Spatial and temporal characteristics of wind and wind power off the coasts of Brittany

The main objective of this paper is to thoroughly examine the remotely sensed wind characteristics around the coasts of Brittany as well as some more specific areas. The offshore wind power potential is then assessed. To achieve this objective, information on wind speed and direction with sufficient spatial and temporal sampling under all weather conditions and during day and night is required. This study uses more than 12 years (December 1999–December 2012) of consistent remotely sensed data retrieved from the ASCAT and QuikSCAT scatterometers to estimate the conventional moments and associated wind distribution parameters. The latter are comparable to wind observations from meteorological stations. Furthermore, combining in-situ and scatterometer wind information enables an improved assessment of the spatial and temporal wind structures at specific locations of interest to be made. The wind statistical results are used to study the spatial and temporal patterns of the wind power. Although the main parameters characterizing wind power potential such as mean, variability, maximum energy, wind speed and intra-annual exhibit seasonal features, significant inter-annual variability is also depicted. Furthermore, differences are found between the wind power estimated for northern and for southern Brittany.     

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.     

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.     

Effects of sea surface temperature accuracy on offshore wind resource assessment using a mesoscale model

Offshore wind simulations were performed with the Weather Research and Forecasting (WRF) model driven by three different sea surface temperature (SST) datasets for Japanese coastal waters to investigate the effect of the SST accuracies on offshore wind simulations. First, the National Centers for Environmental Prediction Final analysis (FNL) (1° × 1° grid resolution) and the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) (0.05° × 0.05° grid resolution) datasets were compared with in situ measurements. The results show a decrease in accuracy of these datasets toward the coast from the open ocean. Aiming at an improved accuracy of SST data, we developed a new high‐resolution SST dataset (0.02° × 0.02° grid resolution). The new dataset referred to as MOSST is based on the Moderate Resolution Imaging Spectroradiometer (MODIS) product, provided by the Japan Aerospace Exploration Agency (JAXA). MOSST was confirmed to be more accurate than FNL and OSTIA for the coastal waters. Then, WRF simulations were carried out for 1 year with a 2 km grid resolution and by using the FNL, OSTIA and MOSST datasets. The use of the OSTIA dataset for a WRF simulation was found to improve the accuracy when compared with the FNL dataset, and further improvement was obtained when the MOSST dataset was applied. The sensitivity of wind speed and wind energy density to SST is also discussed. We conclude that the use of an accurate SST is a key factor not only for realistic offshore wind simulations near the surface but also for accurate wind resource assessments at the hub height of wind turbines. Copyright © 2014 John Wiley & Sons, Ltd.     

Eight years of wind measurements from scatterometer for wind resource mapping in the Mediterranean Sea

Eight years of wind observations from the SeaWinds scatterometer instrument on the National Aeronautics and Space Administration QuikScat satellite and in situ data from 11 locations in the Mediterranean have been considered. The data have been co‐located in time and space, and it is shown that the scatterometer is able to provide similar long‐term statistics as available from buoy data, such as annual and monthly wind indexes. Such statistics is useful to give an overview of the climatology in the different areas. The correlation between QuikScat and in situ observations is degraded towards the coast, giving indication of how well the scatterometer can represent the coastal winds. The degradation is stronger in areas with strong spatial variability. The QuikScat winds are gridded into a 0.25° by 0.25° grid to produce seasonal and annual means of the offshore wind conditions over the Mediterranean. Copyright © 2010 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.     

Prediction of the extreme wind speed in the mixed climate region by using Monte Carlo simulation and measure‐correlate‐predict method

The extreme wind speed at an offshore location was predicted using Monte Carlo simulation (MCS) and measure‐correlate‐predict (MCP) method. The Gumbel distribution could successfully express the annual maximum wind speed of extratropical cyclone. On the other hand, the estimated extreme wind speed of tropical cyclones by analytical probability distribution shows larger uncertainty. In the mixed climate like Japan, the extreme wind speed estimated from the combined probability distribution obtained by MCP and MCS methods agrees well with the observed data as compared with the combined probability distribution obtained by the MCP method only. The uncertainty of extreme wind speed due to limited observation period of wind speed and pressure was also evaluated by the Gumbel theory and Monte Carlo simulation. As a result, it was found that the uncertainty of 50 year recurrence wind speed obtained by MCS method is considerably smaller than that obtained by MCP method in the mixed climate. Copyright © 2014 John Wiley & Sons, Ltd.     

A numerical approach for planning offshore wind farms from regional to local scales over the Mediterranean

Renewable energy resources, such as wind, are available worldwide. Locating areas with high and continual wind sources are crucial in pre-planning of wind farms. Vast offshore areas are characterized by higher and more reliable wind resources in comparison with continental areas. However, offshore wind energy production is in a quite preliminary phase. Elaborating the potential productivity of wind farms over such areas is challenging due to sparse in situ observations. The Mediterranean basin is not an exception. In this study we are proposing numerical simulations of near-surface wind fields from regional climate models (RCMs) in order to obtain and fill the gaps in observations over the Mediterranean basin. Four simulations produced with two regional climate models are examined here. Remote sensing observations (QuikSCAT satellite) are used to assess the skill of the simulated fields. A technique for estimating the potential energy from the wind fields over the region is introduced. The wind energy potential atlas and the map of a wind turbine's functional range are presented, locating the potentially interesting sub-regions for wind farms. The ability of models to reproduce the annual cycle and the probability density function of wind speed anomalies are detailed for specified sub-regions.     

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.     

基于WRF模式与相似Kalman滤波法订正后的风速预报效果分析

为提高张家山风电场风速预报质量,比较了张家山测风塔处的实测风速WRF模式预报的风速的误差,再利用相似Kalman滤波方法订正预报的风速,以减小WRF模式预报风场的系统误差和随机误差。结果表明,WRF模式对敏感区的风速预报能力最好,对小风区的预报效果较差,对冬季的预报效果略好于春秋季;再使用相似Kalman滤波方法对模拟风速进行误差订正后,所有时刻的预报能力得到提高,平均偏差、均方根误差变小,从而提高了风速预报的准确率。     

A dynamical downscaling of ERA‐Interim in the North Sea using WRF with a 3 km grid—for wind resource applications

Large offshore wind energy projects are being planned and installed in the North Sea, and there is an urgent demand for high‐resolution atmospheric statistics to assess potential power production and revenue. Meteorological observations are too sparse to obtain those statistics, and global reanalyses like ERA‐Interim have a resolution too coarse in space and time to capture important small‐scale and terrain‐driven features of the atmospheric flow. We therefore dynamically downscale ERA‐Interim with the mesoscale model Weather Research and Forecasting to a 3 km grid to capture those unresolved features, for the period 1999–2008. The large‐scale flow is conditioned by spectral nudging, and we make use of observation nudging towards QuikSCAT near‐surface winds. The downscaling results in 100 m wind‐speed distributions and mean wind speeds, which are closer to the observations than ERA‐Interim, while the accuracy in terms of root‐mean‐square error decreases. The observation nudging partially counteracts this latter effect, improving the root‐mean‐square error of wind speed and direction by 0.5 m s^?1 and ~10°, respectively. We also introduce the power skill score, specifically designed to evaluate model performance within wind resource mapping. The power skill score confirms that the dynamical downscaling improves the distribution of wind speed in ranges where high accuracy is important for wind resource assessment. Copyright © 2016 John Wiley & Sons, Ltd.     

An evaluation of the predictive accuracy of wake effects models for offshore wind farms

Wake losses are perceived as one of the largest uncertainties in energy production estimates (EPEs) for new offshore wind projects. In recent years, significant effort has been invested to improve the accuracy of wake models. However, it is still common for a standard wake loss uncertainty of 50% to be assumed in EPEs for new offshore wind farms. This paper presents a body of evidence to support reducing that assumed uncertainty. It benchmarks the performance of four commonly used wake models against production data from five offshore wind farms. Three levels of evidence are presented to substantiate the performance of the models:

• Case studies, i.e. efficiencies of specific turbines under specific wind conditions;
• Array efficiencies for the wind farm as a whole for relatively large bins of wind speed and direction; and
• Validation wake loss, which corresponds to the overall wake loss within the proportion of the annual energy production where validation is possible.

The most important result for predicting annual energy production is the validation wake loss. The other levels of evidence demonstrate that this result is not unduly reliant on cancellation of errors between wind speed and/or wind direction bins. All of the root‐mean‐squared errors in validation wake loss are substantially lower than the 50% uncertainty commonly assumed in EPEs; indeed, even the maximum errors are below 25%. It is therefore concluded that there is a good body of evidence to support reducing this assumed uncertainty substantially, to a proposed level of 25%. Copyright © 2015 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.     

Offshore wind resource assessment with WAsP and MM5: comparative study for the German Bight

In this study, two different approaches to estimate the wind resource over the German Bight in the North Sea are compared: the mesoscale meteorological model MM5 and the wind resource assessment program WAsP. The dynamics of the atmosphere of the year 2004 was simulated with the MM5 model, with input from the NCEP global model, without directly utilizing measurement data. WAsP estimations were calculated on the basis of six measurement stations: three on islands, two offshore and one onshore. The annual mean wind speed at onshore, offshore and island sites is estimated by both models. The predictions are compared both with each other and with measured data. A spatial comparison of the wind resource calculated by the two models is made by means of a geographical information system. The results show that the accuracy of the WAsP predictions depends mainly on the measurement station used as input. Small differences are shown in the estimations performed by the three island stations, despite the large geographical distance between them. Compared with the measurements of the offshore sites, they seem to be suitable for estimating the offshore wind resource from measurements on land. The two offshore stations show differences when predicting each other's mean wind speed with the WAsP method, while the MM5 calculations show a similar deviation for both sites. The largest differences between the two models are found at distances of 5–50km from the coast. While in WAsP the increase occurs in the first 10km from the coast, MM5 models an increase due to coastal effects for at least 50km. Copyright © 2006 John Wiley &Sons, Ltd.     

Evaluating the impact of wind induced roughness change and tidal range on extrapolation of offshore vertical wind speed profiles

Wind energy resource estimation frequently requires extrapolation of wind speeds from typical measurement heights to turbine hub‐heights. However, this extrapolation is uncertain, and this uncertainty is exacerbated in the offshore environment by the effect of the dynamic surface (i.e. surface roughness and height respond to wind speed or vary over time). This paper examines the impact of roughness variations and small tidal ranges on mean predicted wind speeds in near‐neutral conditions. Roughness variations offshore are in the range 0.002 and 0.00002 m. This range of roughnesses gives a difference in predicted wind speed extrapolated from 10 to 50 m of less than 8%. For a more typical range of 0.0005 tp 0.00005 m, the difference will be smaller (～3%). With a tidal range of 4 m the difference in mean wind speed extrapolated from 10 to 50 m height is about 1%. Copyright © 2001 John Wiley & Sons, Ltd.     

The dynamics of an offshore wind turbine in parked conditions: a comparison between simulations and measurements

Offshore wind turbines are complex structures, and their dynamics can vary significantly because of changes in operating conditions, e.g., rotor‐speed, pitch angle or changes in the ambient conditions, e.g., wind speed, wave height or wave period. Especially in parked conditions, with reduced aerodynamic damping forces, the response due to wave actions with wave frequencies close to the first structural resonance frequencies can be high. Therefore, this paper will present numerical simulations using the HAWC2 code to study an offshore wind turbine in parked conditions. The model has been created according to best practice and current standards based on the design of an existing Vestas V90 offshore wind turbine on a monopile foundation in the Belgian North Sea. The damping value of the model's first fore‐aft mode has been tuned on the basis of measurements obtained from a long‐term ambient monitoring campaign on the same wind turbine. Using the updated model of the offshore wind turbine, the paper will present some of the effects of the different design parameters and the different ambient conditions on the dynamics of an offshore wind turbine. The results from the simulations will be compared with the processed data obtained from the real measurements. The accuracy of the model will be discussed in terms of resonance frequencies, mode shapes, damping value and acceleration levels, and the limitations of the simulations in modeling of an offshore wind turbine will be addressed. Copyright © 2014 John Wiley & Sons, Ltd.     

Offshore wind turbine fault alarm prediction

Offshore wind operations and maintenance (O&M) costs could reach up to one third of the overall project costs. In order to accelerate the deployment of offshore wind farms, costs need to come down. A key contributor to the O&M costs is the component failures and the downtime caused by them. Thus, an understanding is needed on the root cause of these failures. Previous research has indicated the relationship between wind turbine failures and environmental conditions. These studies are using work‐order data from onshore and offshore assets. A limitation of using work orders is that the time of the failure is not known and consequently, the exact environmental conditions cannot be identified. However, if turbine alarms are used to make this correlation, more accurate results can be derived. This paper quantifies this relationship and proposes a novel tool for predicting wind turbine fault alarms for a range of subassemblies, using wind speed statistics. A large variation of the failures between the different subassemblies against the wind speed are shown. The tool uses 5 years of operational data from an offshore wind farm to create a data‐driven predictive model. It is tested under low and high wind conditions, showing very promising results of more than 86% accuracy on seven different scenarios. This study is of interest to wind farm operators seeking to utilize the operational data of their assets to predict future faults, which will allow them to better plan their maintenance activities and have a more efficient spare part management system.     

ENDOW (efficient development of offshore wind farms): modelling wake and boundary layer interactions

While experience gained through the offshore wind energy projects currently operating is valuable, a major uncertainty in estimating power production lies in the prediction of the dynamic links between the atmosphere and wind turbines in offshore regimes. The objective of the ENDOW project was to evaluate, enhance and interface wake and boundary layer models for utilization offshore. The project resulted in a significant advance in the state of the art in both wake and marine boundary layer models, leading to improved prediction of wind speed and turbulence profiles within large offshore wind farms. Use of new databases from existing offshore wind farms and detailed wake profiles collected using sodar provided a unique opportunity to undertake the first comprehensive evaluation of wake models in the offshore environment. The results of wake model performance in different wind speed, stability and roughness conditions relative to observations provided criteria for their improvement. Mesoscale model simulations were used to evaluate the impact of thermal flows, roughness and topography on offshore wind speeds. The model hierarchy developed under ENDOW forms the basis of design tools for use by wind energy developers and turbine manufacturers to optimize power output from offshore wind farms through minimized wake effects and optimal grid connections. The design tools are being built onto existing regional‐scale models and wind farm design software which was developed with EU funding and is in use currently by wind energy developers. Copyright © 2004 John Wiley & Sons, Ltd.     

Modification of the Baltic Sea wind field by land‐sea interaction

The wind and turbulence fields over a small, high‐latitude sea are investigated. These fields are highly influenced by the proximity to the coast, which is never more than 200 km away. Simulations with the WRF model over the Baltic Sea are compared with a simplified, stationary wind model driven by the synoptic forcing. The difference between the models is therefore representative of the mesoscale influence. The results show that the largest wind‐field modifications compared with a neutral atmosphere occur during spring and summer, with a mean monthly increase of up to approximately 1 ms^?1 at typical hub heights and upper rotor area (120‐170 m height) in the WRF model. The main reason for this is large‐scale low‐level jets caused by the land‐sea temperature differences, likely increasing in strength due to inertial oscillations. These kind of events can be persistent for approximately 12 hours and cover almost the entire basin, causing wind speed and wind shear to increase considerably. The strongest effect is around 2000 to 2300 local time. Sea breezes and coastal low‐level jets are of less importance, but while sea breezes are mostly detected near the coastline, other types of coastal jets can extend large distances off the coast. During autumn and winter, there are fewer low‐level jet occurrences, but the wind profile cannot be explained by the classical theory of the one‐dimensional model. This indicates that the coastal environment is complex and may be affected by advection from land surfaces to a large degree even when unstable conditions dominate.     

Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm

Here, we quantify relationships between wind farm efficiency and wind speed, direction, turbulence and atmospheric stability using power output from the large offshore wind farm at Nysted in Denmark. Wake losses are, as expected, most strongly related to wind speed variations through the turbine thrust coefficient; with direction, atmospheric stability and turbulence as important second order effects. While the wind farm efficiency is highly dependent on the distribution of wind speeds and wind direction, it is shown that the impact of turbine spacing on wake losses and turbine efficiency can be quantified, albeit with relatively large uncertainty due to stochastic effects in the data. There is evidence of the ‘deep array effect’ in that wake losses in the centre of the wind farm are under‐estimated by the wind farm model WAsP, although overall efficiency of the wind farm is well predicted due to compensating edge effects. Copyright © 2010 John Wiley & Sons, Ltd.