Evaluating winds and vertical wind shear from Weather Research and Forecasting model forecasts using seven planetary boundary layer schemes

The existence of vertical wind shear in the atmosphere close to the ground requires that wind resource assessment and prediction with numerical weather prediction (NWP) models use wind forecasts at levels within the full rotor span of modern large wind turbines. The performance of NWP models regarding wind energy at these levels partly depends on the formulation and implementation of planetary boundary layer (PBL) parameterizations in these models. This study evaluates wind speeds and vertical wind shears simulated by the Weather Research and Forecasting model using seven sets of simulations with different PBL parameterizations at one coastal site over western Denmark. The evaluation focuses on determining which PBL parameterization performs best for wind energy forecasting, and presenting a validation methodology that takes into account wind speed at different heights. Winds speeds at heights ranging from 10 to 160 m, wind shears, temperatures and surface turbulent fluxes from seven sets of hindcasts are evaluated against observations at Høvsøre, Denmark. The ability of these hindcast sets to simulate mean wind speeds, wind shear, and their time variability strongly depends on atmospheric static stability. Wind speed hindcasts using the Yonsei University PBL scheme compared best with observations during unstable atmospheric conditions, whereas the Asymmetric Convective Model version 2 PBL scheme did so during near‐stable and neutral conditions, and the Mellor–Yamada–Janjic PBL scheme prevailed during stable and very stable conditions. The evaluation of the simulated wind speed errors and how these vary with height clearly indicates that for wind power forecasting and wind resource assessment, validation against 10 m wind speeds alone is not sufficient. Copyright © 2012 John Wiley & Sons, Ltd.     

Validation of boundary layer parameterization schemes in the Weather Research and Forecasting (WRF) model under the aspect of offshore wind energy applications—part II: boundary layer height and atmospheric stability

Five different planetary boundary layer (PBL) schemes in the Weather Research and Forecasting (WRF) model have been tested with respect to their capability to model boundary layer parameters relevant for offshore wind deployments. For the year 2005, model simulations based on the YSU, ACM2, QNSE, MYJ and MYNN2 PBL schemes with WRF have been performed for the North Sea and validated against measurements from the FINO1 platform. In part I, the investigations had focused on the key parameters 100 m mean wind speed and wind shear in terms of the power‐law exponent. In part II, the focus is now set on the capability of the model to represent height and stability of the marine atmospheric boundary layer.Considerable differences are found among the PBL schemes in predicting the PBL height. A substantial part of this variation is explained by the use of different PBL‐height definitions in the schemes. The use of a standardized procedure in calculating the PBL height from common WRF output parameters, in particular the temperature gradient and the wind shear, leads to reduced differences between the different schemes and a closer correspondence with the FINO1 measurements. The study also reveals a very clear seasonal dependency of the atmospheric stability over Southern North Sea. During winter time, the marine atmospheric boundary layer is more or less neutral with several episodes of unstable periods. During spring and early summer, the occurrence of periods with very stable stratification becomes dominant with stable conditions up to 40–45% of the time when warm continental air is advected from the South. In general, the results of part II confirm again that the MYJ scheme performs slightly better than the others and can therefore be suggested as first choice for marine atmospheric boundary layer simulations without a priori information of atmospheric stability in the region of interest. Copyright © 2014 John Wiley & Sons, Ltd.     

Improving wind speed forecasts from the Weather Research and Forecasting model at a wind farm in the semiarid Coquimbo region in central Chile

Accurate predictions of the wind field are key for better wind power forecasts. Wind speed forecasts from numerical weather models present differences with observations, especially in places with complex topography, such as the north of Chile. The present study has two goals: (a) to find the WRF model boundary layer (PBL) scheme that best reproduces the observations at the Totoral Wind Farm, located in the semiarid Coquimbo region in north‐central Chile, and (b) to use an artificial neural network (ANN) to postprocess wind speed forecasts from different model domains to analyze the sensitivity to horizontal resolution. The WRF model was run with three different PBL schemes (MYNN, MYNN3, and QNSE) for 2013. The WRF simulation with the QNSE scheme showed the best agreement with observations at the wind farm, and its outputs were postprocessed using two ANNs with two algorithms: backpropagation (BP) and particle swarm optimization (PSO). These two ANNs were applied to the innermost WRF domains with 3‐km (d03) and 1‐km (d04) horizontal resolutions. The root‐mean‐square errors (RMSEs) between raw WRF forecasts and observations for d03 and d04 were 2.7 and 2.4 ms^?1 , respectively. When both ANN models (BP and PSO) were applied to Domains d03 and d04, the RMSE decreased to values lower than 1.7 ms^?1 , and they showed similar performances, supporting the use of an ANN to postprocess a three‐nested WRF domain configuration to provide more accurate forecasts in advance for the region.     

Hybridizing the fifth generation mesoscale model with artificial neural networks for short-term wind speed prediction

This paper presents the hybridization of the fifth generation mesoscale model (MM5) with neural networks in order to tackle a problem of short-term wind speed prediction. The mean hourly wind speed forecast at wind turbines in a wind park is an important parameter used to predict the total power production of the park. Our model for short-term wind speed forecast integrates a global numerical weather prediction model and observations at different heights (using atmospheric soundings) as initial and boundary conditions for the MM5 model. Then, the outputs of this model are processed using a neural network to obtain the wind speed forecast in specific points of the wind park. In the experiments carried out, we present some results of wind speed forecasting in a wind park located at the south-east of Spain. The results are encouraging, and show that our hybrid MM5-neural network approach is able to obtain good short-term predictions of wind speed at specific points.     

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.     

Wind forecasts for wind power generation using the Eta model

The goal of this article is to apply the regional atmospheric numerical weather prediction Eta model and describe its performance in validation of the wind forecasts for wind power plants. Wind power generation depends on wind speed. Wind speed is converted into power through characteristic curve of a wind turbine. The forecasting of wind speed and wind power has the same principle.Two sets of Eta model forecasts are made: one with a coarse resolution of 22 km, and another with a nested grid of 3.5 km, centered on the Nasudden power plants, (18.22°E, 57.07°N; 3 m) at island Gotland, Sweden. The coarse resolution forecasts were used for the boundary conditions of the nested runs. Verification is made for the nested grid model, for summers of 1996–1999, with a total number of 19 536 pairs of forecast and observed winds. The Eta model is compared against the wind observed at the nearest surface station and against the wind turbine tower 10 m wind. As a separate effort, the Eta model wind is compared against the wind from tower observations at a number of levels (38, 54, 75 and 96 m).Four common measures of accuracy relative to observations - mean difference (bias), mean absolute difference, root mean square difference and correlation coefficient are evaluated. In addition, scatter plots of the observed and predicted pairs at 10 and 96 m are generated. Average overall results of the Eta model 10 m wind fits to tower observations are: mean difference (bias) of 0.48 m/s, mean absolute difference of 1.14 m/s, root mean square difference of 1.38 m/s, and the correlation coefficient of 0.79. Average values for the upper tower observation levels are the mean difference (bias) of 0.40 m/s; mean absolute difference of 1.46 m/s; root mean square difference of 1.84 m/s and the correlation coefficient of 0.80.     

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.     

Limitations and breakdown of Monin–Obukhov similarity theory for wind profile extrapolation under stable stratification

The intent of this study is to investigate the limitations of the Monin–Obukhov similarity theory (MOST) for wind profile extrapolation—particularly its breakdown in stable stratification—and to explore several modifications intended to circumvent aspects of this breakdown. Using 10years of 10min averaged data from the 213m Cabauw meteorological tower in the Netherlands, we first demonstrate the sensitivity of the logarithmic wind speed model to highly uncertain estimates of the roughness length, z₀, and the associated limitations of applying the model in horizontally inhomogeneous conditions. We then demonstrate that these limitations can be mitigated by avoiding the use of z₀ in the logarithmic wind speed model. Rather, by using a lower boundary above z₀ (e.g. 10m) and a ‘bulk’ Obukhov length measured between two near‐surface altitudes, substantial improvements in wind speed extrapolation accuracy are found. Next, we demonstrate the limitations in applying the logarithmic wind speed model above the surface layer (SL), specifically the divergence of different forms of the MOST stability function, the role of the Coriolis force and the decoupling of surface winds from those aloft. Finally, we explore similarity‐based modifications to the logarithmic wind speed model that are intended to improve its accuracy above the SL, but we find that such modifications cannot circumvent the limitations described earlier. Given that modern hub heights and altitudes swept out by a wind turbine blade extend well beyond the range of applicability of MOST under conditions of stable stratification, new extrapolation models are required that are more applicable at these altitudes. Copyright © 2015 John Wiley & Sons, Ltd.     

Assessing atmospheric stability and its impacts on rotor‐disk wind characteristics at an onshore wind farm

As the average hub height and blade diameter of new wind turbine installations continue to increase, turbines typically encounter higher wind speeds, which enable them to extract large amounts of energy, but they also face challenges due to the complex nature of wind flow and turbulence in the planetary boundary layer (PBL). Wind speed and turbulence can vary greatly across a turbine's rotor disk; this variability is partially due to whether the PBL is stable, neutral or convective. To assess the influence of stability on these wind characteristics, we utilize a unique data set including observations from two meteorological towers, a surface flux tower and high‐resolution remote‐sensing sound detection and ranging (SODAR) instrument. We compare several approaches to defining atmospheric stability to the Obukhov length (L). Typical wind farm observations only allow for the calculation of a wind shear exponent (α) or horizontal turbulence intensity (I_U) from cup anemometers, whereas SODAR gives measurements at multiple heights in the rotor disk of turbulence intensity (I) in the latitudinal (I_u), longitudinal (I_v) and vertical (I_w) directions and turbulence kinetic energy (TKE). Two methods for calculating horizontal Ifrom SODAR data are discussed. SODAR stability parameters are in high agreement with the more physically robust L,with TKE exhibiting the best agreement, and show promise for accurate characterizations of stability. Vertical profiles of wind speed and turbulence, which likely affect turbine power performance, are highly correlated with stability regime. At this wind farm, disregarding stability leads to over‐assessments of the wind resource during convective conditions and under‐assessments during stable conditions. Copyright © 2011 John Wiley & Sons, Ltd.     

Investigation on wind power potential on Hong Kong islands—an analysis of wind power and wind turbine characteristics

This paper discusses the potential for electricity generation on Hong Kong islands through an analysis of the local weather data and typical wind turbine characteristics. An optimum wind speed, u_op, is proposed to choose an optimal type of wind turbine for different weather conditions. A simulation model has been established to describe the characteristics of a particular wind turbine. A case study investigation allows wind speed and wind power density to be obtained using different hub heights, and the annual power generated by the wind turbine to be simulated. The wind turbine's capacity factor, being the ratio of actual annual power generation to the rated annual power generation, is shown to be 0.353, with the capacity factor in October as high as 0.50. The simulation shows the potential for wind power generation on the islands surrounding Hong Kong.     

Computational Fluid Dynamics model of stratified atmospheric boundary‐layer flow

For wind resource assessment, the wind industry is increasingly relying on computational fluid dynamics models of the neutrally stratified surface‐layer. So far, physical processes that are important to the whole atmospheric boundary‐layer, such as the Coriolis effect, buoyancy forces and heat transport, are mostly ignored. In order to decrease the uncertainty of wind resource assessment, the present work focuses on atmospheric flows that include stability and Coriolis effects. The influence of these effects on the whole atmospheric boundary‐layer are examined using a Reynolds‐averaged Navier–Stokes k‐ ε model. To validate the model implementations, results are compared against measurements from several large‐scale field campaigns, wind tunnel experiments, and previous simulations and are shown to significantly improve the predictions. Copyright © 2013 John Wiley & Sons, Ltd.     

Effectiveness of WRF wind direction for retrieving coastal sea surface wind from synthetic aperture radar

Wind direction is required as input to the geophysical model function (GMF) for the retrieval of sea surface wind speed from a synthetic aperture radar (SAR) images. The present study verifies the effectiveness of using the wind direction obtained from the weather research and forecasting model (WMF) as input to the GMF to retrieve accurate wind fields in coastal waters adjacent to complex onshore terrain. The wind speeds retrieved from 42 ENVISAT ASAR images are validated based on in situ measurements at an offshore platform in Japan. Accuracies are also compared with cases using wind directions: the meso‐analysis of the Japan Meteorological Agency (MANAL), the SeaWinds microwave scatterometer on QuikSCAT and the National Center for Environmental Prediction final operational global analysis data (NCEP FNL). In comparison with the errors of the SAR‐retrieved wind speeds obtained using the WRF, MANAL, QuikSCAT and NCEP FNL wind directions, the magnitudes of the errors do not appear to be correlated with the errors of the wind directions themselves. In addition to wind direction, terrain factors are considered to be a main source of error other than wind direction. Focusing on onshore winds (blowing from the sea to land), the root mean square errors on wind speed are found to be 0.75 m s ^{? 1} (in situ), 0.96 m s ^{? 1} (WRF), 1.75 m s ^{? 1} (MANAL), 1.58 m s ^{? 1} (QuikSCAT) and 2.00 m s ^{? 1} (NCEP FNL), respectively, but the uncertainty is of the same order of magnitude because of the low number of cases. These results indicate that although the effectiveness of using the accurate WRF wind direction for the wind retrieval is partly confirmed, further efforts to remove the error due to factors other than wind direction are necessary for more accurate wind retrieval in coastal waters. Copyright © 2012 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.     

Turbulence modeling of atmospheric boundary layer flow over complex terrain: a comparison of models at wind tunnel and full scale

The aim of this work is to evaluate the performance of two popular k ? ? turbulence closure schemes for atmospheric boundary layer (ABL) flow over hills and valleys and to investigate the effect of using ABL‐modified model constants. The standard k ? ? and the RNG k ? ? models are used to simulate flow over the two‐dimensional analytical shapes from the RUSHIL and RUSHVAL wind tunnel experiments. Furthermore, the mean turbulent flow over the real complex terrain of Blashaval hill is simulated and the results verified with a data set of full‐scale measurements. In general, all models yield similar results. However, use of ABL‐modified constants in both models tends to decrease the predicted velocity and increase the predicted turbulent kinetic energy. Copyright © 2010 John Wiley & Sons, Ltd.     

Reynolds number dependence of turbulence statistics in the wake of wind turbines

A wind tunnel experiment has been performed to quantify the Reynolds number dependence of turbulence statistics in the wake of a model wind turbine. A wind turbine was placed in a boundary layer flow developed over a smooth surface under thermally neutral conditions. Experiments considered Reynolds numbers on the basis of the turbine rotor diameter and the velocity at hub height, ranging from Re = 1.66 × 10⁴ to 1.73 × 10⁵. Results suggest that main flow statistics (mean velocity, turbulence intensity, kinematic shear stress and velocity skewness) become independent of Reynolds number starting from Re ≈ 9.3 × 10⁴. In general, stronger Reynolds number dependence was observed in the near wake region where the flow is strongly affected by the aerodynamics of the wind turbine blades. In contrast, in the far wake region, where the boundary layer flow starts to modulate the dynamics of the wake, main statistics showed weak Reynolds dependence. These results will allow us to extrapolate wind tunnel and computational fluid dynamic simulations, which often are conducted at lower Reynolds numbers, to full‐scale conditions. In particular, these findings motivates us to improve existing parameterizations for wind turbine wakes (e.g. velocity deficit, wake expansion, turbulence intensity) under neutral conditions and the predictive capabilities of atmospheric large eddy simulation models. Copyright © 2011 John Wiley & Sons, Ltd.     

An improved k‐ ϵ model applied to a wind turbine wake in atmospheric turbulence

An improved k‐ ? turbulence model is developed and applied to a single wind turbine wake in a neutral atmospheric boundary layer using a Reynolds averaged Navier–Stokes solver. The proposed model includes a flow‐dependent C_μ that is sensitive to high velocity gradients, e.g., at the edge of a wind turbine wake. The modified k‐ ? model is compared with the original k‐ ? eddy viscosity model, Large‐Eddy Simulations and field measurements using eight test cases. The comparison shows that the velocity wake deficits, predicted by the proposed model are much closer to the ones calculated by the Large‐Eddy Simulation and those observed in the measurements, than predicted by the original k‐ ? model. Copyright © 2014 John Wiley & Sons, Ltd.     

A new turbulence model for offshore wind turbine standards

Correct turbulence intensity modeling is crucial for fatigue load estimation for wind turbine structural design. It is well known that the International Electrotechnical Commission 61400‐3 Normal Turbulence Model recommended for offshore wind turbine design is not representative of offshore wind conditions. A new model is urgently needed as offshore wind energy is rapidly developing worldwide. After evaluating the suitability of the Normal Turbulence Model at three sites in Asia, Europe and the USA, it is found that wind–wave interaction and stability correction should be taken into account in modeling the offshore turbulence intensity and wind speed relationship. Therefore, a new turbulence intensity model, which models wind–wave interaction with the Charnock equation and adjusts for the influence of atmospheric stability through empirical turbulence scaling functions for the unstable atmospheric boundary layer, was developed. The new model is physically based and is tested against observations from the three sites. It shows better performance than the Normal Turbulence Model and hence is recommended to replace the Normal Turbulence Model. For model application, only two parameters are required, which are defined herein to represent offshore sites with high, medium and low turbulence intensities. Copyright © 2013 John Wiley & Sons, Ltd.     

Modeling stable thermal stratification and its impact on wind flow over topography

Microscale flow models used in the wind energy industry commonly assume statically neutral conditions. These models can provide reasonable wind speed predictions for statically unstable and neutral flows; however, they do not provide reliable predictions for stably stratified flows, which can represent a substantial fraction of the available energy at a given site. With the objective of improving wind speed predictions and in turn reducing uncertainty in energy production estimates, we developed a Reynolds‐Averaged Navier–Stokes (RANS)‐based model of the stable boundary layer. We then applied this model to eight prospective wind farms and compared the results with on‐site wind speed measurements classified using proxies for stability; the comparison also included results from linear and RANS wind flow models that assume neutral stratification. This validation demonstrates that a RANS‐based model of the stable boundary layer can significantly and consistently improve wind speed predictions. Copyright © 2014 John Wiley & Sons, Ltd.     

Blade‐resolved simulations of a model wind turbine: effect of temporal convergence

Simulations of a model wind turbine at various tip‐speed‐ratios were carried out using Tenasi, a node‐centered, finite volume unstructured flow solver. The simulations included the tunnel walls, tower, nacelle, hub and the blades. The effect of temporal convergence on the predicted thrust and power coefficients is evaluated and guidelines for best practices are established. The results presented here are for tip‐speed‐ratios of 3, 6 and 10, with 6 being the design point. All simulations were carried out at a freestream velocity of 10 m s^?1 with an incoming boundary layer present and the wind turbine RPM was varied to achieve the desired tip‐speed‐ratio. The performance of three turbulence models is evaluated. The models include a one‐equation model (Spalart–Allmaras), a two‐equation model (Menter SST) and the DES version of the Menter SST. Turbine performance as well as wake data at various locations is compared to experiment. All the turbulence models performed well in terms of predicting power and thrust coefficients. The DES model was significantly better than the other two turbulence models for predicting the mean and fluctuating components of the velocity in the wake. Copyright © 2015 John Wiley & Sons, Ltd.     

基于中微尺度耦合模式的风电场风资源评估方法研究

针对传统风资源评估方法采用假设的入流风廓线模型而无法考虑宏观大气环流对风电场内风流动影响的问题,文章基于中尺度WRF模式和微尺度CFD模型,研究了基于中微尺度耦合模式的风资源评估方法。首先,建立基于WRF模式的中尺度数值模拟方法和基于CFD方法的微尺度风资源评估方法;其次,研究了中微尺度数值模拟方法的耦合原理,构建了从中尺度模拟结果中提取微尺度建模计算边界附近风速廓线的方法,建立了中微尺度耦合风资源评估流程;最后,通过某复杂山地风电场进行验证。验证结果表明,中尺度模拟结果可以改善微尺度CFD模型的入流边界条件,并有效降低风资源评估的误差。