Increasing the resolution of solar and wind time series for energy system modeling: A review

This is an open access article under the CC BY license

Detalles Bibliográficos
Autores: Omoyele, Olalekan, Hoffmann, Maximilian, Koivisto, Matti, Larrañeta, Miguel, Weinand, Jann Michael, Stolten, Detlef
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/152432
Acceso en línea:https://hdl.handle.net/11441/152432
https://doi.org/10.1016/j.rser.2023.113792
Access Level:acceso abierto
Palabra clave:Energy system optimization
Machine learning
Solar photovoltaics
Temporal resolution
Wind speed distribution
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spelling Increasing the resolution of solar and wind time series for energy system modeling: A reviewOmoyele, OlalekanHoffmann, MaximilianKoivisto, MattiLarrañeta, MiguelWeinand, Jann MichaelStolten, DetlefEnergy system optimizationMachine learningSolar photovoltaicsTemporal resolutionWind speed distributionThis is an open access article under the CC BY licenseBottom-up energy system models are often based on hourly time steps due to limited computational tractability or data availability. However, in order to properly assess the rentability and reliability of energy systems by accounting for the intermittent nature of renewable energy sources, a higher level of detail is necessary. This study reviews different methods for increasing the temporal resolutions of time series data for global horizontal and direct normal irradiance for solar energy, and wind speed for wind energy. The review shows that stochastic methods utilizing random sampling and non-dimensional approaches are the most frequently employed for solar irradiance data downscaling. The non-dimensional approach is particularly simple, with global applicability and a robust methodology with good validation scores. The temporal increment of wind speed, however, is challenging due to its spatiotemporal complexity and variance, especially for accurate wind distribution profiles. Recently, researchers have mostly considered methods that draw on the combination of meteorological reanalysis and stochastic fluctuations, which are more accurate than the simple and conventional interpolation methods. This review provides a road map of how to approach solar and wind speed temporal downscaling methods and quantify their effectiveness. Furthermore, potential future research areas in solar and wind data downscaling are also highlighted.ElsevierIngeniería EnergéticaTEP122: Termodinamica y Energias Renovables2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/152432https://doi.org/10.1016/j.rser.2023.113792reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésRenewable and Sustainable Energy Reviews, 189, 113792.https://www.sciencedirect.com/science/article/pii/S1364032123006494?via%3Dihubinfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1524322026-06-17T12:51:07Z
dc.title.none.fl_str_mv Increasing the resolution of solar and wind time series for energy system modeling: A review
title Increasing the resolution of solar and wind time series for energy system modeling: A review
spellingShingle Increasing the resolution of solar and wind time series for energy system modeling: A review
Omoyele, Olalekan
Energy system optimization
Machine learning
Solar photovoltaics
Temporal resolution
Wind speed distribution
title_short Increasing the resolution of solar and wind time series for energy system modeling: A review
title_full Increasing the resolution of solar and wind time series for energy system modeling: A review
title_fullStr Increasing the resolution of solar and wind time series for energy system modeling: A review
title_full_unstemmed Increasing the resolution of solar and wind time series for energy system modeling: A review
title_sort Increasing the resolution of solar and wind time series for energy system modeling: A review
dc.creator.none.fl_str_mv Omoyele, Olalekan
Hoffmann, Maximilian
Koivisto, Matti
Larrañeta, Miguel
Weinand, Jann Michael
Stolten, Detlef
author Omoyele, Olalekan
author_facet Omoyele, Olalekan
Hoffmann, Maximilian
Koivisto, Matti
Larrañeta, Miguel
Weinand, Jann Michael
Stolten, Detlef
author_role author
author2 Hoffmann, Maximilian
Koivisto, Matti
Larrañeta, Miguel
Weinand, Jann Michael
Stolten, Detlef
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Ingeniería Energética
TEP122: Termodinamica y Energias Renovables
dc.subject.none.fl_str_mv Energy system optimization
Machine learning
Solar photovoltaics
Temporal resolution
Wind speed distribution
topic Energy system optimization
Machine learning
Solar photovoltaics
Temporal resolution
Wind speed distribution
description This is an open access article under the CC BY license
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/152432
https://doi.org/10.1016/j.rser.2023.113792
url https://hdl.handle.net/11441/152432
https://doi.org/10.1016/j.rser.2023.113792
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Renewable and Sustainable Energy Reviews, 189, 113792.
https://www.sciencedirect.com/science/article/pii/S1364032123006494?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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