Show simple item record

dc.contributor.authorCrippa, Paola
dc.contributor.authorAlifa, Mariana
dc.contributor.authorBolster, Diogo
dc.contributor.authorGenton, Marc G.
dc.contributor.authorCastruccio, Stefano
dc.date.accessioned2021-08-04T06:32:10Z
dc.date.available2021-08-04T06:32:10Z
dc.date.issued2021-07-28
dc.date.submitted2021-01-13
dc.identifier.citationCrippa, P., Alifa, M., Bolster, D., Genton, M. G., & Castruccio, S. (2021). A temporal model for vertical extrapolation of wind speed and wind energy assessment. Applied Energy, 301, 117378. doi:10.1016/j.apenergy.2021.117378
dc.identifier.issn0306-2619
dc.identifier.doi10.1016/j.apenergy.2021.117378
dc.identifier.urihttp://hdl.handle.net/10754/670392
dc.description.abstractAccurate wind speed estimates at turbine hub height are critical for wind farm operational purposes, such as forecasting and grid operation, but also for wind energy assessments at regional scales. Power law models have widely been used for vertical wind speed profiles due to their simplicity and suitability for many applications over diverse geographic regions. The power law requires estimation of a wind shear coefficient, α, linking the surface wind speed to winds at higher altitudes. Prior studies have mostly adopted simplified models for α, ranging from a single constant, to a site-specific constant in time value. In this work we (i) develop a new model for α which is able to capture hourly variability across a range of geographic/topographic features; (ii) quantify its improved skill compared to prior studies; and (iii) demonstrate implications for wind energy estimates over a large geographical area. To achieve this we use long-term high-resolution simulations by the Weather Research and Forecasting model, as well as met-mast and radiosonde observations of vertical profiles of wind speed and other atmospheric properties. The study focuses on Saudi Arabia, an emerging country with ambitious renewable energy plans, and is part of a bigger effort supported by the Saudi Arabian government to characterize wind energy resources over the country. Results from this study indicate that the proposed model outperforms prior formulations of α, with a domain average reduction of the wind speed RMSE of 23–33%. Further, we show how these improved estimates impact assessments of wind energy potential and associated wind farm siting.
dc.description.sponsorshipThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-CRG 7 2018-3742.2. The authors thank the King Abdullah City for Atomic and Renewable Energy (K.A.CARE) for providing the wind speed observational data. HRES-ECMWF operational analysis data were downloaded from the ECMWF data portal (https://www.ecmwf.int/en/forecasts/datasets/set-i.) through the KAUST ECMWF licence.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0306261921007819
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Applied Energy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Energy, [301, , (2021-07-28)] DOI: 10.1016/j.apenergy.2021.117378 . © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleA temporal model for vertical extrapolation of wind speed and wind energy assessment
dc.typeArticle
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalApplied Energy
dc.rights.embargodate2023-07-28
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Civil and Environmental Engineering and Earth Sciences, University of Notre Dame, Notre Dame, IN, USA.
dc.contributor.institutionDepartment of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA.
dc.identifier.volume301
dc.identifier.pages117378
kaust.personGenton, Marc G.
kaust.grant.numberOSR-CRG 7 2018-3742.2
dc.date.accepted2021-07-04
refterms.dateFOA2021-08-04T14:09:29Z
kaust.acknowledged.supportUnitCRG
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)
dc.date.published-online2021-07-28
dc.date.published-print2021-11


Files in this item

Thumbnail
Name:
manuscript_APEN_final.pdf
Size:
945.7Kb
Format:
PDF
Description:
Accepted manuscript
Embargo End Date:
2023-07-28

This item appears in the following Collection(s)

Show simple item record