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Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System
Improvements in modelling energy systems of populous emerging economies are highly decisive for a successful global energy transition. The models used–increasingly open source–still need more appropriate open data. As an illustrative example, we take the Brazilian energy system, which has great pote...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9946950/ https://www.ncbi.nlm.nih.gov/pubmed/36813797 http://dx.doi.org/10.1038/s41597-023-01992-9 |
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author | Deng, Ying Cao, Karl-Kiên Hu, Wenxuan Stegen, Ronald von Krbek, Kai Soria, Rafael Rochedo, Pedro Rua Rodriguez Jochem, Patrick |
author_facet | Deng, Ying Cao, Karl-Kiên Hu, Wenxuan Stegen, Ronald von Krbek, Kai Soria, Rafael Rochedo, Pedro Rua Rodriguez Jochem, Patrick |
author_sort | Deng, Ying |
collection | PubMed |
description | Improvements in modelling energy systems of populous emerging economies are highly decisive for a successful global energy transition. The models used–increasingly open source–still need more appropriate open data. As an illustrative example, we take the Brazilian energy system, which has great potential for renewable energy resources but still relies heavily on fossil fuels. We provide a comprehensive open dataset for scenario analyses, which can be directly used with the popular open energy system model PyPSA and other modelling frameworks. It includes three categories: (1) time series data of variable renewable potentials, electricity load profiles, inflows for the hydropower plants, and cross-border electricity exchanges; (2) geospatial data on the administrative division of the Brazilian federal states; (3) tabular data, which contains power plant data with installed and planned generation capacities, aggregated grid network topology, biomass thermal plant potential, as well as scenarios of energy demand. Our dataset could enable further global or country-specific energy system studies based on open data relevant to decarbonizing Brazil’s energy system. |
format | Online Article Text |
id | pubmed-9946950 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-99469502023-02-24 Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System Deng, Ying Cao, Karl-Kiên Hu, Wenxuan Stegen, Ronald von Krbek, Kai Soria, Rafael Rochedo, Pedro Rua Rodriguez Jochem, Patrick Sci Data Data Descriptor Improvements in modelling energy systems of populous emerging economies are highly decisive for a successful global energy transition. The models used–increasingly open source–still need more appropriate open data. As an illustrative example, we take the Brazilian energy system, which has great potential for renewable energy resources but still relies heavily on fossil fuels. We provide a comprehensive open dataset for scenario analyses, which can be directly used with the popular open energy system model PyPSA and other modelling frameworks. It includes three categories: (1) time series data of variable renewable potentials, electricity load profiles, inflows for the hydropower plants, and cross-border electricity exchanges; (2) geospatial data on the administrative division of the Brazilian federal states; (3) tabular data, which contains power plant data with installed and planned generation capacities, aggregated grid network topology, biomass thermal plant potential, as well as scenarios of energy demand. Our dataset could enable further global or country-specific energy system studies based on open data relevant to decarbonizing Brazil’s energy system. Nature Publishing Group UK 2023-02-22 /pmc/articles/PMC9946950/ /pubmed/36813797 http://dx.doi.org/10.1038/s41597-023-01992-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Deng, Ying Cao, Karl-Kiên Hu, Wenxuan Stegen, Ronald von Krbek, Kai Soria, Rafael Rochedo, Pedro Rua Rodriguez Jochem, Patrick Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System |
title | Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System |
title_full | Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System |
title_fullStr | Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System |
title_full_unstemmed | Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System |
title_short | Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System |
title_sort | harmonized and open energy dataset for modeling a highly renewable brazilian power system |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9946950/ https://www.ncbi.nlm.nih.gov/pubmed/36813797 http://dx.doi.org/10.1038/s41597-023-01992-9 |
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