Offshore validation of a 3 km ERA‐Interim downscaling—WRF model's performance on static stability

In the evaluation of performance of numerical models, static stability has gained less interest and attention than many other atmospheric parameters. Nevertheless, this parameter may in fact have rather large implications as it controls such features as gravity waves and turbulence. In this paper, a closer look has been taken at the behavior of static stability in a 3 km gridded numerical downscaling of the ERA‐Interim reanalysis. The simulation performed with the Weather Research and Forecasting model has been nudged towards large scale analysis (spectral nudging) and satellite‐derived ocean surface winds (Quick Scatterometer). The validation of low‐level static stability was conducted in the North Sea using data from the FINO1 mast for year 2008, and a deeper part of the atmosphere was validated using the Ekofisk oil platform radiosonde data for years 2007–2010. The offshore mast comparison shows that the model over‐predicts the number of unstable cases and that the unstable cases are not unstable enough. The model simulated a clear dependency between vertical wind shear and static stability; however, this was not the case in the observations. The model produced too few events with weak turbulence, but rather put the emphasis on intermediate turbulent intensities. With the radiosonde data measuring the static stability over a deeper part of the atmosphere, similar results as for lower levels showed. In addition, the temperature gradient across the inversion at the boundary layer top was too shallow in the model, potentially letting too much wave energy escape the boundary layer. © 2015 The Authors Wind Energy Published by John Wiley & Sons Ltd.     

A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change

A methodology is presented for downscaling General Circulation Model (GCM) output to predict surface wind speeds at scales of interest in the wind power industry under expected future climatic conditions. The approach involves a combination of Neural Network tools and traditional weather forecasting techniques. A Neural Network transfer function is developed to relate local wind speed observations to large scale GCM predictions of atmospheric properties under current climatic conditions. By assuming the invariability of this transfer function under conditions of doubled atmospheric carbon dioxide, the resulting transfer function is then applied to GCM output for a transient run of the National Center for Atmospheric Research coupled ocean-atmosphere GCM. This methodology is applied to three test sites in regions relevant to the wind power industry—one in Texas and two in California. Changes in daily mean wind speeds at each location are presented and discussed with respect to potential implications for wind power generation.     

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.     

Adaptive post‐processing of short‐term wind forecasts for energy applications

We present a new method of reducing the error in predicted wind speed, thus enabling better management of wind energy facilities. A numerical weather prediction model, COSMO, was used to produce 48 h forecast data every day in 2008 at horizontal resolutions of 10 and 3 km. A new adaptive statistical method was applied to the model output to improve the forecast skill. The method applied corrective weights to a set of forecasts generated using several post‐processing methods. The weights were calculated based on the recent skill of the different forecasts. The resulting forecast data were compared with observed data, and skill scores were calculated to allow comparison between different post‐processing methods. The total root mean square error performance of the composite forecast is superior to that of any of the individual methods. Copyright © 2010 John Wiley & Sons, Ltd.     

Validation of boundary layer parameterization schemes in the weather research and forecasting model under the aspect of offshore wind energy applications— Part I: Average wind speed and wind shear

Five different planetary boundary layer (PBL) schemes in the weather research and forecasting 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 Yonsei University, asymmetric convection model version 2, quasi‐normal scale elimination, Mellor–Yamada–Janjic and Mellor–Yamada–Nakanishi–Niino PBL schemes with weather research and forecasting have been performed for the North Sea and validated against measurements of the Forschungsplattformen in Nord‐ und Ostsee Nr.1 platform. The investigations have been focused on the key parameters 100 m mean wind speed and wind shear expressed by the power law exponent α. All PBL‐schemes are doing well in reproducing averages and average annual statistics of the 100 m wind speed. However, two of the schemes (Yonsei University and Mellor–Yamada–Nakanishi–Niino) overestimate the wind speed above 15 m s^?1 systematically. The results for the power law wind profile show a large variability between the models and the observations for different atmospheric stability conditions and also differ a lot from the industry standards. Overall, the Mellor–Yamada–Janjic scheme performs slightly better than the others and is suggested as first choice for marine atmospheric boundary layer simulations without apriori information of atmospheric stability in the region of interest. Copyright © 2014 John Wiley & Sons, Ltd.     

Characterization of the wind speed variability and future change in the Iberian Peninsula and the Balearic Islands

Wind energy is susceptible to global climate change because it could alter the wind patterns. Then, improvement of our knowledge of wind field variability is crucial to optimize the use of wind resources in a given region. Here, we quantify the effects of climate change on the surface wind speed field over the Iberian Peninsula and Balearic Islands using an ensemble of four regional climate models driven by a global climate model. Regions of the Iberian Peninsula with coherent temporal variability in wind speed in each of the models are identified and analysed using cluster analysis. These regions are continuous in each model and exhibit a high degree of overlap across the models. The models forced by the European Reanalysis Interim (ERA‐Interim) reanalysis are validated against the European Climate Assessment and Dataset wind. We find that regional models are able to simulate with reasonable skill the spatial distribution of wind speed at 10 m in the Iberian Peninsula, identifying areas with common wind variability. Under the Special Report on Emissions Scenarios (SRES) A1B climate change scenario, the wind speed in the identified regions for 2031–2050 is up to 5% less than during the 1980–1999 control period for all models. The models also agree on the time evolution of spatially averaged wind speed in each region, showing a negative trend for all of them. These tendencies depend on the region and are significant at p = 5% or slightly more for annual trends, while seasonal trends are not significant in most of the regions and seasons. 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.     

Enhanced estimation of boundary layer advective properties to improve space‐to‐time conversion processes for wind energy applications

Remote sensing instruments that scan have the ability to provide high‐resolution spatial measurements of atmospheric boundary layer winds across a region. However, the ability to use these spatially distributed measurements to extract temporal variations in the flow at time scales less than the measurement revisit period is historically limited. As part of this work, the framework for an enhanced space‐to‐time conversion technique is established, allowing for time histories of atmospheric boundary layer wind characteristics to be reliably extracted for locations within the measurement domain. This space‐to‐time conversion technique is made possible by quantifying momentum advection within the measurement domain, rather than simply assuming a uniform advection based on a singular mean wind speed and direction. The use of this technique enables the extraction of long lead‐time (ie, upwards of 60 seconds) forecasts of wind speed and direction at individual locations within the measurement domain, thereby expanding the application and potential benefits of scanning instruments. For example, these long lead‐time forecasts can be used to enhance proactive wind turbine control and more accurately define wind turbine wake statistics.     

Evaluation of four numerical wind flow models for wind resource mapping

A wide range of numerical wind flow models are available to simulate atmospheric flows. For wind resource mapping, the traditional approach has been to rely on linear Jackson–Hunt type wind flow models. Mesoscale numerical weather prediction (NWP) models coupled to linear wind flow models have been in use since the end of the 1990s. In the last few years, computational fluid dynamics (CFD) methods, in particular Reynolds‐averaged Navier–Stokes (RANS) models, have entered the mainstream, whereas more advanced CFD models such as large‐eddy simulations (LES) have been explored in research but remain computationally intensive. The present study aims to evaluate the ability of four numerical models to predict the variation in mean wind speed across sites with a wide range of terrain complexities, surface characteristics and wind climates. The four are (1) Jackson–Hunt type model, (2) CFD/RANS model, (3) coupled NWP and mass‐consistent model and (4) coupled NWP and LES model. The wind flow model predictions are compared against high‐quality observations from a total of 26 meteorological masts in four project areas. The coupled NWP model and NWP‐LES model produced the lowest root mean square error (RMSE) as measured between the predicted and observed mean wind speeds. The RMSE for the linear Jackson‐Hunt type model was 29% greater than the coupled NWP models and for the RANS model 58% greater than the coupled NWP models. The key advantage of the coupled NWP models appears to be their ability to simulate the unsteadiness of the flow as well as phenomena due to atmospheric stability and other thermal effects. Copyright © 2012 John Wiley & Sons, Ltd.     

The influence of non‐logarithmic wind speed profiles on potential power output at Danish offshore sites

Detailed knowledge of mean wind speed profiles is essential for properly assessing the power output of a potential wind farm. Since atmospheric stratification plays a crucial role in affecting wind speed profiles, obtaining a detailed picture of the climatology of stability conditions at a given site is very important. In the present study, long time series from offshore measurement sites around Denmark are analysed, with the aim of quantifying the role of atmospheric stability in wind speed profiles and in our ability to model them. A simple method for evaluating stability is applied, and the resulting statistics of the atmospheric stratification is thoroughly studied. A significant improvement in the mean wind speed profile prediction is obtained by applying a stability correction to the logarithmic profiles suitable for neutral conditions. These results are finally used to estimate power densities at different heights. Copyright © 2004 John Wiley & Sons, Ltd.     

Two improvements to the dynamic wake meandering model: including the effects of atmospheric shear on wake turbulence and incorporating turbulence build‐up in a row of wind turbines

The dynamic wake meandering (DWM) model is an engineering wake model designed to physically model the wake deficit evolution and the unsteady meandering that occurs in wind turbine wakes. The present study aims at improving two features of the model:

相似文献   

Using wind velocity estimated from a reanalysis to minimize the variability of aggregated wind farm production over Europe

In this work we use the mean‐variance portfolio optimization, using as input the power derived from the wind and density results of meteorological model simulations (ERA‐Interim reanalysis), to minimize the variability of the wind power produced in a large region. The methodology involves selecting the placement of the wind farms on a high spatial resolution grid. We used the EU‐28 region to check the method and perform sensitivity tests. We studied the influence of the ratio between the total installed power of the whole domain (P_t) and the maximum power that can be installed per cell (P_mi) on the variability of wind power yield. The results show that the reliability of the electrical system improves when P_mi grows and worsens when P_t grows. A quadratic fit relates the variability of the system and the aforementioned ratio. The optimization procedure tends to select groups of terrain cells where wind farms should be installed. These groups grow when more energy production is demanded of the system, but they roughly maintain their location. There is some evidence that in a larger region greater system reliability could be achieved. Most of the selected cells have either a high or a low capacity factor and those with the latter are crucial in enhancing system reliability.     

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.     

Prediction of local wind climatology from Met Office models: Virtual Met Mast techniques

The Met Office has developed the Virtual Met Mast? (VMM) tool for assessing the feasibility of potential wind farm sites. It provides site‐specific climatological wind information for both onshore and offshore locations. The VMM relies on existing data from past forecasts from regional‐scale numerical weather prediction (NWP) models, to which corrections are applied to account for local site complexity. The techniques include corrections to account for the enhanced roughness lengths used in NWP models to represent drag due to sub‐grid orography and downscaling methods that predict local wind acceleration over small‐scale terrain. The corrected NWP data are extended to cover long periods (decades) using a technique in which the data are related to alternative long‐term datasets. For locations in the UK, the VMM currently relies on operational mesoscale model forecast data at 4 km horizontal resolution. Predictions have been verified against observations made at typical wind turbine hub heights at over 80 sites across the UK. In general, the predictions compare well with the observations. The techniques provide an efficient method for screening potential wind resource sites. Examples of how the VMM techniques can be used to produce local wind maps are also presented. © 2016 Crown copyright. Wind Energy © 2016 John Wiley & Sons, Ltd     

Wind turbine power curves incorporating turbulence intensity

The performance of a wind turbine in terms of power production (the power curve) is important to the wind energy industry. The current International Electrotechnical Commission 61400‐12‐1 standard for power curve evaluation recognizes only the mean wind speed at hub height and the air density as relevant to the power production. However, numerous studies have shown that the power production depends on several variables, in particular turbulence intensity. This paper presents a model and a method that are computationally tractable and able to account for some of the influence of turbulence intensity on the power production. The model and method are parsimonious in the sense that only a single function (the zero‐turbulence power curve) and a single auxiliary parameter (the equivalent turbulence factor) are needed to predict the mean power at any desired turbulence intensity. The method requires only 10 min statistics but can be applied to data of a higher temporal resolution as well. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

A prediction approach using ensemble empirical mode decomposition‐permutation entropy and regularized extreme learning machine for short‐term wind speed

Accurate prediction of short‐term wind speed is of great significance to the operation and maintenance of wind farms, the optimal scheduling of turbines, and the safe and stable operation of power grids. A prediction approach for short‐term wind speed using ensemble empirical mode decomposition‐permutation entropy and regularized extreme learning machine is proposed. Firstly, wind speed time series is decomposed into several components with different frequency by ensemble empirical mode decomposition, which can reduce the non‐stationarity of the original time series. The permutation entropy value for each component is used to analyze its complexity. The components can be recombined to obtain a set of new subsequences. Then, different prediction models based on regularized extreme learning machine are used to predict each subsequence. Fivefold cross validation is used to improve the reliability of the regularized extreme learning machine model. Finally, the predicted value of each subsequence is superimposed to obtain the final predictive result. Ten minutes, 30 minutes, and 1 hour short‐term wind speed data from wind farms in Liaoning Province, China, are used for conducting experiments. The experimental results indicate that the values of the root mean square error of the developed prediction approach utilizing 10 minutes, 30 minutes, and 1 hour interval data are 0.5629, 0.4473, and 0.5697; mean absolute error are 0.4427, 3.0701, and 0.4897; mean absolute percentile error are 4.1456%, 16.8166%, and 6.8166%; relative root mean square are 0.0505, 0.2997, and 0.2609; square sum error are 55.5263, 59.6347, and 64.9154; and the Theil inequality coefficient are 0.0235, 0.0808, and 0.0625, which are much lower than those of the comparison methods. The values of the R square of the developed prediction approach utilizing 10 minutes, 30 minutes, and 1 hour interval data are 0.9363, 0.9161, and 0.9472, and the index of agreement are 0.9994, 0.9925, and 0.9894, which are higher than those of the comparison methods. The Pearson's test results show that the association strength between the actual value and the predicted values of the proposed approach is stronger. Also, the proposed prediction approach in this paper has higher reliability under the same confidence level. The effectiveness of the proposed prediction approach for short‐term wind speed is verified.     

Mesoscale modeling of coastal low‐level jets: implications for offshore wind resource estimation

Detailed and reliable spatiotemporal characterizations of turbine hub height wind fields over coastal and offshore regions are becoming imperative for the global wind energy industry. Contemporary wind resource assessment frameworks incorporate diverse multiscale prognostic models (commonly known as mesoscale models) to dynamically downscale global‐scale atmospheric fields to regional‐scale (i.e., spatial and temporal resolutions of a few kilometers and a few minutes, respectively). These high‐resolution model solutions aim at depicting the expected wind behavior (e.g., wind shear, wind veering and topographically induced flow accelerations) at a particular location. Coastal and offshore regions considered viable for wind power production are also known to possess complex atmospheric flow phenomena (including, but not limited to, coastal low‐level jets (LLJs), internal boundary layers and land breeze–sea breeze circulations). Unfortunately, the capabilities of the new‐generation mesoscale models in realistically capturing these diverse flow phenomena are not well documented in the literature. To partially fill this knowledge gap, in this paper, we have evaluated the performance of the Weather Research and Forecasting model, a state‐of‐the‐art mesoscale model, in simulating a series of coastal LLJs. Using observational data sources we explore the importance of coastal LLJs for offshore wind resource estimation along with the capacity to which they can be numerically simulated. We observe model solutions to demonstrate strong sensitivities with respect to planetary boundary layer parameterization and initialization conditions. These sensitivities are found to be responsible for variability in AEP estimates by a factor of two. Copyright © 2013 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.