A boundary layer scaling technique for estimating near-surface wind energy using numerical weather prediction and wind map data. (15th December 2017)

• Record Type: Journal Article
• Authors: Allen, D.J.; Tomlin, A.S.; Bale, C.S.E.; Skea, A.; Vosper, S.; Gallani, M.L.
• Is Part Of: Applied energy. Volume 208(2017)
• Page Start: 1246
• Page End: 1257
• Publication Date: 2017-12-15
• Subjects: Near-surface wind -- Wind resource assessment -- Small and medium scale wind turbine -- Boundary layer scaling -- Numerical weather prediction; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.09.029
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Improved accuracy of wind speed predictions from a boundary layer scaling technique. Highly accurate power density predictions from numerical weather prediction data. Boundary layer scaling more suitable for wind speed prediction than mandated method. Robust wind resource assessment techniques for near-surface wind speeds. Abstract: A boundary layer scaling (BLS) method for predicting long-term average near-surface wind speeds and power densities was developed in this work. The method was based on the scaling of reference climatological data either from long-term average wind maps or from hourly wind speeds obtained from high-resolution Numerical Weather Prediction (NWP) models, with case study applications from Great Britain. It incorporated a more detailed parameterisation of surface aerodynamics than previous studies and the predicted wind speeds and power densities were validated against observational wind speeds from 124 sites across Great Britain. The BLS model could offer long-term average wind speed predictions using wind map data derived from long-term observational data, with a mean percentage error of 1.5% which provided an improvement on the commonly used NOABL (Numerical Objective Analysis of Boundary Layer) wind map. The boundary layer scaling of NWP data was not, however, able to improve upon the use of raw NWP data for near surface wind speed predictions. However, the use of NWP data scaled by the BLS model could offer improved power density predictions compared to the use of the reference data sets. Using a vertical scaling of the shape factor of a Weibull distribution fitted to the BLS NWP data, power density predictions with a 1% mean percentage error were achieved. This provided a significant improvement on the use of a fixed shape factor which must be utilised when only long-term average wind speeds are available from reference wind maps. The work therefore highlights the advantages that use of a BLS model for wind speed and NWP data for power density predictions can offer for small to medium scale wind energy resource assessments, potentially facilitating more robust annual energy production and financial assessments of prospective small and medium scale wind turbine installations.