A temporal model for vertical extrapolation of wind speed and wind energy assessment

Paola Crippa, Mariana Alifa, Diogo Bolster, Marc G. Genton, Stefano Castruccio

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Accurate 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.
Original languageEnglish (US)
Pages (from-to)117378
JournalApplied Energy
Volume301
DOIs
StatePublished - Jul 28 2021

ASJC Scopus subject areas

  • Energy(all)
  • Civil and Structural Engineering

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