceland
op_relation https://doi.org/10.1016/j.esr.2019.100388
https://doi.org/10.5281/zenodo.3246303
https://doi.org/10.5281/zenodo.3533038
https://zenodo.org/communities/rifs_library
https://doi.org/10.5281/zenodo.3891480
https://doi.org/10.5281/zenodo.6559895
oai:zenodo.org:6559895
op_rights info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
op_doi https://doi.org/10.5281/zenodo.655989510.1016/j.esr.2019.10038810.5281/zenodo.324630310.5281/zenodo.353303810.5281/zenodo.3891480
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