Probabilistic stability and ‘tall’ wind profiles: theory and method for use in wind resource assessment

Abstract

A model has been derived for calculating the effects of stability and the finite height of the planetary boundary layer upon the long-term mean wind profile. A practical implementation of this probabilistic extended similarity-theory model is made, including its incorporation within the European Wind Atlas (EWA) methodology for site-to-site application. Theoretical and practical implications of the EWA methodology are also derived and described, including unprecedented documentation of the theoretical framework encompassing vertical extrapolation, as well as some improvement to the methodology. Results of the modelling are shown for a number of sites, with discussion of the models' efficacy and the relative improvement shown by the new model, for situations where a user lacks local heat flux information, as well as performance of the new model using measured flux statistics. Further, the uncertainty in vertical extrapolation is characterized for the EWA model contained in standard (i.e. WAsP) wind resource assessment, as well as for the new model.

Statistical characterization of roughness uncertainty and impact on wind resource estimation

“…Such treatment, in conjunction with taking the profile roughness and geostrophic-scale roughness to be the same, is a choice that we have made to facilitate systematic modeling of roughness-induced uncertainty; thus, we have been able to estimate the effect of roughness, which occurs through both the wind profile (vertical extrapolation) and through invocation of the GDL (horizontal extrapolation). A separate model for the uncertainty in vertical extrapolation using a logarithmic-based profile (as in the EWA and popular wind software, e.g., WAsP), but without considering roughness uncertainty, is given in Kelly and Troen (2016) and Kelly (2016). Treating the z 0 -related uncertainties separately, per the geostrophic drag law and wind profile, is the subject of continuing work beyond the scope of the current article.…”

“…This is typically accomplished by using the log law (Eq. 1), which is valid in statistically neutral conditions, and approximately in the mean (Kelly and Gryning, 2010;Kelly and Troen, 2016). Furthermore, to relate u * at the prediction site to the (mean) geostrophic wind G, Eq.…”

The Making of the New European Wind Atlas – Part 2: Production and Evaluation

“…(2) the evaluation method assumes neutral stability for vertical extrapolation and makes no stability correction to the wind climate, while the long-term method assumes slightly stable conditions (on land) for vertical extrapolation and makes a stability correction (Kelly and Troen, 2016) during the generalisation step. These two factors should add very small differences between the two methods of evaluation.…”

Statistical prediction of far-field wind-turbine noise, with probabilistic characterization of atmospheric stability

“…Further, its distribution PðL À1 Þ tends to follow a universal form that is effectively scalable from site to site, as found by Kelly and Gryning; 29 this form has been shown to predict the mean profile U(z) at various sites. 40 The wind profile dominates the sound speed profile, and so, the effect of stability on the sound speed profile is through the wind shear, particularly approaching receivers near ground; since we know the range of L À1 values to be expected across (typical) turbine sites in practice, then knowing the form PðL À1 Þ allows us to sample a number of L À1 values and calculate the propagation for each situation. Specifically, we simulate the flow field-including turbine-induced wake-for a representative range of stability cases and calculate the propagation loss for each case, and then, we are able to compute a weighted sum of the case results based on the widths of the unstable (L < 0) and stable (L > 0) sides of the stability distribution.…”