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An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power
With solar and wind power generation reaching unprecedented growth rates globally, much research effort has recently gone into a comprehensive mapping of the worldwide potential of these variable renewable electricity (VRE) sources. From a perspective of energy systems analysis, the locations with t...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9622823/ https://www.ncbi.nlm.nih.gov/pubmed/36316331 http://dx.doi.org/10.1038/s41597-022-01786-5 |
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author | Sterl, Sebastian Hussain, Bilal Miketa, Asami Li, Yunshu Merven, Bruno Ben Ticha, Mohammed Bassam Elabbas, Mohamed A. Eltahir Thiery, Wim Russo, Daniel |
author_facet | Sterl, Sebastian Hussain, Bilal Miketa, Asami Li, Yunshu Merven, Bruno Ben Ticha, Mohammed Bassam Elabbas, Mohamed A. Eltahir Thiery, Wim Russo, Daniel |
author_sort | Sterl, Sebastian |
collection | PubMed |
description | With solar and wind power generation reaching unprecedented growth rates globally, much research effort has recently gone into a comprehensive mapping of the worldwide potential of these variable renewable electricity (VRE) sources. From a perspective of energy systems analysis, the locations with the strongest resources may not necessarily be the best candidates for investment in new power plants, since the distance from existing grid and road infrastructures and the temporal variability of power generation also matter. To inform energy planning and policymaking, cost-optimisation models for energy systems must be fed with adequate data on potential sites for VRE plants, including costs reflective of resource strength, grid expansion needs and full hourly generation profiles. Such data, tailored to energy system models, has been lacking up to now. In this study, we present a new open-source and open-access all-Africa dataset of “supply regions” for solar photovoltaic and onshore wind power to feed energy models and inform capacity expansion planning. |
format | Online Article Text |
id | pubmed-9622823 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96228232022-11-02 An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power Sterl, Sebastian Hussain, Bilal Miketa, Asami Li, Yunshu Merven, Bruno Ben Ticha, Mohammed Bassam Elabbas, Mohamed A. Eltahir Thiery, Wim Russo, Daniel Sci Data Analysis With solar and wind power generation reaching unprecedented growth rates globally, much research effort has recently gone into a comprehensive mapping of the worldwide potential of these variable renewable electricity (VRE) sources. From a perspective of energy systems analysis, the locations with the strongest resources may not necessarily be the best candidates for investment in new power plants, since the distance from existing grid and road infrastructures and the temporal variability of power generation also matter. To inform energy planning and policymaking, cost-optimisation models for energy systems must be fed with adequate data on potential sites for VRE plants, including costs reflective of resource strength, grid expansion needs and full hourly generation profiles. Such data, tailored to energy system models, has been lacking up to now. In this study, we present a new open-source and open-access all-Africa dataset of “supply regions” for solar photovoltaic and onshore wind power to feed energy models and inform capacity expansion planning. Nature Publishing Group UK 2022-10-31 /pmc/articles/PMC9622823/ /pubmed/36316331 http://dx.doi.org/10.1038/s41597-022-01786-5 Text en © The Author(s) 2022 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 | Analysis Sterl, Sebastian Hussain, Bilal Miketa, Asami Li, Yunshu Merven, Bruno Ben Ticha, Mohammed Bassam Elabbas, Mohamed A. Eltahir Thiery, Wim Russo, Daniel An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power |
title | An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power |
title_full | An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power |
title_fullStr | An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power |
title_full_unstemmed | An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power |
title_short | An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power |
title_sort | all-africa dataset of energy model “supply regions” for solar photovoltaic and wind power |
topic | Analysis |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9622823/ https://www.ncbi.nlm.nih.gov/pubmed/36316331 http://dx.doi.org/10.1038/s41597-022-01786-5 |
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