Atmospheric stability‐dependent infinite wind‐farm models and the wake‐decay coefficient

We extend the infinite wind‐farm boundary‐layer (IWFBL) model of Frandsen to take into account atmospheric static stability effects. This extended model is compared with the IWFBL model of Emeis and to the Park wake model used in Wind Atlas Analysis and Application Program (WAsP), which is computed for an infinite wind farm. The models show similar behavior for the wind‐speed reduction when accounting for a number of surface roughness lengths, turbine to turbine separations and wind speeds under neutral conditions. For a wide range of atmospheric stability and surface roughness length values, the extended IWFBL model of Frandsen shows a much higher wind‐speed reduction dependency on atmospheric stability than on roughness length (roughness has been generally thought to have a major effect on the wind‐speed reduction). We further adjust the wake‐decay coefficient of the Park wake model for an infinite wind farm to match the wind‐speed reduction estimated by the extended IWFBL model of Frandsen for different roughness lengths, turbine to turbine separations and atmospheric stability conditions. It is found that the WAsP‐recommended values for the wake‐decay coefficient of the Park wake model are (i) larger than the adjusted values for a wide range of neutral to stable atmospheric stability conditions, a number of roughness lengths and turbine separations lower than ～ 10 rotor diameters and (ii) too large compared with those obtained by a semiempirical formulation (relating the ratio of the friction to the hub‐height free velocity) for all types of roughness and atmospheric stability conditions. © 2013 The Authors. Wind Energy published by John Wiley & Sons, Ltd.     

Time‐adaptive wind turbine model for an LES framework

Most large‐eddy simulation studies related to wind energy have been carried out either by using a fixed pressure gradient to ensure that mean wind direction is perpendicular to the wind turbine rotor disk or by forcing the flow with a geostrophic wind and timely readjusting the turbines' orientation. This has not allowed for the study of wind farm characteristics with a time‐varying wind vector. In this paper, a new time‐adaptive wind turbine model for the large‐eddy simulation framework is introduced. The new algorithm enables the wind turbines to dynamically realign with the incoming wind vector and self‐adjust the yaw orientation with the incoming wind vector similar to real wind turbines. The performance of the new model is tested first with a neutrally stratified atmospheric flow forced with a time‐varying geostrophic wind vector. A posteriori, the new model is used to further explore the interaction between a synthetic time‐changing thermal atmospheric boundary layer and an embedded wind farm. Results show that there is significant potential power to be harvested during the unstable time periods at the cost of designing wind turbines capable of adapting to the enhanced variance of these periods. Stable periods provide less power but are more constant over time with an enhanced lateral shear induced by an increased change in wind direction with height. Copyright © 2015 John Wiley & Sons, Ltd.     

Real‐time wind field reconstruction from LiDAR measurements using a dynamic wind model and state estimation

The use of light detection and ranging (LiDAR) instruments offer many potential benefits to the wind energy industry. Although much effort has been invested in developing such instruments, the fact remains that they provide limited spatio‐temporal velocity measurements of the wind field. Moreover, LiDAR measurements only provide the radial (line‐of‐sight) velocity component of the wind, making it difficult to precisely determine wind magnitude and direction, owing to the so‐called ‘cyclops’ dilemma. Motivated by a desire to extract more information from typical LiDAR data, this paper aims to show that it is possible to accurately estimate, in a real‐time fashion, the radial and tangential velocity components of the wind field. We show how such reconstructions can be generated through the synthesis of an unscented Kalman filter that employs a low‐order dynamic model of the wind to estimate the unmeasured velocities within the wind field, using repeated measurement updates from typical nacelle‐mounted LiDAR instruments. This approach is validated upon synthetic data generated from large eddy simulations of the atmospheric boundary layer. The accuracy of the wind field estimates are validated across a variety of beam configurations, look directions, atmospheric stabilities and imperfect measurement conditions. The main outcome of this paper is a technique that offers the potential to accurately reconstruct wind fields from LiDAR data, overcoming the cyclops dilemma in the process. The ultimate aim of this research is to provide reliable gust detection warning systems to offshore construction workers, in addition to accurate wind field estimates for use in preview turbine pitch control systems. © 2014 The Authors. Wind Energy published by John Wiley & Sons Ltd.     

A spinner‐integrated wind lidar for enhanced wind turbine control

A field test with a continuous wave wind lidar (ZephIR) installed in the rotating spinner of a wind turbine for unimpeded preview measurements of the upwind approaching wind conditions is described. The experimental setup with the wind lidar on the tip of the rotating spinner of a large 80 m rotor diameter, 59 m hub height 2.3 MW wind turbine (Vestas NM80), located at Tjæreborg Enge in western Denmark is presented. Preview wind data at two selected upwind measurement distances, acquired during two measurement periods of different wind speed and atmospheric stability conditions, are analyzed. The lidar‐measured speed, shear and direction of the wind field previewed in front of the turbine are compared with reference measurements from an adjacent met mast and also with the speed and direction measurements on top of the nacelle behind the rotor plane used by the wind turbine itself. Yaw alignment of the wind turbine based on the spinner lidar measurements is compared with wind direction measurements from both the nearby reference met mast and the turbine's own yaw alignment wind vane. Furthermore, the ability to detect vertical wind shear and vertical direction veer in the inflow, through the analysis of the spinner lidar data, is investigated. Finally, the potential for enhancing turbine control and performance based on wind lidar preview measurements in combination with feed‐forward enabled turbine controllers is discussed. Copyright © 2012 John Wiley & Sons, Ltd.     

Prediction of wind‐turbine fatigue loads in forest regions based on turbulent LES inflow fields

Large‐eddy simulations (LES) were used to predict the neutral atmospheric boundary layer over a sparse and a dense forest, as well as over grass‐covered flat terrain. The forest is explicitly represented in the simulations through momentum sink terms. Turbulence data extracted from the LES served then as inflow turbulence for the simulation of the dynamic structural response of a generic wind turbine. In this way, the impact of forest density, wind speed and wind‐turbine hub height on the wind‐turbine fatigue loads was studied. Results show for example significantly increased equivalent fatigue loads above the two forests. Moreover, a comparison between LES turbulence and synthetically generated turbulence in terms of load predictions was made and revealed that synthetic turbulence was able to excite the same spectral peaks as LES turbulence but lead to consistently lower equivalent fatigue loads. Copyright © 2016 John Wiley & Sons, Ltd.     

One‐way mesoscale‐microscale coupling for simulating a wind farm in North Texas: Assessment against SCADA and LiDAR data

One‐way nested mesoscale to microscale simulations of an onshore wind farm have been performed nesting the Weather Research and Forecasting (WRF) model and our in‐house high‐resolution large‐eddy simulation code (UTD‐WF). Each simulation contains five nested WRF domains, with the largest domain spanning the north Texas Panhandle region with a 4 km resolution, while the highest resolution (50 m) nest simulates microscale wind fluctuations and turbine wakes within a single wind farm. The finest WRF domain in turn drives the UTD‐WF LES higher‐resolution domain for a subset of six turbines at a resolution of ～5 m. The wind speed, direction, and boundary layer profiles from WRF are compared against measurements obtained with a met‐tower and a scanning Doppler wind LiDAR located within the wind farm. Additionally, power production obtained from WRF and UTD‐WF are assessed against supervisory control and data acquisition (SCADA) system data. Numerical results agree well with the experimental measurements of the wind speed, direction, and power production of the turbines. UTD‐WF high‐resolution domain improves significantly the agreement of the turbulence intensity at the turbines location compared with that of WRF. Velocity spectra have been computed to assess how the nesting allows resolving a wide range of scales at a reasonable computational cost. A domain sensitivity analysis has been performed. Velocity spectra indicate that placing the inlet too close to the first row of turbines results in an unrealistic peak of energy at the rotational frequency of the turbines. Spectra of the power production of a single turbine and of the cumulative power of the array have been compared with analytical models.     

De‐trending of wind speed variance based on first‐order and second‐order statistical moments only

The lack of efficient methods for de‐trending of wind speed resource data may lead to erroneous wind turbine fatigue and ultimate load predictions. The present paper presents two models, which quantify the effect of an assumed linear trend on wind speed standard deviations as based on available statistical data only. The first model is a pure time series analysis approach, which quantifies the effect of non‐stationary characteristics of ensemble mean wind speeds on the estimated wind speed standard deviations as based on mean wind speed statistics only. This model is applicable to statistics of arbitrary types of time series. The second model uses the full set of information and includes thus additionally observed wind speed standard deviations to estimate the effect of ensemble mean non‐stationarities on wind speed standard deviations. This model takes advantage of a simple physical relationship between first‐order and second‐order statistical moments of wind speeds in the atmospheric boundary layer and is therefore dedicated to wind speed time series but is not applicable to time series in general. The capabilities of the proposed models are discussed by comparing model predictions with conventionally de‐trended characteristics of measured wind speeds using data where high sampled time series are available, and a traditional de‐trending procedure therefore can be applied. This analysis shows that the second model performs significantly better than the first model, and thus in turn that the model constraint, introduced by the physical link between the first and second statistical moments, proves very efficient in the present context. © 2013 The Authors. Wind Energy Published by John Wiley & Sons Ltd.     

Estimation of variability and predictability of large‐scale wind energy in The Netherlands

This paper presents a data‐driven approach for estimating the degree of variability and predictability associated with large‐scale wind energy production for a planned integration in a given geographical area, with an application to The Netherlands. A new method is presented for generating realistic time series of aggregated wind power realizations and forecasts. To this end, simultaneous wind speed time series—both actual and predicted—at planned wind farm locations are needed, but not always available. A 1‐year data set of 10‐min averaged wind speeds measured at several weather stations is used. The measurements are first transformed from sensor height to hub height, then spatially interpolated using multivariate normal theory, and finally averaged over the market resolution time interval. Day‐ahead wind speed forecast time series are created from the atmospheric model HiRLAM (High Resolution Limited Area Model). Actual and forecasted wind speeds are passed through multi‐turbine power curves and summed up to create time series of actual and forecasted wind power. Two insights are derived from the developed data set: the degree of long‐term variability and the degree of predictability when Dutch wind energy production is aggregated at the national or at the market participant level. For a 7.8 GW installed wind power scenario, at the system level, the imbalance energy requirements due to wind variations across 15‐min intervals are ±14% of the total installed capacity, while the imbalance due to forecast errors vary between 53% for down‐ and 56% for up‐regulation. When aggregating at the market participant level, the balancing energy requirements are 2–3% higher. Copyright © 2008 John Wiley & Sons, Ltd.     

Uncertainty of power curve measurement with a two‐beam nacelle‐mounted lidar

Nacelle lidars are attractive for offshore measurements since they can provide measurements of the free wind speed in front of the turbine rotor without erecting a met mast, which significantly reduces the cost of the measurements. Nacelle‐mounted pulsed lidars with two lines of sight (LOS) have already been demonstrated to be suitable for use in power performance measurements. To be considered as a professional tool, however, power curve measurements performed using these instruments require traceable calibrated measurements and the quantification of the wind speed measurement uncertainty. Here we present and demonstrate a procedure fulfilling these needs. A nacelle lidar went through a comprehensive calibration procedure. This calibration took place in two stages. First with the lidar on the ground, the tilt and roll readings of the inclinometers in the nacelle lidar were calibrated. Then the lidar was installed on a 9m high platform in order to calibrate the wind speed measurement. The lidar's radial wind speed measurement along each LOS was compared with the wind speed measured by a calibrated cup anemometer, projected along the LOS direction. The various sources of uncertainty in the lidar wind speed measurement have been thoroughly determined: uncertainty of the reference anemometer, the horizontal and vertical positioning of the beam, the lack of homogeneity of the flow within the probe volume, lidar measurement mean deviation and standard uncertainty. The resulting uncertainty lies between 1 and 2% for the wind speed range between cut‐in and rated wind speed. Finally, the lidar was mounted on the nacelle of a wind turbine in order to perform a power curve measurement. The wind speed was simultaneously measured with a mast‐top mounted cup anemometer placed two rotor diameters upwind of the turbine. The wind speed uncertainty related to the lidar tilting was calculated based on the tilt angle uncertainty derived from the inclinometer calibration and the deviation of the measurement height from hub height. The resulting combined uncertainty in the power curve using the nacelle lidar was less than 10% larger on average than that obtained with the mast mounted cup anemometer. Copyright © 2015 John Wiley & Sons, Ltd.     

Large‐eddy simulation of stable boundary layer turbulence and estimation of associated wind turbine loads

Stochastic simulation of turbulent inflow fields commonly used in wind turbine load computations is unable to account for contrasting states of atmospheric stability. Flow fields in the stable boundary layer, for instance, have characteristics such as enhanced wind speed and directional shear; these effects can influence loads on utility‐scale wind turbines. To investigate these influences, we use large‐eddy simulation (LES) to generate an extensive database of high‐resolution ( ～ 10 m), four‐dimensional turbulent flow fields. Key atmospheric conditions (e.g., geostrophic wind) and surface conditions (e.g., aerodynamic roughness length) are systematically varied to generate a diverse range of physically realizable atmospheric stabilities. We show that turbine‐scale variables (e.g., hub height wind speed, standard deviation of the longitudinal wind speed, wind speed shear, wind directional shear and Richardson number) are strongly interrelated. Thus, we strongly advocate that these variables should not be prescribed as independent degrees of freedom in any synthetic turbulent inflow generator but rather that any turbulence generation procedure should be able to bring about realistic sets of such physically realizable sets of turbine‐scale flow variables. We demonstrate the utility of our LES‐generated database in estimation of loads on a 5‐MW wind turbine model. More importantly, we identify specific turbine‐scale flow variables that are responsible for large turbine loads—e.g., wind speed shear is found to have a greater influence on out‐of‐plane blade bending moments for the turbine studied compared with its influence on other loads such as the tower‐top yaw moment and the fore‐aft tower base moment. Overall, our study suggests that LES may be effectively used to model inflow fields, to study characteristics of flow fields under various atmospheric stability conditions and to assess turbine loads for conditions that are not typically examined in design standards. Copyright © 2013 John Wiley & Sons, Ltd.     

Spatio‐temporal analysis and modeling of short‐term wind power forecast errors

Forecasts of wind power production are increasingly being used in various management tasks. So far, such forecasts and related uncertainty information have usually been generated individually for a given site of interest (either a wind farm or a group of wind farms), without properly accounting for the spatio‐temporal dependencies observed in the wind generation field. However, it is intuitively expected that, owing to the inertia of meteorological forecasting systems, a forecast error made at a given point in space and time will be related to forecast errors at other points in space in the following period. The existence of such underlying correlation patterns is demonstrated and analyzed in this paper, considering the case‐study of western Denmark. The effects of prevailing wind speed and direction on autocorrelation and cross‐correlation patterns are thoroughly described. For a flat terrain region of small size like western Denmark, significant correlation between the various zones is observed for time delays up to 5 h. Wind direction is shown to play a crucial role, while the effect of wind speed is more complex. Nonlinear models permitting capture of the interdependence structure of wind power forecast errors are proposed, and their ability to mimic this structure is discussed. The best performing model is shown to explain 54% of the variations of the forecast errors observed for the individual forecasts used today. Even though focus is on 1‐h‐ahead forecast errors and on western Denmark only, the methodology proposed may be similarly tested on the cases of further look‐ahead times, larger areas, or more complex topographies. Such generalization may not be straightforward. While the results presented here comprise a first step only, the revealed error propagation principles may be seen as a basis for future related work. Copyright © 2010 John Wiley & Sons, Ltd.     

Analytical model for predicting the performance of arbitrary size and layout wind farms

A simple engineering model for predicting wind farm performance is presented, which is applicable to wind farms of arbitrary size and turbine layout. For modeling the interaction of wind farm with the atmospheric boundary layer (ABL), the wind farm is represented as added roughness elements. The wind speed behind each turbine is calculated using a kinematic model, in which the friction velocity and the wind speed outside the turbine wake, constructed based on the wind farm‐ABL interaction model, are employed to estimate the wake expansion rate in the crosswind direction and the maximum wind speed that can be recovered within the turbine wake, respectively. Validation of the model is carried out by comparing the model predictions with the measurements from wind tunnel experiments and the Horns Rev wind farm. For all validation cases, satisfactory agreement is obtained between model predictions and experimental data. Copyright © 2015 John Wiley & Sons, Ltd.     

Investigation of boundary‐layer wind predictions during nocturnal low‐level jet events using the Weather Research and Forecasting model

The accuracy of boundary‐layer wind profiles occurring during nocturnal low‐level jet (LLJ) events, and their sensitivities to variations of user‐specifiable model configuration parameters within the Weather Research and Forecasting model, was investigated. Simulations were compared against data from a wind‐profiling lidar, deployed to the Northern Great Plains during the U.S. Department of Energy‐supported Weather Forecast Improvement Project. Two periods during the autumn of 2011 featuring LLJs of similar magnitudes and durations occurring during several consecutive nights were selected for analysis. Simulated wind speed and direction at 80 and 180 m above the surface, the former a typical wind turbine hub height, bulk vertical gradients between 40 and 120 m, a typical rotor span, and the maximum wind speeds occurring at 80 and 180 m, and their times of occurrence, were compared with the observations. Sensitivities of these parameters to the horizontal and vertical grid spacing, planetary boundary layer and land surface model physics options, and atmospheric forcing dataset, were assessed using ensembles encompassing changes of each of these configuration parameters. Each simulation captured the diurnal cycle of wind speed and stratification, producing LLJs during each overnight period; however, large discrepancies in relation to the observations were frequently observed, with each ensemble producing a wide range of distributions, reflecting highly variable representations of stratification during the weakly stable overnight conditions. Root mean square error and bias values computed over the LLJ cycle (late evening through the following morning) revealed that, while some configurations performed better or worse in different aspects and at different times, none exhibited definitively superior performance. The considerable root mean square error and bias values, even among the ‘best’ performing simulations, underscore the need for improved simulation capabilities for the prediction of near‐surface winds during LLJ conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

Nacelle anemometer measurement‐based extremum‐seeking wind turbine region‐2 control for improved convergence in fluctuating wind

For region‐2 operation of wind turbines in practice, the optimal torque gain can deviate from the nominal value because of the variations in turbine and wind conditions. The extremum‐seeking control (ESC) has shown its potential as a model‐free region‐2 control solution in some recent work; however, the ESC with rotor power feedback suffers from undesirable convergence under fluctuating wind. In this paper, we propose to use an estimated power coefficient as the objective function for the torque‐gain ESC, where the hub‐height free‐stream wind speed (FSWS) is estimated with the nacelle anemometer measurement on the basis of the so‐called nacelle transfer function (NTF) between the nacelle anemometer and met‐tower measurement. A sensitivity analysis is performed to quantify the impact of the wind speed estimation error on the estimation of power coefficient. An ESC integrated interregion switching scheme is proposed to avoid the load increase. Simulation results show that, compared with the power feedback‐based ESC, the proposed method can greatly improve the convergence rate of ESC under fluctuating wind, even under relatively large wind speed estimation error. Evaluation for the fatigue loads of wind turbine shows that the proposed control strategy induces mild increase of the wind turbine load.     

Three‐dimensional viscous‐inviscid coupling method for wind turbine computations

In this paper, a computational model for predicting the aerodynamic behavior of wind turbine wakes and blades subjected to unsteady motions and viscous effects is presented. The model is based on a three‐dimensional panel method using a surface distribution of quadrilateral sources and doublets, which is coupled to a viscous boundary layer solver. Unlike Navier‐Stokes codes that need to solve the entire flow domain, the panel method solves the flow around a complex geometry by distributing singularity elements on the body surface, obtaining a faster solution and making this type of codes suitable for the design of wind turbines. A free‐wake model has been employed to simulate the wake behind a wind turbine by using vortex filaments that carry the vorticity shed by the trailing edge of the blades. Viscous and rotational effects inside the boundary layer are taken into account via the transpiration velocity concept, applied using strip theory with the cross sectional angle of attack as coupling parameter. The transpiration velocity is obtained from the solution of the integral boundary layer equations with extension for rotational effects. It is found that viscosity plays a very important role in the predictions of blade aerodynamics and wake dynamics, especially at high angles of attack just before and after boundary layer separation takes place. The present code is validated in detail against the well‐known MEXICO experiment and a set of non‐rotating cases. Copyright © 2014 John Wiley & Sons, Ltd.     

Short‐term spatio‐temporal prediction of wind speed and direction

This paper aims to produce a low‐complexity predictor for the hourly mean wind speed and direction from 1 to 6 h ahead at multiple sites distributed around the UK. The wind speed and direction are modelled via the magnitude and phase of a complex‐valued time series. A multichannel adaptive filter is set to predict this signal on the basis of its past values and the spatio‐temporal correlation between wind signals measured at numerous geographical locations. The filter coefficients are determined by minimizing the mean square prediction error. To account for the time‐varying nature of the wind data and the underlying system, we propose a cyclo‐stationary Wiener solution, which is shown to produce an accurate predictor. An iterative solution, which provides lower computational complexity, increased robustness towards ill‐conditioning of the data covariance matrices and the ability to track time‐variations in the underlying system, is also presented. The approaches are tested on wind speed and direction data measured at various sites across the UK. Results show that the proposed techniques are able to predict wind speed as accurately as state‐of‐the‐art wind speed forecasting benchmarks while simultaneously providing valuable directional information. Copyright © 2013 John Wiley & Sons, Ltd.     

Impact of different forest densities on atmospheric boundary‐layer development and wind‐turbine wake

The aim of this work is to investigate the atmospheric boundary‐layer (ABL) flow and the wind turbine wake over forests with varying leaf area densities (LAD). The forest LAD profile used in this study is based on a real forest site, Ryningsnäs, located in Sweden. The reference turbine used to model the wake is a well‐documented 5‐MW turbine, which is implemented in the simulations using an actuator line model (ALM). All simulations are carried out with openFOAM using the Reynolds averaged Navier‐Stokes (RANS) approach. Twelve forest cases with leaf area index (LAI) ranging from 0.42 to 8.5 are considered. Results show that the mean velocity decreases with increasing LAI within the forest canopy, but increases with LAI above the hub height. Meanwhile, the turbulent kinetic energy (TKE) varies nonmonotonically with forest density. The TKE increases with forest density and reaches to its maximum at an average LAI of 1.70, afterwards, it decreases gradually as the density increases. It is also observed that the forest density has a clear role in the wake development and recovery. Comparisons between no‐forest and forest cases show that the forest characteristics help in damping the added turbulence from the turbine. As a consequence, the forest with the highest upstream turbulence has the shortest wake downstream of the turbine.     

Variable‐sized wind turbines are a possibility for wind farm optimization

The potential benefits associated with harnessing available momentum and reducing turbulence levels in a wind farm composed of wind turbines of alternating size are investigated through wind tunnel experiments. A variable size turbine array composed of 3 by 8 model wind turbines is placed in a boundary layer flow developed over both a smooth and rough surfaces under neutrally stratified thermal conditions. Cross‐wire anemometry is used to capture high resolution and simultaneous measurements of the streamwise and vertical velocity components at various locations along the central plane of the wind farm. A laser tachometer is employed to obtain the instantaneous angular velocity of various turbines. The results suggest that wind turbine size heterogeneity in a wind farm introduces distinctive flow interactions not possible in its homogeneous counterpart. In particular, reduced levels of turbulence around the wind turbine rotors may have positive effects on turbulent loading. The turbines also appear to perform quite uniformly along the entire wind farm, whereas surface roughness impacts the velocity recovery and the spectral content of the turbulent flow within the wind farm. Copyright © 2013 John Wiley & Sons, Ltd.     

Observability of the ambient conditions in model‐based estimation for wind farm control: A focus on static models

Wind farm control (WFC) algorithms rely on an estimate of the ambient wind speed, wind direction, and turbulence intensity in the determination of the optimal control setpoints. However, the measurements available in a commercial wind farm do not always carry sufficient information to estimate these atmospheric quantities. In this paper, a novel measure (“observability”) is introduced that quantifies how well the ambient conditions can be estimated with the measurements at hand through a model inversion approach. The usefulness of this measure is shown through several case studies. While the turbine power signals and the inter‐turbine wake interactions provide information on the wind direction, the case studies presented in this article show that there is a strong need for wind direction measurements for WFC to sufficiently cover observability for any ambient condition. Further, generally, more wake interaction leads to a higher observability. Also, the mathematical framework presented in this article supports the straightforward notion that turbine power measurements provide no additional information compared with local wind speed measurements, implying that power measurements are superfluous. Irregular farm layouts result in a higher observability due to the increase in unique wake interaction. The findings in this paper may be used in WFC to predict which ambient quantities can (theoretically) be estimated. The authors envision that this will assist in the estimation of the ambient conditions in WFC algorithms and can lead to an improvement in the performance of WFC algorithms over the complete envelope of wind farm operation.     

Estimation of offshore extreme wind from wind‐wave coupled modeling

A coupledwind‐wave modeling system is used to simulate 23 years of storms and estimate offshore extreme wind statistics. In this system, the atmospheric Weather Research and Forecasting (WRF) model and Spectral Wave model for Near shore (SWAN) are coupled, through a wave boundary layer model (WBLM) that is implemented in SWAN. The WBLM calculates momentum and turbulence kinetic energy budgets, using them to transfer wave‐induced stress to the atmospheric modeling. While such coupling has a trivial impact on the wind modeling for 10‐m wind speeds less than 20 ms^?1, the effect becomes appreciable for stronger winds—both compared with uncoupled WRF modeling and with standard parameterization schemes for roughness length. The coupled modeling output is shown to be satisfactory compared with measurements, in terms of the distribution of surface‐drag coefficient with wind speed. The coupling is also shown to be important for estimation of extreme winds offshore, where the WBLM‐coupled results match observations better than results from noncoupled modeling, as supported by measurements from a number of stations.