Error features and their possible causes in simulated low‐level winds by WRF at a wind farm

The WRF model is applied to simulate the low‐level wind field for a wind farm located in a typical arid region in northwest China for February 2008. The selected region has complex terrain with sparse vegetation. Overall, the WRF model reproduced the variation features of wind speeds and wind directions. However, the model overestimated the observed low‐level wind speeds, and there were large discrepancies for the low wind velocity (i.e. the errors of simulated winds increase with height and will be larger when the observed wind speeds are lower than 2.5 m/s). The features of the simulated errors and the possible causes in the model were analysed. The simulated low‐level wind in the afternoon is more accurate than that in early morning, which is usually unstable. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

Wind characteristics in the North and Baltic Seas from the QuikSCAT satellite

The QuikSCAT mission provided valuable daily information on global ocean wind speed and direction from July 1999 until November 2009 for various applications including numerical weather prediction, ocean and atmospheric modelling. One new and important application for wind vector satellite data is offshore wind energy, where accurate and frequent measurements are required for siting and operating modern wind farms. The greatest advantage of satellite observations rests in their extended spatial coverage. This paper presents analyses of the 10 year data set from QuikSCAT, for the overview of the wind characteristics observed in the North and Baltic Seas, where most of Europe's offshore wind farms operate and more will be constructed. Significant issues in data availability are identified, directly related to the flagging schemes. In situ observations from three locations in the North Sea are used for comparisons. Mean biases (in situ minus satellite) are close to zero for wind speed and ‐2.7° for wind direction with a standard deviation of 1.2 m s ^{? 1} and 15°, respectively. The impact of using QuikSCAT and in situ measurements extrapolated to 10 m for wind power density estimations is assessed, accounting for possible influences of rain‐contaminated retrievals, the sample size, the atmospheric stability effects and either fitting the Weibull distribution or obtaining the estimates from the time series of wind speed observations.Copyright © 2012 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.     

Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function

The wind resource is a crucial step in planning a wind energy project and detailed knowledge of the wind characteristic at a site is needed to estimate the performance of a wind energy project. In this paper, with the help of 2-parameter Weibull distribution, the assessment of wind energy potentiality at Kudat and Labuan in 2006-2008 was carried out. “WRPLOT” software has been used to show the wind direction and resultant of the wind speed direction. The monthly and yearly highest mean wind speeds were 4.76 m/s at Kudat and 3.39 m/s at Labuan respectively. The annual highest values of the Weibull shape parameter (k) and scale parameter (c) were 1.86 and 3.81 m/s respectively. The maximum wind power density was found to be 67.40 W/m² at Kudat for the year 2008. The maximum wind energy density was found to be 590.40 kWh/m²/year at Kudat in 2008. The highest most probable wind speed and wind speed carrying maximum energy were estimated 2.44 m/s at Labuan in 2007 and 6.02 m/s at Kudat in 2007. The maximum deviation, at wind speed more than 2 m/s, between observed and Weibull frequency distribution was about 5%. The most probable wind directions (blowing from) were 190° and 269° at Kudat and Labuan through the study years. From this study, it is concluded that these sites are unsuitable for the large-scale wind energy generation. However, small-scale wind energy can be generated at the turbine height of 100 m.     

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.     

Investigation of wind energy potentials in Brunei Darussalam

Conventional power generation mainly depends on natural gas and diesel oil in Brunei Darussalam. The power utility company is now thinking of power generation using natural wind. In this paper, wind energy, being one of the most readily available renewable energy sources, was studied. The wind characteristic, velocity and directions were studied using Weibull distribution based on the measurement of wind speed at two different locations in Brunei Darussalam. These wind speed distributions were modeled using the Wind Power program. The wind rose graph was obtained for the wind direction to analyze the wind power density onshore and offshore. Based on this analysis, it has been found that the wind speed of 3 to 5 m/s has a probability of occurrence of 40%. Besides, the annual energy production at a wind speed of 5 m/s has been found to be in the range between 1000 and 1500 kWh for both the locations in Brunei Darussalam.     

A comparison between Advanced Scatterometer and Weather Research and Forecasting wind speeds for the Japanese offshore wind resource map

This paper investigates the validity of the method used in the Japanese offshore wind map (NeoWins) to seamlessly connecting satellite‐derived wind speed for open oceans to mesoscale model‐simulated wind speed for coastal waters. In the NeoWins, the former was obtained from the satellite‐borne Advanced Scatterometer (ASCAT), and the latter was obtained from numerical simulations using the Weather Research and Forecasting (WRF) model. In this study, the consistency of the ASCAT and WRF 10‐m height wind speeds is examined in their overwrapping areas. The comparison between ASCAT and WRF model reveals that their differences in annual mean wind speed are mostly within ±5% and that the ASCAT annual mean wind speed is, as a whole, slightly higher than the WRF annual mean wind speed. The results indicate that there are no large wind speed gaps between WRF and ASCAT in most parts of the Japanese offshore areas. It is moreover found that the discrepancies between the two wind speeds are due to two factors: one is the existence of winter sea ice in the ASCAT observation in the Sea of Okhotsk in ASCAT observation and the other is that the accuracy of the WRF wind speed depends on atmospheric stability.     

Feasibility study of offshore wind turbine installation in Iran compared with the world

Renewable energies have potential for supplying of relatively clean and mostly local energy. Wind energy generation is expected to increase in the near future and has experienced dramatic growth over the past decade in many countries. Offshore winds are generally stronger and more constant than onshore winds in many areas. The economic feasibility for utilization of offshore wind energy depends on the favorable wind conditions in the area. The present paper analyses offshore wind speed in global scale and also studies feasibility of introducing this technology for harnessing wind in Persian Gulf, Caspian Sea, Urmia Lake and Gulf of Oman. Wind speed data were collected from different sources. The ocean surface winds at a 10 m height from satellite passes as processed by NOAA/NESDIS, from near real-time data collected by NASA/JPL's Sea Winds Scatterometer aboard the QuikSCAT. Development of renewable energy is one of priority research goals in Iran. There are many installed wind turbines in suitable regions like Manjil and Binalood, but there has not been any offshore wind installation yet in Iran. It is suggested that policy makers to invest and pay more attentions toward harnessing renewable energy sources like offshore wind in Persian Gulf and Gulf of Oman in southern parts of Iran.     

A general method to estimate wind farm power using artificial neural networks

An artificial neural network (ANN) is trained and validated using a large dataset of observations of wind speed, direction, and power generated at an offshore wind farm (Lillgrund in Sweden). In its traditional form, the ANN is used to generate a new two‐dimensional power curve, which predicts with high accuracy (bias ～?0.5% and absolute error ～2%) the power of the entire Lillgrund wind farm based on wind speed and direction. By contrast, manufacturers only provide one‐dimensional power curves (i.e., power as a function of wind speed) for a single turbine. The second innovative application of the ANN is the use of a geometric model (GM) to calculate two simple geometric properties to replace wind direction in the ANN. The resulting GM‐ANN has the powerful feature of being applicable to any wind farm, not just Lillgrund. A validation at an onshore wind farm (Nørrekær in Denmark) demonstrates the high accuracy (bias ～?0.7% and absolute error ～6%) and transfer‐learning ability of the GM‐ANN.     

Worldwide wind energy status and the characteristics of wind energy in Iran,case study: the province of Sistan and Baluchestan

In this paper, the recent trend of the worldwide wind energy utilisation is reviewed and the recent activities in using renewable energy sources in Iran are explained. As a case study, the wind characteristics of the province of Sistan and Baluchestan are statistically analysed. The wind characteristics such as the monthly mean wind speed and the wind power density of each station are presented. The monthly variation of the wind direction is presented and also the dominant wind direction is shown in a wind rose diagram. The values of turbulence intensity at different heights are calculated. The results show that the stations of Khash and Nosratabad are more suitable for limited off-grid utility applications. Lootak with the average annual wind power density of 388?W?m^?2 at the height of 40?m and constant wind direction is recommended for large-scale grid-connected wind turbines.     

Offshore wind power simulation by using WRF in the central coast of Chile

This paper presents the first estimate of offshore wind power potential for the central coast of Chile. For this purpose, wind speed data from in-situ stations and ERA-Interim reanalysis were used to simulate wind fields at regional level by means of the Weather Research and Forecasting (WRF) model. Wind field simulations were performed at different heights (20, 30, 40 and 140 m.a.s.l.) and a spatial resolution of 3 × 3 km for the period from February 1, 2006 to January 31, 2007, which comprised the entire series of in-situ data available. The results show an RMSE and r² of 2.2 m s⁻¹ and 0.55 respectively for the three heights simulated as compared to in-situ data. Based on the simulated wind data, the wind power for the study area was estimated at ∼1000 W m⁻² at a height of 140 m.a.s.l. For a typical wind turbine of 8 MW generator, the estimated capacity factor exceeds 40%, with an average annual generation of ∼30 GWh. Offshore wind power in Chile is an emerging renewable energy source that is as yet still under-developed, these estimates help to fill in some of the gaps in our knowledge about Chile's true renewable energy potential.     

Wind energy assessment and wind farm simulation in Triunfo – Pernambuco,Brazil

This paper presents a wind energy assessment and a wind farm simulation in the city of Triunfo in the state of Pernambuco in the northeast region of Brazil. The wind data were obtained from the SONDA (Sistema de Organização Nacional de Dados Ambientais) project’s meteor station (wind speed, wind direction and temperature) at both heights of 50 m during a period of time of 30 months. The Triunfo wind characterization and wind power potential assessment study shows an average wind speed (V) of 11.27 m/s (predominant Southeast wind direction), an average wind power density (P/A_T) of 1.672 W/m² and Weibull parameters shape (K) and scale (A) respectively equal to 2.0 and 12.7 m/s. Those values demonstrate an important wind potential in this region for future wind farm prospection. The wind farm (TRI) was simulated by using 850 kW wind turbines given a total of 20 MW installed. The simulated results show(s) an AEP (annual energy produced) of 111.4 GWh, a capacity factor (C_f) of 62% and a total of 5.462 h of operation by year (full load hours). The economical simulated results show(s) a Pay-back of 3 years Internal Rate of Return (IRR) of 47% and Net Present Value (NPV) of 85.506 k€ (both in a period of time of 20 years).     

Detection of nacelle anemometer faults in a wind farm minimizing the uncertainty

The performance assessment of wind farms requires the acquisition of accurate power and wind speed data of each turbine. Nowadays, the nacelle anemometry is widely studied as an option for power performance verification. Therefore, systems to detect the nacelle anemometer faults in a wind farm in operation are necessary for maintenance purposes. In this paper, we propose a method to detect wind speed deviations of the nacelle anemometers by comparing them with the nearby anemometers. This comparison is made through an approach to estimate the wind speed in each nacelle. The approach is based on the discretization of wind speed data using the bin method. The key issue of this proposal is the estimation of the anemometer deviations considering the range of data with lower uncertainty. To this end, an average uncertainty model per bin and direction sector has been integrated into the method. The tests show that using wind speeds higher than 4.5 m s ^{? 1} gives the lowest uncertainty. Data from two wind farms have been used to test this method, and the obtained results have allowed the detection of problematic anemometers. 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.     

Examining wind quality and wind power prospects on Fiji Islands

Wind resource analysis was carried out for two major islands in the Fiji. Wind data from July 1993 to June 2005 from NASA data base was analysed. Annual seasonal variation in wind speed, direction and power density were analysed for various locations. The average yearly wind speed for Fiji is between 5 and 6 m/s with average power density of 160 W/m². Site specific validation showed no significant relationship between NASA and experimental data. The wind resource at Laucala Bay has a power density of 131 W/m² at 55 m. The expected annual energy produced from a 275 kW GEV Vergnet wind turbine is 344 MWh. The capacity factor of the turbine is expected to be 14.3% with an overall efficiency of 37%. The electricity generated would cost $FJ 0.27 per kWh. The system will payback its worth in 12.2 years.     

One‐year observations of the wind distribution and low‐level jet occurrence at Braunschweig,North German Plain

A data set consisting of one‐year vertical profiles of horizontal wind speed obtained with lidar at Braunschweig Airport, North German Plain, is analyzed with respect to the low‐level jet (LLJ). The observations reveal a typical LLJ altitude between 80 and 360 m, a frequency of occurrence up to almost 9% for some altitudes, and a typical wind speed between 4 and 9 m s^?1. LLJ events occurred most frequently in summer during the night. In the winter, LLJs were observed both during day and night. The Weibull distribution for wind speed is presented for different heights. The most probable wind speed of the Weibull distribution increases from 4 m s^?1 at 40 m altitude to values exceeding 7 m s^?1 for altitudes above 240 m. There is a significant difference for the Weibull parameters determined with a monthly, seasonal and annual data set. The contribution of the LLJ to the overall wind speed distribution is analyzed. An LLJ event occurred on 52% of the days over the year, with a total measurement time of 739 h. As the typical rated speed for onshore wind turbines is in the range from 11.5 to 14.5 m s^?1 and the typical hub height is in the range of 100 to 150 m, it can be expected that wind turbines are affected by the LLJ. Copyright © 2015 John Wiley & Sons, Ltd.     

WRF模式三种降尺度方案在风功率预报业务中的效果对比分析

针对风功率实际预报业务中的空间分辨率需求,以WRF数值模式为基础,分别采用直接插值、WRF模式嵌套和WRF＋CALMET三种降尺度方案,得到目标站点数值预报产品,并将70 m高度风速、风向、温度与测风观测塔观测结果进行了对比统计。最后得出结论：三种方案得到的风速预报与实际观测值相关性相差不大,其中WRF嵌套方案最好,而CALMET方案相关性最差,但是CALMET方案的系统偏差最小;三种方案得到的风向预报与观测有一定误差,但三者相差不明显;三种方案的温度预报效果均很好;在实际功率预报业务中,仅使用WRF嵌套方案或WRF粗网格插值法即可。     

A method for spatial interpolation of wind climatologies

The local wind climate is described by probability distributions for the wind direction and for the wind speed given a certain direction. As an alternative to the latter, the wind speed may be described by its statistical moments which are easily integrated over all directions. This allows representation of the wind climatology as a set of directionally independent statistics and the corresponding normalized directional distributions often called wind roses. The methodology is to spatially interpolate wind climatologies in an irregular mesh by two‐dimensioanl third‐order Bézier polynomials for the directionally independent statistics and linear interpolation for the wind roses. Local Bézier surfaces are set up in a patchwork of triangles and adjusted for approximate first‐order geometric continuity (GC¹) and minimal surface curvature. The wind rose interpolation is calculated relative to a typical local wind direction by means of Fourier transformation. A complementary surface extrapolation method is suggested which retains continuity on the grid boundary and asymptotically linear variation away from this. Copyright © 1999 John Wiley & Sons, Ltd.     

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.