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ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
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Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America

Authors: Sterl, Sebastian; Sterl, Sebastian;

Solar PV and wind power Model Supply Region (MSR) dataset as energy model input for countries in Central and South America

Abstract

This dataset provides model-ready data to include geospatial differentiation in solar and wind power investment options in energy models (primarily capacity expansion models and dispatch models) at the level of every Central and South American country. The methodology used to create the dataset takes into account resource quality, land use restrictions, distance from infrastructure, and other factors. It was previously applied to create an all-Africa dataset explained in Sterl et al. (2022) and published by Sterl, Hussain & Elabbas (2023). Folder (1) provides shapefiles of each country's overall feasible area for developing solar and wind power projects, under the restrictions/criteria mentioned above and described in Sterl et al. (2022). Folder (2) provides the best 5% ("best" measured by expected LCOE, from lowest to highest, including grid and road extension costs; 5% measured in terms of coverage of a country's area) of each country's solar and wind development potential, including hourly time series for model input. Folder (3) provides the corresponding shapefiles. Folder (4) provides simplified/aggregated results in terms of MSR clusters (see Sterl et al. 2022 for details), alongside hourly time series based on the meteorological year 2018. The amount of clusters was chosen to be 3, 5 or 10 depending on country size. Folder (5) provides PDF-file maps at the country level, showing resource strength and clustering outcomes by MSR (post-screening). Explanations of the headers in any spreadsheet files are provided in the Supplementary Information of Sterl et al. (2022). Countries/territories included in the dataset: ArgentinaBelizeBoliviaBrazilChileColombiaCosta RicaCubaDominican RepublicEcuadorEl SalvadorFrench GuianaGuatemalaGuyanaHaitiHondurasJamaicaNicaraguaPanamaParaguayPeruSurinameUruguayVenezuela References Sterl, S., Hussain, B., Miketa, A. et al. An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power. Sci Data 9, 664 (2022). https://doi.org/10.1038/s41597-022-01786-5 Sterl, S., Hussain, B., & Elabbas, M. (2023). Data for the paper « An all-Africa dataset of energy model "supply regions" for solar PV and wind power » (1.2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14870967

Related Organizations
Keywords

Caribbean, variable renewables, Latin America, LCOE, energy models, solar power, Central America, South America, wind power

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average