The proposed model for the wind speed deficit in wind farms is analytical and encompasses both small wind farms and wind farms extending over large areas. As is often the need for offshore wind farms, the model handles a regular array geometry with straight rows of wind turbines and equidistant spacing between units in each row and equidistant spacing between rows. Firstly, the case with the flow direction being parallel to rows in a rectangular geometry is considered by defining three flow regimes. Secondly, when the flow is not in line with the main rows, solutions are suggested for the patterns of wind turbine units corresponding to each wind direction. The presentation is an outline of a model complex that will be adjusted and calibrated with measurements in the near future. Copyright © 2006 John Wiley & Sons, Ltd.

Average power losses due to wind turbine wakes are of the order of 10 to 20% of total power output in large offshore wind farms. Accurately quantifying power losses due to wakes is, therefore, an important part of overall wind farm economics. The focus of this research is to compare different types of models from computational fluid dynamics (CFD) to wind farm models in terms of how accurately they represent wake losses when compared with measurements from offshore wind farms. The ultimate objective is to improve modelling of flow for large wind farms in order to optimize wind farm layouts to reduce power losses due to wakes and loads.The research presented is part of the EC-funded UpWind project, which aims to radically improve wind turbine and wind farm models in order to continue to improve the costs of wind energy. Reducing wake losses, or even reduce uncertainties in predicting power losses from wakes, contributes to the overall goal of reduced costs. Here, we assess the state of the art in wake and flow modelling for offshore wind farms, the focus so far has been cases at the Horns Rev wind farm, which indicate that wind farm models require modification to reduce under-prediction of wake losses while CFD models typically over-predict wake losses. Further investigation is underway to determine the causes of these discrepancies.

[1] A comprehensive intercomparison of historical wind speed trends over the contiguous United States is presented based on two observational data sets, four reanalysis data sets, and output from two regional climate models (RCMs). This research thus contributes to detection, quantification, and attribution of temporal trends in wind speeds within the historical/contemporary climate and provides an evaluation of the RCMs being used to develop future wind speed scenarios. Under the assumption that changes in wind climates are partly driven by variability and evolution of the global climate system, such changes should be manifest in direct observations, reanalysis products, and RCMs. However, there are substantial differences in temporal trends derived from observational wind speed data, reanalysis products, and RCMs. The two observational data sets both exhibit an overwhelming dominance of trends toward declining values of the 50th and 90th percentile and annual mean wind speeds, which is also the case for simulations conducted using MM5 with NCEP-2 boundary conditions. However, converse trends are seen in output from the North American Regional Reanalysis, other global reanalyses (NCEP-1 and ERA-40), and the Regional Spectral Model. Equally, the relationship between changing annual mean wind speed and interannual variability is not consistent among the different data sets. NCEP-1 and NARR exhibit some tendency toward declining (increasing) annual mean wind speeds being associated with decreased (increased) interannual variability, but this is not the case for the other data sets considered. Possible causes of the differences in temporal trends from the eight data sources analyzed are provided. Motivation and Objectives[2] Wind speed time series have been subject to far fewer trend analyses than temperature and precipitation records [Gower, 2002;Keimig and Bradley, 2002;McAvaney et al., 2001;McVicar et al., 2008; Tomasin, 1999, 2003;Pryor and Barthelmie, 2003;Tuller, 2004;Brazdil et al., 2009], in part because of data homogeneity issues [Thomas et al., 2008;Tuller, 2004;DeGaetano, 1998]. However, understanding how evolution of the global climate system has been manifest as changes in near-surface wind regimes in the past and how near-surface wind speed regimes might alter in the future is of great relevance to the insurance industry [Changnon et al., 1999;Thornes, 1991], the construction and maritime industries [Ambrose and Vergun, 1997;Caires and Sterl, 2005;Caires et al., 2006], surface energy balance estimation [Rayner, 2007], the community charged with mitigating coastal erosion [Bijl, 1997;Viles and Goudie, 2003], the agricultural industry [O'Neal et al., 2005], forest and infrastructure protection communities [Jungo et al., 2002], and the burgeoning wind energy industry [Pryor et al., 2006b]. With respect to the latter, it is worth noting that during 2005-2008 over 18,000 MW of wind energy developments came online in the continental United States, increasing installed capacity to over 25 GW (AWEA wind ene...

. (2012). The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev wind farm. Wind Energy, 15(1), 183-196. DOI: 10.1002/we.512 The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev wind farm Moreover, there is a strong stability directional dependence with southerly winds having fewer unstable conditions while northerly winds have fewer observations in the stable classes.Stable conditions also tend to be associated with lower levels of turbulence intensity and this relationship persists as wind speeds increase. Power deficit is a function of ambient turbulence intensity. The level of power deficit is strongly dependent on the wind turbine spacing and as turbulence intensity increases the power deficit decreases. The power deficit is determined for four different wind turbine spacing distances and for stability classified as very stable, unstable and other (near-neutral to very unstable). The more stable conditions are, the larger the power deficit.

There is an urgent need to develop and optimize tools for designing large wind farm arrays for deployment offshore. This research is focused on improving the understanding of, and modeling of, wind turbine wakes in order to make more accurate power output predictions for large offshore wind farms. Detailed data ensembles of power losses due to wakes at the large wind farms at Nysted and Horns Rev are presented and analyzed. Differences in turbine spacing (10.5 versus 7 rotor diameters) are not differentiable in wake-related power losses from the two wind farms. This is partly due to the high variability in the data despite careful data screening. A number of ensemble averages are simulated with a range of wind farm and computational fluid dynamics models and compared to observed wake losses. All models were able to capture wake width to some degree, and some models also captured the decrease of power output moving through the wind farm. Root-mean-square errors indicate a generally better model performance for higher wind speeds (10 rather than 6 m s−1) and for direct down the row flow than for oblique angles. Despite this progress, wake modeling of large wind farms is still subject to an unacceptably high degree of uncertainty.