A comprehensive review on wind resource extrapolation models applied in wind energy

A review spanning across a 40-year period (1978-2018) and including a total of 332 applications has been addressed on theoretical and empirical wind resource extrapolation models applied in wind energy, which can be grouped into three main families: (i) the logarithmic models; (ii) the Deaves and Harris (DH) model; (iii) the power law (PL). Applied over 96 very heterogeneous locations worldwide, models have been tested against observations at upper extrapolation height and assessed by location characteristics, extrapolation range skills, and application economical advantages. The logarithmic models can nowadays be considered unsuitable for extrapolating wind resource to hub height of current multi-MW WTs, mainly because exhibiting a limited extrapolation range capability (about 10-50m median bin). Finer scores in extrapolating wind resource (mean absolute bias of 3.3%) and in predicting energy output (10.1%) were achieved by the DH model, also showing remarkable extrapolation range skills (10-80m median bin). However, although among the most economical and forward-looking solutions, its need for accurate z0 assessment and u* observations resulted so far in great limitations to its large-scale application for wind energy purposes (less than 1%). Eventually, the PL confirmed the most reliable - and largely most commonly used (73.5%) - approach for wind energy applications. Out of the plethora of PL models developed in the literature, the PL(?)-?lower and the PL(?)-?I were the finest in predicting both extrapolated wind resource (mean absolute error of 4% and 4.4%, respectively) and energy output (8.9% and 5.5%), also exhibiting extrapolation range skills meeting modern WTs requirements. By contrast, the PL using ?=1/7 returned among the worst scores, yet resulting - since the simplest - the solution most frequently applied (19.6%). This study also demonstrated that extrapolation tools requiring the most expensive instrumentation equipment do not necessarily return the finest scores.