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Use of spatio-temporal calibrated wind shear model to improve accuracy of wind resource assessment
Abstract
Wind shear models are commonly used to predict the wind speed at wind turbine hub heights from the wind data collected at the elevation of the monitoring station. In many cases, the model parameters are based on empirical values recommended by design specifications that reflect the site conditions. This paper evaluates the benefits of incorporating site-specific wind data to calibrate the wind shear model parameters. Wind speed data collected by a ZephIR® Light Detection and Ranging (LiDAR) system over a 2-year (Oct. 2010–Sept. 2012) period are used for the analyses. The atmospheric stability is found to have appreciable effects on the wind shear parameters, i.e. wind shear coefficients (WSC) for the power law model and roughness lengths for the logarithmic law wind shear models. The calibrated wind shear model parameters by the monitored wind data during the first year are presented in the format of a contour map to demonstrate the spatio-temporal variations, which shows daily and seasonal variations. The calibrated wind shear models are then validated by the wind data collected during the second year, which demonstrates decent performance. The accuracy and performance of incorporating site-specific wind shear model calibration to predict the wind energy resource is evaluated, where six different methods are compared. The results show that the consideration of spatio-temporal variations of wind shear model parameters achieved improve performance over the application of the empirical or yearly-averaged wind shear model parameters in extrapolating the wind speed. It is also found that the performance of considering spatio-temporal wind shear parameters are even better at higher elevations. Furthermore, the analyses find that the use of empirical wind shear model parameters underestimates the wind energy output at the studied sites. Site-specific calibration of the wind shear models could further improve the accuracy of wind energy assessment by considering the site condition and the variability in the atmospheric stability.
... In fact, the wind shear coefficient (WSC) varies from place to place, depending on the roughness of the earth's surface. The 1/7 exponent is only suitable for use over smooth, grassy landscapes under semi-neutral weather conditions [19]. Thus, the extrapolation of wind speeds over vast areas according to a constant wind shear exponent often leads to uncertainty in the assessment of wind resources, causing significant errors in the complementarity estimation. ...
... With modern wind turbine technology, the optimum hub height could reach as high as 100 m or above to capture higher wind speeds [19]. However, most measured or reanalyzed wind data are available at 10 m above ground level. ...
... The power law, which was first developed by [31], is also known as the Hellman exponential law. This law uses the power law model to correlate wind speeds at two different heights as follows [19]: ...
Considering the spatial–temporal variation of renewable energy (RE) resources, assessment of their complementarity is of great significance for decision-makers to increase the stability of power output and reduce the need for storage systems. In this regard, the current paper presents a roadmap to assess the temporal complementarity patterns between wind and solar resources for the first time in Iraq. A new approach based on re-analyzed climate data, Landcover products, and geographical information system (GIS) is proposed. As such, renewable resource datasets are collected for 759 locations with a daily timescale over five years. Landcover classes are translated into wind shear coefficients (WSCs) to model wind velocity at turbine hub height. Then, the Pearson correlation coefficient (PCC) is applied to calculate the complementarity indices for each month of the year. Results of this investigation reveal that there are significant synergy patterns spanning more than six months in the southwestern regions and some eastern parts of Iraq. The highest complementarity is observed in March and December with a value of −0.70 and −0.63, respectively. Despite this promising potential, no typical temporal complementarity has been discovered that would completely eliminate the fluctuations of clean power generation. However, the synergistic properties yielded by this work could mitigate the reliance on storage systems, particularly as they cover important regions of the country. The proposed approach and tools can help improve the planning of renewable energy systems.
... One of the challenges of vertical extrapolation using the power law is that α usually varies with height [7,8,10,11]. This results in a reduced accuracy of the extrapolation process as α is usually assumed to be constant above the mast. ...
... It is well known that α strongly depends on all factors influencing the wind profile, such as atmospheric stability [10][11][12][13][14] and surface characteristics [7,12,14,15]. Thus, α is highly site-dependent and changes on a daily and annual course [10,11,13,16]-i.e., it strongly varies with meteorological conditions. ...
... It is well known that α strongly depends on all factors influencing the wind profile, such as atmospheric stability [10][11][12][13][14] and surface characteristics [7,12,14,15]. Thus, α is highly site-dependent and changes on a daily and annual course [10,11,13,16]-i.e., it strongly varies with meteorological conditions. In a first step, therefore, temporal patterns of the variation of the power law exponent with height are investigated. ...
This study investigates how short-term lidar measurements can be used in combinationwith a mast measurement to improve vertical extrapolation of wind speed. Several methods aredeveloped and analyzed for their performance in estimating the mean wind speed, the windspeed distribution, and the energy yield of an idealized wind turbine at the target height ofthe extrapolation. These methods range from directly using the wind shear of the short-termmeasurement to a classification approach based on commonly available environmental parametersusing linear regression. The extrapolation strategies are assessed using data of ten wind profiles upto 200 m measured at different sites in Germany. Different mast heights and extrapolation distancesare investigated. The results show that, using an appropriate extrapolation strategy, even a veryshort-term lidar measurement can significantly reduce the uncertainty in the vertical extrapolation ofwind speed. This observation was made for short as well as for very large extrapolation distances.Among the investigated methods, the linear regression approach yielded better results than the othermethods. Integrating environmental variables into the extrapolation procedure further increased theperformance of the linear regression approach. Overall, the extrapolation error in (theoretical) energyyield was decreased by around 50% to 70% on average for a lidar measurement of approximately oneto two months depending on the extrapolation height and distance. The analysis of seasonal patternsrevealed that appropriate extrapolation strategies can also significantly reduce the seasonal bias thatis connected to the season during which the short-term measurement is performed.
... Among these three classes, power law is the most extensively used technique for the extrapolation of wind speed data to known hub height (Khahro, et al., 2014;Belabes et al., 2015;Ohunakin et al., 2011;Adaramola et al., 2011;Tar, 2008;Dabbaghiyan et al., 2016;Hau, 2006;Ahmed, 2011). The Logarithmic law has also been proven to be a good method of wind profile estimation at hub height and it is the second most utilized method (Peterson and Hennessey Jr, 1978;Mikhail and Justus, 1979;Pneumatikos, 1991;Khan et al., 2004;Motta et al., 2005;Ray et al., 2006;Elkinton et al., 2006;Banuelos-Ruedas et al., 2010;Barthelmie et al., 2010;Cheggaga and Ettoumi, 2011;Dalila, et al., 2014;Hadi, 2015;Islam and Rehman, 2017;Li et al., 2018). ...
... Li et al. (2018) evaluated the benefits of incorporating site-specific wind data to calibrate the wind shear model parameters. Wind speed data collected by Light Detection and Ranging (LiDAR) system over a period of 2 years was used for the analyses. ...
... utilization of satellite derived landcover map to obtain roughness length. The study shows a positive correlation between measured roughness length and landcover fraction of roughness enhancing land use.Hasager et al. (2003) compared the effective roughness length values derived from satellite data to roughness length measured from wind speed data.Li et al. (2018) evaluated the benefits of incorporating site-specific wind data to calibrate the wind shear model parameters. Wind speed data collected by Light Detection and Ranging (LiDAR) system over a period of 2 years was used for the analyses. The atmospheric stability is found to have appreciable effects on the wind shear parameters, i.e. wind s ...
The rapid growth in population and corresponding energy demand of Pakistan play as a major contributor in its economical degradation especially due to the increase in Country’s dependency on imported petroleum products for power generation & transportation sectors. Therefore, inclusion of alternate/renewable energy option is inevitable to the current energy mix as the present conventional energy sources including fossil fuels (oil, gas & coal), nuclear and mega-hydro are unable to meet the growing energy demand of the Country. Among all the renewable energy options, wind energy for power generation for Pakistan is the most suitable option as it is a proven technology worldwide, plus it is free-of-cost and most environmental source of energy. To develop any wind energy based power generation facility, wind resource assessment is a must which is depends on number of meteorological, geographical, geomorphological and related variables. Terrain roughness is one of these essential wind resource assessment variables. Conventionally, terrain roughness is computed through the field surveys or by using field wind speed data, which is time-consuming and labor-intensified job. In the present research study, novel methods have been developed to identify and classify the terrain roughness of the study area (coastal districts of Karachi, Thatta and Badin of Sindh province) by utilizing satellite remote sensing analyses (RSA) techniques over satellite imageries. During the study, 6 landcover types including waterbodies, cropland, forest, grassland, built-up and barren areas have been identified and extracted using different remote sensing indexing techniques. Two new indexing techniques namely Advance Slope based Indexing Technique (ASIT) and Landsat 8 Forest Index (L8FI) have specifically been developed to perform the required data processing. The overall efficiency (OA) for each landcover identification remains above 95 % for the study area. Afterward, GIS-linked roughness class and roughness length maps of the study area have been developed which divide the whole area in 9 roughness zones including waterbodies, open terrain, grassland, agriculture land, few trees, suburban areas, woodland, city centers and dense forest. Since, this roughness classification technique only depends on satellite remote sensing, it is served as an alternate to conventional ground surveying methods and therefore is time-efficient & cost-effective.
... To address these, in our recent study [19], a 2-D spatiotemporal wind field reconstruction was achieved based on NS equations without model reductions, which followed the physicsinformed deep learning technique [20] where the 2-D NS equations were incorporated in the deep neural network (NN) structure. However, similarly to the above studies, our work [19] only investigated the reconstructions of the two-dimensional (2-D) flow field, while in practice the wind field is three-dimensional (3-D) and the variation of wind speed in vertical direction (e.g. the wind shear) has a clear impact on the wind turbine loading (in particularly its spatial variations), wind power generations and wind resource assessment [21,22]. Thus a big research gap still exists. ...
... This demonstrates that the proposed method can be applied to tackle yaw misalignment and turbine tilt control simultaneously. (4) The developed method can (i) infer the turbulent viscosity; (ii) accurately capture the propagation and evolution of the 3-D flow structures, i.e. the high/low speed zones; (iii) accurately predict the vertical wind shear, which is of great importance for various wind applications [21,22]; (iv) accurately predict the undisturbed wind speed at specific turbine blade locations, including the wind speed variation due to both turbine rotations and spatially evolving turbulence. This detailed blade-level wind prediction shows the great potential of the proposed method in assessing turbine blade load and its spatial variations, and in smart rotor design/control [29]. ...
In this work, a physics-informed deep learning model is developed to achieve the reconstruction of the three-dimensional (3-D) spatiotemporal wind field in front of a wind turbine, by combining the 3-D Navier–Stokes equations and the scanning LIDAR measurements. To the best of the authors’ knowledge, this is for the first time that the full 3-D spatiotemporal wind field reconstruction is achieved based on real-time measurements and flow physics. The proposed method is evaluated using high-fidelity large eddy simulations. The results show that the wind vector field in the whole 3-D domain is predicted very accurately based on only scalar line-of-sight LIDAR measurements at sparse locations. Specifically, at the baseline case, the prediction errors for the streamwise, spanwise and vertical velocity fields are 0.263 m/s, 0.397 m/s and 0.361 m/s, respectively. The prediction errors for the horizontal and vertical direction fields are 2.84° and 2.58° which are important in tackling yaw misalignment and turbine tilt control, respectively. Further analysis shows that the 3-D wind features are captured clearly, including the evolutions of flow structures, the wind shear in vertical direction, the blade-level speed variations due to turbine rotation, and the speed variations modulated by the turbulent wind. Also, the developed model achieves short-term wind forecasting without the commonly-used Taylor’s frozen turbulence hypothesis. Furthermore it is very useful in advancing other wind energy research fields e.g. wind turbine control & monitoring, power forecasting, and resource assessments because the 3-D spatiotemporal information is important for them but not available with current sensor and prediction technologies.
... where is the air density, A is the rotor swept area, v is the inflow wind speed and C p is the power coefficient of the machine, and it is the ratio of power extracted by the wind turbine rotor to the power available in the wind [22,23]. Due to the cubic relationship between the wind power and wind velocity, wind power output gets significantly altered by even slight variations in the inflow wind speed, which, for example, when gets doubled, the wind power output increases by eight times. ...
... As a rule of thumb, is often approximated as 1/7, or 0.143, and in that event, the power law is known as the 1/7th power law. The American Society of Civil Engineers suggests the value of wind shear coefficient as 1/4 for urban and suburban areas, 1/6.5 for open terrain and 1/9 for flat, unobstructed areas and water surfaces [23]. It has also been reported that the wind shear coefficient at the same terrain varies with the height, temperature, pressure, humidity, mean wind speed, wind direction, hour of the day, time of the year etc. [27][28][29]. ...
Prolonged wind speed assessment at higher altitudes is essential for wind energy estimation and planning. However, the erection and maintenance of tall wind measuring masts for this purpose cause many practical inconveniences from the engineering perspective. A rather simplified method often used for this task is to measure the wind profile at relatively lower altitudes and extrapolate the same to the required higher heights by empirical equations framed using hypothetical and experiential research. Such models often show errors due to the uncertainties caused by the complex nature of turbulent flows and the terrain. In this paper, we propose a new method of applying symbolic regression to the wind speed data over a short duration measured at a reference location to obtain a symbolic function capable of estimating wind speeds at higher altitudes using wind speed data at lower altitudes at different locations. Compared to the traditional power law method, the new method performs more accurately in different seasons at the reference as well as far away locations, achieving a maximum of 61.04% reduction in daily RMSE when analyzed with wind speeds averaged over 10-min intervals in this study. The new method opens up the possibility of wind resource assessment at higher altitudes at different locations by employing engineering-friendly shorter wind measuring masts.
... As a thumb rule, a is often assumed as 1/7, or 0.143, and the power law, in that case, is called the 1/7th power law. As per recommendations of the American Society of Civil Engineers, the value of a is taken as 1/9 for flat, unobstructed areas and water surface, 1/6.5 for open terrain and 1/4 for urban and suburban areas [51]. The value of a for a given site has been found to vary significantly with the variations in height levels, ambient temperature, land features, time of the day, the month of the year, etc. [52][53][54]. ...
Since wind is a fluctuating resource, the integration of wind energy into the electricity grid necessitates precise wind speed forecasting to maintain grid stability and power quality. Machine learning models built on different algorithms are widely used for wind forecasting. This requires a vast quantity of past wind speed data collected at the hub levels of the wind electric machines employed. Tall met masts pose a variety of practical issues in terms of installation and long-term maintenance, which will grow more challenging as next-generation wind turbines come with larger capacities and higher hub heights. In this paper, we propose four non-conventional methods for the time ahead forecasting of wind speed at a higher height by utilizing the wind speed data collected with relatively shorter wind measuring masts. We employ machine learning-based models and rely on the principle of interrelation between wind speeds at different altitudes in our investigations. Wind speed forecasts generated by the new methods at an altitude of 80 m above the ground level using wind speed data measured at lower altitudes of 50 m and 20 m are of industrially acceptable accuracy. The simplified physical requirements such methods demand far outweigh the marginal fall in prediction accuracy observed with these methods.
... According to the wind power output equation, accurate wind speed prediction is the most important factor affecting wind energy output [8,9]. With the increasing emphasis on renewable energy and the increasing proportion of wind power in renewable energy, the difficulty of predicting wind speed has become the most influential factor in wind energy resource assessment. ...
Accurate typhoon wind speed prediction is significant because it enables wind farms to take advantage of high wind speeds and to simultaneously protect wind turbines from damage. However, the wind characteristics of the typhoon are highly random, fluctuating, and nonlinear, which makes precise prediction difficult. One-year wind data collected from a wind farm on the southeast coast of China are employed in the study. The characteristics of the typhoon are analyzed, and a sensitivity study is carried out by comparing two groups of training datasets. This study proposes a hybrid approach that considers both the physical model and the artificial neural network (ANN) model to accurately predict the short-term typhoon wind speed. The variational mode decomposition (VMD) algorithm is selected to decompose wind speed, and the particle swarm optimization (PSO) method is employed to optimize the bidirectional, long short-term memory (Bi-LSTM) prediction model. The results show that the proposed PSO-VMD-Bi-LSTM has strong robustness for making uncertainty predictions and can be utilized to predict the wind speed of typhoons. This study demonstrates the potential of an innovative ANN method to predict wind speed during the typhoon period.
... In the wind energy sector, the Weibull distribution is an often used single parametric distribution (Wais 2017a, b). For instance, the Weibull model has been utilized for estimating the wind power corresponding to the wind turbine capacity factor (Chang and Tu 2007;Shu et al. 2015;Arslan et al. 2020), assessing the performance of wind energy systems (Celik 2003(Celik , 2006, and mapping of wind resources and properties (Faghani et al. 2018;Li et al. 2018;Schallenberg-Rodríguez and Montesdeoca 2018;Silva et al. 2021). Some of the advantages that have made the Weibull distribution popular in the wind industry include its flexibility, the fact that it has only two parameters, the ease with which its parameters are estimated, and its closed form expression (Carta et al. 2009). ...
In this study, the Weibull distribution with various numerical estimation methods is utilized for the assessment of wind energy potential in Mersing and Port Dickson, Malaysia, by considering different monsoon seasons, i.e., northeast and southwest monsoons. This assessment is conducted based on hourly wind speed data obtained from the Malaysian Meteorological Department and the Department of Environment, Malaysia. A total of 28 numerical estimation methods are presented, and their performances are first investigated through a Monte Carlo simulation. The results of the Monte Carlo simulation indicate that no particular method outperforms all other methods. In different settings, i.e., different sample sizes and shape parameters, certain methods are more efficient than others in estimating the Weibull parameters. Based on the simulation results, 24 out of 28 numerical estimation methods are utilized for wind energy potential assessment in Mersing and Port Dickson. The performance efficiency of all the considered methods is evaluated using an integrated approach comprising four goodness of fit criteria, the modified Kolmogorov − Smirnov, modified Anderson − Darling, modified Cramér–von Mises, and power density error. Using this integrated approach allow us to obtain a single-value goodness of fit measure known as the global score to determine the best method. Based on the best fitted Weibull model, the wind characteristics in both sites are investigated. The wind characteristics during northeast monsoon is found to be favorable for wind energy generation in Mersing with the northeast (0 − 40°) as the prominent wind direction. However, small-scale wind energy is the best option for wind energy development in Mersing. On the other hand, the Port Dickson site demonstrates poor wind characteristics, indicating that this area is not favorable for wind energy development.
... In order to make good use of the incoming wind and to mitigate the impact of the disturbance, wind speed measurement technologies, such as light detection and ranging (LIDAR) [1], have been developed in recent years. Extensive research efforts have since then been spent in the measurement analysis of LIDAR [2,3] and their applications in wind turbine control [4,5] and wind resource assessment [6,7]. However, LIDAR can only provide wind speed measurements at sparse spatial locations along the laser beam. ...
Spatiotemporal wind field information is of great interest in wind industry e.g. for wind resource assessment and wind turbine/farm monitoring & control. However, its measurement is not feasible because only sparse point measurements are available with the current sensor technology such as LIDAR. This work fills the gap by developing a method that can achieve spatiotemporal wind field predictions by combining LIDAR measurements and flow physics. Specifically, a deep neural network is constructed and the Navier–Stokes equations, which provide a good description of atmospheric flows, are incorporated in the deep neural network by employing the physics-informed deep learning technique. The training of this physics-incorporated deep learning model only requires the sparse LIDAR measurement data while the spatiotemporal wind field in the whole domain (which cannot be measured) can be predicted after training. This study, which can discover complex wind patterns that do not present in the training dataset, is totally distinct from previous machine learning based wind prediction studies which treat machine learning models as “black-box” and require the corresponding input and target values to learn complex relations. The numerical results on the prediction of the wind field in front of a wind turbine show that the proposed method predicts the spatiotemporal flow velocity (including both downwind and crosswind components) in the whole domain very well for a wide range of scenarios (including various measurement noises, resolutions, LIDAR look directions, and turbulence levels), which is promising given that only line-of-sight wind speed measurements at sparse locations are used.
... 23 To estimate E values at a specific site, wind speed measurements in at least two different heights are required. Moreover, these measurements should be carried out over a period as long as possible to account for the temporal variability of E. 24 For instance, using a Light Detection and Ranging (LIDAR) system, long-term U observations were obtained in Singapore 25 and Ohio (USA) 26 to calculate spatiotemporal explicit E values. Another approach to calculate spatiotemporal explicit E values is to use U measurements from a tower equipped with U measurement sensors. ...
The power law is most often applied to extrapolate the near-surface wind speed to the wind turbine hub height. Due to variations of the meteorological conditions , the power law exponent varies over time. Usually, no long-term wind speed measurements from multiple heights are available which would allow time-dependent and spatially explicit power law exponent estimations. Instead, often the mean of the power law exponent or a constant value of 0.14 is assumed. The goal of this study was to quantify the error in wind potential assessments resulting from applying the mean of the power law exponent or a value of 0.14. The data base for this study are the hourly wind speed time series at 10 and 100 m above ground available for the period 2007 to 2018 from the ERA5 reanalysis project at a global 0.25 × 0.25 grid. The errors in the estimation of the wind power density and the capacity factor were calculated. It was found that, onshore, the global median of the absolute percentage error related to the wind power density using the mean of the power law exponent is 7.5%. Assuming a constant value of 0.14, the power law is less accurate (absolute percentage error: 37.1%). For the estimation of the capacity factor the absolute percentage errors are 5.5% and 36.9%. Based on the results of this study, the use of time-dependent and spatially explicit power law exponents is suggested. In the absence of long-term wind speed measurements from multiple heights, the results provide a comprehensive global overview of the errors to be expected from using the mean of the power law exponent or assuming a value of 0.14. In many regions where the wind resource is abundant, using the mean of the power law exponent only leads to minor errors in capacity factor estimation. There, the assessment of wind resources with small errors is possible, even in the absence of long-term wind speed measurements at different heights.
... Such a strategy was employed in various previous studies. For example, both Li and Yu (2017) and Li et al. (2018) applied 10-min average wind velocities to assess the wind resource over Lake Erie (USA), and Drew et al. (2013) investigated the wind velocity profile during an 8-month observation period based on 1 h average data. The duration of ascent of the GPS sonde from the ground surface to the height of 270 m can be expressed as t GPS . ...
This study undertook verification of the applicability and accuracy of wind data measured using a WindCube V2 Doppler Wind Lidar (DWL). The data were collected as part of a field experiment in Zhoushan, Zhejiang Province (China), which was conducted by Shanghai Typhoon Institute of China Meteorological Administration during the passage of Super Typhoon Lekima (2019). The DWL measurements were compared with balloon-borne GPS radiosonde (GPS sonde) data, which were acquired using balloons launched from the DWL location. Results showed that wind speed measured by GPS sonde at heights of < 100 m is unreliable owing to the drift effect. Optimal agreement (at heights of > 100 m) was found for DWL-measured wind speed time-averaged during the ascent of the GPS sonde from the ground surface to the height of 270 m (correlation coefficient: 0.82; root mean square (RMS): 2.19). Analysis revealed that precipitation intensity (PI) exerts considerable influence on both the carrier-to-noise ratio and the rate of missing DWL data; however, PI has minimal effect on the wind speed bias of DWL measurements. Specifically, the rate of missing DWL data increased with increasing measurement height and PI. For PI classed as heavy rain or less (PI < 12 mm·h−1), the DWL data below 300 m were considered valid, whereas for PI classed as a severe rainstorm (PI > 90 mm·h−1), only data below 100 m were valid. Up to the height of 300 m, the RMS of the DWL measurements was nearly half that of wind profile radar (WPR) estimates (4.32 m·s−1), indicating that DWL wind data are more accurate than WPR data under typhoon conditions.
... The exponent a is a highly variable parameter and often changes from less than 1/7 to more than 1/4 depending on the terrain type [63,65]. From Eqs. (8) and (9), the exponent a can be determined using the data of v 1 and v 0 as follows: ...
This study introduced an investigation to evaluate spatial and temporal variations of the wind potentialfor the techno-economic feasibility analysis of the energy production in Northwest Africa (a case ofMauritania). The present research was introduced as thefirst attempt to appraise the spatio-temporalinfluence of the wind energy production in Mauritania, and particularly focused on analyzing sea-sonal, daily, and turbulence index on the available wind potential in this region. Data measured every10 min over one-year period were collected from eight sites (with three different height levels) locatedmainly on the west coast of Mauritania, and the annual average of the wind characteristics weredetermined. Power density, Weibull parameters, turbulence indices, and power-law exponents wereestimated based on seasonal and daily wind analyses. Comparative studies of the power density potentialof the wind on different sites were also conducted while investigating the influence of seasons, height ofthe wind turbines, wind directional distributions, and daily characteristics. Investigations regarding thegenerated energy from the wind turbine and the related capacity factor were performed based on eightparticular wind turbines (Ecot�ecnia-44, Ecot�ecnia-48, Nordex-N50, Neg-Micon, Vestas-V66, Power-Wind-90, Bonus-2MW, and Vestas-V90). Results showed that the power-law exponent was higher wherethe turbulence index was low. The analysis of the power distribution allowed concluding on the energyavailability according to the influent variables. Findings of the present techno-economic analysis (forelectricity generation from the planned wind energy systems) revealed that the best cost of energy(ranging from 0.0187V/kWh to 0.0596V/kWh) was observed for the wind turbine Ecot�ecnia-48 on all sites.
... Switching conventional energy resources to renewable energy is beneficial for the environment and the economy (Petrakopoulou, 2016). Wind energy is one of the most promising renewable energy resources, being plentiful, widely distributed, and clean has been utilized extensively (Li et al., 2018). By the end of 2019, the global cumulative installed wind capacity is 651 GW, with an increase of 17% comparing to 2018 (GWEC, 2020). ...
The hybrid monopile foundation is an alternative for offshore wind turbines. The parametric study has been performed through a series of centrifuge tests for the optimal design of the hybrid foundation. The wheel diameter, wheel thickness, and pile length are considered in the analysis. The lateral capacity of the hybrid monopile foundation increases with the wheel diameter and tends to accelerate; it increases linearly with the wheel thickness and pile length. The influence of the wheel diameter is more pronounced compared to the other parameters. The hybrid monopile demonstrates an enhanced performance compared to the monopile and the single-wheel. The improvement is more significant at small pile lengths. The hybrid monopile shows its advantages in reducing the pile length. It has great potential in reducing capital costs. An analytical method is proposed by scaling the individual capacity of the pile and the wheel. A design chart for the scale factor is suggested. The calculation is applicable for determining the initial dimension of the hybrid monopile foundation, and the ultimate lateral capacity is assessed.
... There were also other research studies to assess the potential of wind and its economic viability using various techno-economic approaches. Such studies include: techno-economic analysis of small-scale wind energy production in Iran (Hosseinalizadeh et al., 2017), techno-economic evaluation for wind energy modeling using sensitivity study and the Monte Carlo method (Afanasyeva et al., 2016), and enhancing the accuracy of wind resource assessment using a wind shear model (Li et al., 2018). One of the other extensive studies in the literature dealt with a new methodology for estimating global wind energy potential with consideration of land use and water depth (Dupont et al. ...
... The first commercial wind farm in the U.S., the Block Island wind farm, started to produce electricity in December 2016. The first freshwater offshore wind farm in the U.S. will be erected in Lake Erie with six wind turbines at the end of 2018 [4]. The wind farm is expected to have a generating capacity of 20.7 MW, which will be enough to power 6000 to 8000 residence homes in the city of Cleveland. ...
Wind energy is a promising source of renewable energy and is projected to shift to offshore areas increasingly. Monopile foundation is one of the most commonly used foundations for offshore wind applications with the priority in load bearing capability and initial cost. This study describes an innovative monopile foundation, which institutes a creative strategy over the traditional large diameter monopile foundation to achieve higher axially load bearing capacity. This is achieved by adding a restriction plate inside the pile to intensify the soil plug effect. This design is based on the soil plug mechanism, and the arching effects and plug resistance mobilizations are considered. In this study, an extensive amount of geotechnical centrifuge experiments was conducted to analyze the bearing behaviors of the innovative monopile with restriction plates. The pile with 1-hole restriction plate and the pile with 4-hole restriction plate are considered to discuss effects of the plate shape. Twelve models with different diameters and restriction plate types are investigated. The traditional open-ended and close-ended piles are included for comparisons. The static tests are conducted in saturated silica sand first to determine the ultimate bearing capacity of the innovative pile, after which the cyclic tests are performed. The innovative pile is proved to provide a larger bearing capacity than the pipe pile. An analytical method is proposed to estimate the capacity of the innovative pile. The study aims to develop the design code for innovative piles and provide design reference to large-scale offshore wind turbine projects.
... The wind energy experiences rapid growths with its advantages of the abundant reserve, low environmental impact, and cost efficiency. The average installation rate for the wind plant is about 28% during the last decades to achieve the goals of lower carbon emission and longer energy service life [1]. The coastal wind resource has the characteristic of plentiful, visual and noise free, and land saving [2]; it has great potential to become a significant energy supply for local applications. ...
Some offshore wind farms are built in seismically active areas. The offshore wind turbine (OWTs) are high-rise structures and sensitive to lateral failures during the earthquake. In this study, an innovative hybrid monopile foundation is proposed for OWTs. A series of centrifuge shake table tests is conducted to investigate the seismic response and liquefaction characteristics of the hybrid monopile foundation. Mechanisms of the seismic behavior of soil, lateral displacements of the wind turbine, and structural settlements are evaluated. Centrifuge test results indicate that the liquefaction around the hybrid monopile foundation is weakened compared to the traditional single pile. The soil partially keeps its strength and stiffness during the shaking due to the higher confining stress. The lateral stability of the system is enhanced. OWTs with the hybrid monopile foundation tends to settle more during the earthquake due to the soil-structure interaction and the static bearing induced soil shearing. Two types of hybrid monopile foundations are constructed with different weights and materials, which are predominant influence factors for their seismic response. This study aims to investigate the seismic behavior of the hybrid monopile foundation for OWTs during earthquake events and provide references for practical designs.
... Renewable energy sources consist energy types as wind, solar, solar thermal, biomass, hydro, geothermal, and ocean sources (marine currents, ocean thermal energy, comprising tidal, wave and tapping the salt gradient). Wind energy has grown very rapidly among other renewable energy sources in particular in meeting electricity requirement of the world around over the last two decades since wind energy is sustainable, local and renewable, moreover, when the other renewable energy technologies are examined, wind energy has a high return on investment [4]. ...
... In the field of the evaluation of the wind potential, the scientific literature proposes recent studies on different areas of the globe; among them: the study of wind resource assessment offshore the Atlantic Iberian coast with the WRF model in Spain [7]; Use of spatio-temporal calibrated wind shear model to improve accuracy of wind resource assessment in the USA [8]; 3D statistical mapping of Germany 's wind resource using WSWS [9]; Wind resource assessment and economics of electric generation at four locations in Sinai Peninsula, Egypt [10]; Multi criteria decision analysis for offshore wind energy potential in Egypt [11]; Wind energy characteristics and wind park installation in Shark El-Ouinat, Egypt [12]; Offshore wind resource assessment and wind power plant optimization in the Gulf of Thailand [13]; Offshore wind resource assessment of Persian Gulf using uncertainty analysis and GIS [14]; Wind resource assessment using SODAR and meteorological mast -A case study of Pakistan [15]; Wind resource potential assessment using a long term tower measurement approach: A case study of Beijing in China [16]; Validation of wind resource in 14 locations of Nepal [17]; Statistical learning approach for wind resource assessment in United Kingdom [18]; Wind resource assessment of Northern Cyprus in Turkey [19]; A new methodology for urban wind resource assessment in Portugal [20] et. The wind resource assessment around coastal areas of the Korean peninsula [21][22]. ...
The aim of this paper is to evaluate the wind potential in southwestern Algeria, according to months, seasons and entire years and contribute to the updating of the wind map in Algeria at 10 m from the ground, using hourly data for wind collected over a period of more than 30 years. The wind data analysis was done using the Weibull function at 10 m from the ground. Then we did the statistical analysis, which includes several fundamental properties, such as Weibull parameters, mean wind speed and average power density. The results give the city of Tindouf as the one with the highest annual average speed with 5.39 m/s at 10 m from the ground. As for the temporal study, it gives that spring is the best windy period.
... The offshore wind industry experiences rapid growth in recent years [1]. Normally, wind resources in offshore areas are stronger and more uniform. ...
Some large capacity offshore wind turbines are constructed in seismically active areas. The occurrence of soil liquefaction during an earthquake can result in severe failures of the offshore wind turbine. The seismic response of the structure and the failure mechanism of the soil-structure interactions are necessary to investigate. In this study, the seismic response of an innovative hybrid monopile foundation is investigated through a series of centrifuge tests. The seismic performance of the combined system of the superstructure, foundation, and soil are demonstrated. Five hybrid foundation models are tested by considering the influence of the foundation thicknesses and diameters, and a monopile foundation is tested for comparison. Centrifuge test results reveal that the hybrid monopile foundation is effective in reducing the lateral displacement during the shaking. In the saturated condition, soil keeps its strength and stiffness beneath and adjacent to the foundation. The hybrid foundation system tends to settle more due to the larger shear stress caused by the soil structure interactions. Influences of the wheel specifications are illustrated. The foundations with larger thicknesses lead to smaller lateral displacements and lower tendencies of liquefaction, but the settlements are intensified. The larger diameter foundation provides a longer drainage path for the excess pore water pressure. With a similar weight, the structure settles less during the earthquake.
... Wind energy is one of the most promising renewable energy currently (Li, Wang, & Yu, 2018). In recent years, the offshore wind industry experiences rapid growth. ...
Offshore wind farms that are constructed in seismic active areas have high requirements for their foundation designs. The earthquake induced soil liquefaction may cause severe damage to the whole structure and consequently influence the operation of wind turbines. Monopile foundation has been widely used currently, and based on this type of foundation, an innovative foundation is proposed. The concept of hybrid monopile-friction wheel foundation is a combination of a monopile and a gravity base, which is regarded as an improved and reinforced design of the original monopile. A series of centrifuge tests was performed to investigate the seismic response of the hybrid foundation system in sandy soil. Two types of hybrid monopile-friction wheel foundations, which contain solid wheel or gravel wheel, were tested, and the results were compared with the original monopile foundation. Accelerations, pore water pressure ratios, and settlements were recorded to illustrate the results. Each tested model exhibited distinct behaviors during the earthquake. It was found that the friction wheel further reinforced the foundation soil, and the liquefaction tendency is lower. The results validate this new concept of offshore wind hybrid foundation systems.
... To obtain a complete wind energy conversion model, the spatial effects on a wind farm should be taken into account. Among the spatial effects, the wake effect [16,17] represents the decrease in the wind speed in the wind direction, the wind shear [18][19][20] represents the wind speed change caused by undulating terrain, the tower shadow [19,20] represents the airflow disturbance caused by the WTG tower, and the time-lag effect [21,22] represents the time postponement of the wind moving over a certain distance. The spatial effects could also lead to non-synchronous actions of the pitch angle controllers (PACs) in individual WTGs when the wind fluctuates over the rated wind speed of the WTG. ...
The correct electric power fluctuation process of a wind farm is essential for the operation and studying of the power grid integrated with wind power. A complete wind energy conversion model, that should take both the spatial effects and the overall effect of all the pitch angle controllers into account, is required to convert the wind fluctuation into the electric power fluctuation. Although some equivalent models have been proposed in previous studies, there is no simple solution for equivalently modeling the non-synchronous actions of the pitch angle controllers in individual wind turbine generators. This study found the relationship between the overall effect of all the pitch angle controllers and the spatial effects of the wind farm and presented a theoretical derivation of the frequency-domain equivalent modeling method. The proposed modeling method is the simplest way to obtain the equivalent model of the complete wind energy conversion process of a wind farm with consideration of all the spatial effects and the overall effects of all the pitch angle controllers. The only input signal of the proposed equivalent model is the speed of the wind before entering the wind farm, which is called the “original incoming wind speed” and the only output signal is the total power of the wind farm. In this proposed equivalent modeling method, a discrete transfer function for representing both the wind energy conversion process, and all the spatial effects of a wind farm, can be obtained, first through a wind process below the rated wind speed. Second, a compensation factor for calculating a “compensation wind speed” of the original incoming wind can be obtained through a wind process that partly exceeds the rated wind speed. Using this compensation wind speed, a compensation power with negative values can be obtained to represent the total reduced power caused by the pitch angle controllers in individual wind turbine generators. A frequency-domain equivalent model has been identified and validated by field measurements of an actual wind farm. The normalized root-mean-squared error of the model is less than 8% over the entire wind process, and the maximum error of the power ramp rate in 10 min is less than 5 MW. Finally, an example is provided to demonstrate the online use of the proposed model to convert the forecasted wind speed into output power of a wind farm in ultra-short-term wind power forecast.
As the increasing hub height of modern wind turbine, wind shear model is generally adopted as a useful tool to extrapolate wind speed available to higher levels for wind power assessment. This paper conducts a data-driven study to examine the power law model in assessing the wind power in forested regions featured with seasonally-varied roughness. Specifically, wind speeds are observed using a laser lidar mounted in forested regions in the northeast of China, ranging from 80 m to 200 m with a constant interval of 20 m. Subsequently, comparisons are made between the wind profiles established based on the power law model and field observations. The exponents used include the empirical exponents and the fitted exponents, the former are derived from the standard codes, and the latter are fitted based on the seasonal and annual data collections. Results indicate that the power law model is unable to fully capture the vertical distribution of wind speed in forested regions, it is the exponent that plays a decisive role influencing the reliability representing the real wind profile. Compared with the empirical exponents, the seasonally-fitted exponents exhibits much better suitability for wind speed extrapolation in forested regions, as well as for the wind power assessment.
Harvesting renewable wind energy is among the most cost-efficient means of reducing carbon emissions and achieving carbon neutrality. However, wind resource is susceptible to climate change impacts since the global temperature increase will reshape atmospheric circulation patterns. To facilitate the evaluation of fine-scale wind energy potential under climate variability, this paper proposes and validates a multi-model and multi-method ensemble wind resource projection approach. Then this approach is utilized to investigate the future variation of wind resources in Hong Kong based on the combined use of global climate models from Coupled Model Intercomparison Project Phase 6 and long-term observations from meteorological stations. It is found that there is a significant increase in future wind resources during summer, while a remarkable decline is projected during autumn. Nevertheless, the variations of wind resources in winter and spring are relatively insignificant. The outcomes of this study are expected to offer a framework for fine-scale wind resource assessment under climate change, and facilitate the economic and risk assessments of future wind farm projects.
Waves simulated by a high-resolution calibrated SWAN model of China adjacent seas over a long-term period (from 1996 to 2020) are utilized to investigate the characteristics of offshore wind power construction efficiency. The model is forced by validated ERA5 wind data, which shows better agreement with ICOADS observations than CCMPv2 and CFSR&CFSv2. During the calibration process, we focused on wind input, whitecapping, and bottom friction terms and on whitecapping dissipation and bottom friction coefficients. Statistical error analysis of the calibration results demonstrates that the underestimation of significant wave height simulated with default settings has been corrected and the model performance has been improved in the study area. The assessment results revealed that construction efficiency has significant spatial and temporal variation characteristics. Annual and monthly averaged workloads of pile foundation construction and wind turbine installation are distinctly different since their specific weather restrictions and durations. Generally speaking, weather conditions from May to August are more favourable for offshore construction. In winter, offshore construction is strongly suggested to be stopped especially for sea areas of Fujian and Guangdong. For selected projects, the key environmental factors affecting offshore operation are different, which should be considered to improve construction efficiency and further decrease cost.
Wind energy is a mature and cost-effective solution to greenhouse gas emission reduction and climate change mitigation. Wind resource assessment is the most pivotal activity before wind farm development since it determines the bankability of the wind project. Based on the joint use of a Doppler wind sodar (sonic detection and ranging) system and a microwave radiometer, this paper investigates the wind and thermal characteristics and wind energy resources in a coastal region of Hong Kong. First, wind climatology and variability, as well as their correlation with large-scale and local meteorological and geographical conditions, are analyzed and discussed. Then, statistical distributions of wind speed are evaluated, and the goodness-of-fit of the Weibull, Kappa, Wakeby, Normal-Weibull mixture, and Weibull-Weibull mixture distributions is assessed. Subsequently, the probability distribution and variation of atmospheric stability are examined, and their effects on vertical wind shear, wind profile, and wind speed at turbine hub height are revealed. Lastly, the spatiotemporal variation of wind power density is investigated with attention paid to air density. The results presented in this paper are expected to offer insights into coastal wind and thermal characteristics, provide references for vertical extrapolation of wind speed and wind turbine load evaluation, and facilitate wind farm development in coastal regions.
Wind speed is the most important input of wind energy conversion systems and has higher values at high altitudes. Therefore, tall wind measurement masts are used in the wind power industry to determine the wind speed at high altitudes. However, this situation brings many engineering problems (cost escalation, de-erection and re-erection of the masts due to the failure of the anemometer and sensors, lightning strikes, mechanical failures etc.). In this study, it is aimed to estimate the data at the hub height levels of the proposed wind power generators by placing shorter wind masts as a suitable alternative for longer masts. Therefore, we proposed an innovative model that uses multigene genetic programming to estimate wind speed at high altitudes. According to the power and logarithmic law, analysis results show that root mean square error (RMSE) values were decreased with proposed method in the wind speed estimation, 58.62% and 58.77% respectively.
The joint probability density function can quantitatively describe the statistical characteristics and correlation features between wind speed and shear; this forms the theoretical basis for assessing height-dependent wind energy. Here, a nonparametric copula-based joint probability model of wind speed-wind shear is developed to assess height-dependent wind energy in China. Utilizing the transformation method and optimal bandwidth algorithms, a nonparametric copula model for wind speed/wind shear correlation analysis is proposed. Joint probability density models of wind speed and wind shear are then constructed. Various copula and marginal density models (including single parametric, mixture parametric, and kernel density estimation models) are evaluated at the regional scale. The nonparametric copula model exhibits remarkable superiority and is therefore deemed to be more suitable for wind speed/wind shear correlation analysis and joint probability modeling. When assessing height-dependent wind energy, the average distributions of wind turbine power output and capacity factor are obtained across mainland China. Additionally, this model could accurately analyze variations in wind power density with respect to hub height. These results can effectively facilitate accurate micro-site selection, thereby economically benefiting wind farms.
Accurate wind speed estimates at turbine hub height are critical for wind farm operational purposes, such as forecasting and grid operation, but also for wind energy assessments at regional scales. Power law models have widely been used for vertical wind speed profiles due to their simplicity and suitability for many applications over diverse geographic regions. The power law requires estimation of a wind shear coefficient, α, linking the surface wind speed to winds at higher altitudes. Prior studies have mostly adopted simplified models for α, ranging from a single constant, to a site-specific constant in time value. In this work we (i) develop a new model for α which is able to capture hourly variability across a range of geographic/topographic features; (ii) quantify its improved skill compared to prior studies; and (iii) demonstrate implications for wind energy estimates over a large geographical area. To achieve this we use long-term high-resolution simulations by the Weather Research and Forecasting model, as well as met-mast and radiosonde observations of vertical profiles of wind speed and other atmospheric properties. The study focuses on Saudi Arabia, an emerging country with ambitious renewable energy plans, and is part of a bigger effort supported by the Saudi Arabian government to characterize wind energy resources over the country. Results from this study indicate that the proposed model outperforms prior formulations of α, with a domain average reduction of the wind speed RMSE of 23–33%. Further, we show how these improved estimates impact assessments of wind energy potential and associated wind farm siting.
Kulkarni, VinayJoshi, YashwantManthalkar, RamchandraBrain–Computer Interface (BCI) is a vibrant topic in rehabilitation engineering. It is essential to have informative features and practical classification algorithms for the appropriate communication between humans and machines. This paper focuses on improving accuracy in detecting elbow movements and analyzing the effect of kinematic movement variability over the sensory-motor cortex with 10–20 EEG system. The EEG data from healthy volunteers was acquired by training them for a proposed protocol. These healthy volunteers were asked to play a PC game intended for rehabilitation with his/her elbow movement utilizing the ArmeoSpring treatment instrument. The input raw EEG signal is passed through 8–30 Hz bandpass filter. The Common Spatial Pattern (CSP), Event-Related Desynchronization (ERD)/Synchronization (ERS), and Autoregressive Moving Average (ARMA) modeling features are estimated and tested with SVM classifier. The proposed framework can differentiate and classify the kinematic movements of the elbow with an average accuracy of 84.61, 92.77, and 97.26% for CSP, ARMA, and ERD/ERS features, respectively. The experimental results on the proposed dataset demonstrate that the combination of feature extraction techniques and classifiers improves the classification performance, which would benefit the BCI rehabilitation community.
In general, aerodynamic barriers can be affected by wind gradients in wind flows. Besides, the wind speed will increase with increasing altitude above the ground. Flow near the surface encounters obstacles that can reduce wind speed. Technically, the reduction in a speed close to the surface is a result of the function of surface roughness. In addition, the wind speed is very different for different types of terrain. However, this condition will also give random effects on the vertical and horizontal velocity components in the main flow direction, as discussed in these studies. These works are subjected to evaluate the wind gradient impact on the wind energy profile considering the ground launch on various levels. Specifically, wind gradients are modeled and approached by shifting vertical velocities which are designed in various exponential coefficients that refer to the type of surface. The results show that the gradient has a significant effect on changes in wind speed patterns that are in line with potential changes in the generated energy. The ground launch also leads to the energy profile as presented on the speed at the high.
A review spanning across a 40-year period (1978–2018) and including a total of 332 applications has been addressed on theoretical and empirical wind resource extrapolation models applied in wind energy, which can be grouped into three main families: (i) the logarithmic models; (ii) the Deaves and Harris (DH) model; (iii) the power law (PL). Applied over 96 very heterogeneous locations worldwide, models have been tested against observations at upper extrapolation height and assessed by location characteristics, extrapolation range skills, and application economical advantages.
The logarithmic models can nowadays be considered unsuitable for extrapolating wind resource to hub height of current multi-MW WTs, mainly because exhibiting a limited extrapolation range capability (about 10–50 m median bin). Finer scores in extrapolating wind resource (mean absolute bias of 3.3%) and in predicting energy output (10.1%) were achieved by the DH model, also showing remarkable extrapolation range skills (10–80 m median bin). However, although among the most economical and forward-looking solutions, its need for accurate z0 assessment and u* observations resulted so far in great limitations to its large-scale application for wind energy purposes (less than 1%). Eventually, the PL confirmed the most reliable – and largely most commonly used (73.5%) – approach for wind energy applications. Out of the plethora of PL models developed in the literature, the PL(α)-αlower and the PL(α)-αI were the finest in predicting both extrapolated wind resource (mean absolute error of 4% and 4.4%, respectively) and energy output (8.9% and 5.5%), also exhibiting extrapolation range skills meeting modern WTs requirements. By contrast, the PL using α = 1/7 returned among the worst scores, yet resulting – since the simplest – the solution most frequently applied (19.6%). This study also demonstrated that extrapolation tools requiring the most expensive instrumentation equipment do not necessarily return the finest scores.
The uncertainty is a significant characteristic of wind speed in wind engineering field. Especially, it has brought much more problems to the grid in safe and efficient utilization of large scale wind power. And there is urgent need of systematic and perfect models that can describe windspeed uncertainty in grid scheduling and controlling. In this paper, a universal power-law model is proposed for properly depicting the uncertainty of both wind speed and wind power. According to the turbulence nature of wind uncertainty, the uncertainty model of wind speed is firstly obtained by using wavelet multi-scale transform algorithm for its tight supporting characteristic, which is more reasonable than the traditional algorithm of getting the mean valve and the variance valve of the time series. And the turbulent intensity model is further improved by a power-law model, which is suitable for much more kinds of turbulence on complex geographical conditions than that proposed in current international IEC standard with the sufficient actual data. In physically speaking, the model improvement with three parameters is consistent with turbulence development mechanism. Moreover, the uncertainty modeling method of wind power is developed based on the universal power-law model, which is not only suitable for the power of single wind turbine, but also suitable for the power of whole wind farm. It’s very importance that the wind speed uncertainty model is extended to model the power uncertainty of wind turbine and farm, in especial its or their power output is usually limited for human adjustment control. It has a certain significance to the real-time dispatch and optimal control of the renewable energy power system.
A boundary layer scaling (BLS) method for predicting long-term average near-surface wind speeds and power densities was developed in this work. The method was based on the scaling of reference climatological data either from long-term average wind maps or from hourly wind speeds obtained from high-resolution Numerical Weather Prediction (NWP) models, with case study applications from Great Britain. It incorporated a more detailed parameterisation of surface aerodynamics than previous studies and the predicted wind speeds and power densities were validated against observational wind speeds from 124 sites across Great Britain. The BLS model could offer long-term average wind speed predictions using wind map data derived from long-term observational data, with a mean percentage error of 1.5 % which provided an improvement on the commonly used NOABL (Numerical Objective Analysis of Boundary Layer) wind map. The boundary layer scaling of NWP data was not, however, able to improve upon the use of raw NWP data for near surface wind speed predictions. However, the use of NWP data scaled by the BLS model could offer improved power density predictions compared to the use of the reference data sets. Using a vertical scaling of the shape factor of a Weibull distribution fitted to the BLS NWP data, power density predictions with a 1 % mean percentage error were achieved. This provided a significant improvement on the use of a fixed shape factor which must be utilised when only long-term average wind speeds are available from reference wind maps. The work therefore highlights the advantages that use of a BLS model for wind speed and NWP data for power density predictions can offer for small to medium scale wind energy resource assessments, potentially facilitating more robust annual energy production and financial assessments of prospective small and medium scale wind turbine installations.
This paper describes the feasibility analysis of an innovative, extensible blade technology. The blade aims to significantly improve the energy production of a wind turbine, particularly at locations with unfavorable wind conditions. The innovative ‘smart’ blade will be extended at low wind speed to harvest more wind energy; on the other hand, it will be retracted to its original shape when the wind speed is above the rated wind speed to protect the blade from damages by high wind loads. An established aerodynamic model is implemented in this paper to evaluate and compare the power output of extensible blades versus a baseline conventional blade. The model was first validated with a monitored power production curve based on the wind energy production data of a conventional turbine blade, which is subsequently used to estimate the power production curve of extended blades. The load-on-blade structures are incorporated as the mechanical criteria to design the extension strategies. Wind speed monitoring data at three different onshore and offshore sites around Lake Erie are used to predict the annual wind energy output with different blades. The effects of extension on the dynamic characteristics of blade are analyzed. The results show that the extensive blade significantly increases the annual wind energy production (up to 20% to 30%) with different blade extension strategies. It, therefore, has the potential to significantly boost wind energy production for utility-scale wind turbines located at sites with low-class wind resource.
Characterisation of atmospheric turbulence is a central step prior to the construction of long-span bridges. The present paper investigates the potential of a long-range scanning pulsed coherent lidar to measure the wind field at the inlet of a fjord. The wind velocity data acquired by the lidar is compared to the data recorded by sonic anemometers installed on the bridge. The focus is on the ability of the lidar to capture single and two-point statistics of turbulence in complex terrains. Satisfying results are obtained with the Line-of-Sight scanning mode, which provides the best sampling frequency (1 Hz). The low temporal resolution and the low signal-to-noise ratio for the Range Height Indicator and the Plan Position Indicator scanning modes are important limiting factors in the pilot study performed. Better results may be achieved for a lidar measurement set-up which is adapted specifically for investigating coherence.
Accurate and reliable prediction of wind energy production is important for the operational management of wind farm as well as ensuring the stability of electrical grid integrated with renewable wind energy. This paper describes a new method that aims to reliably predict wind energy production based on weather forecast data. With this new method, an aerodynamic model is firstly used to predict the distribution of wind speed with elevation by use of 24-hour ahead forecasted wind speed data at three-hour intervals (which is available with common weather forecast provider). The aerodynamic model considers the influence of factors such as ground topology, types of land cover, etc. on the wind speed distribution. Based on this model, wind speed at 10-minutes time interval is simulated, which is subsequently used together with the turbine production curve to predict energy production within the next 24 hours. The model and procedures for forecasting wind energy production are validated on a 100kW utility scale wind turbine. Comparison with alternative wind energy forecast procedures show that this new model-based forecast method provide more accurate prediction of wind energy production for different seasons. Major advantages of this model include that it is based on data commonly available from weather forecast providers and is applicable to different terrain conditions.
Producing electricity from wind is attractive because it provides a clean, low-maintenance power supply. However, wind resource is intermittent on various timescales, thus occasionally introducing large and sudden changes in power supply. A better understanding of this variability can greatly benefit power grid planning. In the following study, wind resource is characterized using metrics that highlight these intermittency issues; therefore identifying areas of high and low wind power reliability in southern Africa and Kenya at different time-scales. After developing a wind speed profile, these metrics are applied at various heights in order to assess the added benefit of raising the wind turbine hub. Furthermore, since the interconnection of wind farms can aid in reducing the overall intermittency, the value of interconnecting near-by sites is mapped using two distinct methods. Of the countries in this region, the Republic of South Africa has shown the most interest in wind power investment. For this reason, we focus parts of the study on wind reliability in the country. The study finds that, although mean Wind Power Density is high in South Africa compared to its neighboring countries, wind power resource tends to be less reliable than in other parts of southern Africa—namely central Tanzania. We also find that South Africa’s potential varies over different timescales, with higher reliability in the summer than winter, and higher reliability during the day than at night. This study is concluded by introducing two methods and measures to characterize the value of interconnection, including the use of principal component analysis to identify areas with a common signal.
This study presents the design and implementation of a web-based Participatory Geographic Information System (PGIS) framework intended for offshore wind suitability analysis. The PGIS prototype presented here integrates GIS and decision-making tools that are intended to involve different stakeholders and the public for solving complex planning problems and building consensus. Public involvement from the early planning stage of projects with a spatial nature is very important for future legitimacy and acceptance of these projects. Therefore, developing and executing a system that facilitates effective public involvement for resolving contentious issues can help in fostering long-lasting agreements. The prototype here is a distributed and asynchronous PGIS that combines a discussion forum, a mapping tool and a decision tool. The potential strengths and benefits of this PGIS are demonstrated in a hypothetical case study in Lake Erie, northern Ohio. In the hypothetical case study, participants evaluate the importance of three decision alternatives using different evaluation criteria for expressing their individual preferences. The individual preferences are aggregated by Borda Count (BC) method for generating the group solution, which is used for synthesizing the different evaluation aspects such as the importance of criteria, ranking of the decision alternatives and planning issues related to environmental and socio-economic concerns from the participants.
The wind energy resources in the South Banat region are analyzed. The analyses have been carried out on the basis of the wind parameter measurements at the site of village Bavanište. The data were collected at the heights of 10, 40, 50, and 60 m during 2009 and 2010. The statistical analyses of the measured data covered the wind speed and direction, average wind speed and power density, and Weibull distribution parameters (c and k). On the basis of the determined standard deviation of the wind speed, an analysis is performed of the wind turbulence at the measurement site. Based on the method of sum of least squares, a mathematical method for estimation of the vertical wind speed profile has been developed. By applying this model, an analysis of the vertical wind speed profile at the measurement site has been performed. On the basis of the available measurement data, the electrical energy production in the targeted region by three test models of the wind turbines has been estimated. The obtained results show that the region of South Banat possesses good wind energy potential and that it represents a promising region for development of the projects of wind farms.
This paper deals with the analysis and comparison of 7 (seven) numerical methods for the assessment of effectiveness in determining the parameters for the Weibull distribution, using wind speed data collected in Camocim and Paracuru cities, State of Ceará, in the northeast region of Brazil, in the period from August 2004 to April 2006, obtained by the Department of Infrastructure of the State of Ceará. One method is not well known, namely the equivalent energy method, and its performance is compared to the others. By using the methods of analysis of variance, RMSE (root mean square error), and chi-square tests to compare the proposed methods, this study aims to determine which ones are effective in determining the parameters of the Weibull distribution for the available data, in an attempt to establish acceptable criteria to a better utilization of wind power in the State of Ceará, which is a national prominence in the use of renewable sources for electricity generation in Brazil.
The knowledge of the probability density function of wind speed is of paramount importance in many applications such as wind energy conversion systems and bridges construction. An accurate determination of the probability distribution of wind speed allows an efficient use of wind energy, thus rendering wind energy conversion system more productive. In the present paper, the maximum entropy principle (MEP) is used to derive a family of pre-exponential distributions in order to fit wind speed distributions. Using averaged hourly wind speed of six different regions in Algeria, it has been found that the proposed pre-exponential distributions fit the wind speed distributions better than the conventional Weibull distributions in terms of root mean square error. However, it has been found also that MEP based distributions have shown some practical limitations such as the choice of pre-exponential order and interval of definition.
Detailed knowledge of the wind resource is necessary in the developmental and operational stages of a wind farm site. As wind turbines continue to grow in size, masts for mounting cup anemometers-the accepted standard for resource assessment-have necessarily become much taller, and much more expensive. This limitation has driven the commercialization of two remote sensing (RS) tools for the wind energy industry: The LIDAR and the SODAR, Doppler effect instruments using light and sound, respectively. They are ground-based and can work over hundreds of meters, sufficient for the tallest turbines in, or planned for, production. This study compares wind measurements from two commercial RS instruments against an instrumented mast, in upland (semi-complex) terrain typical of where many wind farms are now being installed worldwide. With appropriate filtering, regression analyses suggest a good correlation between the RS instruments and mast instruments: The RS instruments generally recorded lower wind speeds than the cup anemometers, with the LIDAR more accurate and the SODAR more precise.
The sustainable development of offshore wind energy requires thorough investigations on technological issues. The substructure, which acts as the natural link between technologies and environments, is a critical topic for the offshore wind industry. This paper presents a comprehensive review of variable types of offshore wind substructures associate with their corresponding example projects. The study is complemented with a special attention to a novel foundation, namely suction bucket foundation. Main technological issues related to this concept are integrated. In the paper, bearing behaviors of offshore wind turbines (OWTs) with the suction bucket foundation under lateral loads, vertical loads, combined loads, and extreme loading conditions are discussed. Two installation methods are introduced. The geometric and improved design is illustrated by considering capabilities in transportation and installation. Research methods, including field tests, laboratory tests, centrifuge tests, theoretical analysis and numerical simulations, are listed; these methods are employed in previous studies to investigate behaviors of the OWT. This review integrates most relevant aspects and recent advancements together, which aims to provide a reference frame for future studies and projects.
The support structure of an offshore wind turbine (OWT) accounts for up to 25% of the capital cost; therefore, investigations into reliable and efficient foundations are critical for the offshore wind turbine industry. This paper describes an innovative hybrid monopile foundation for OWTs, which is an optimization of the original monopile foundation with broader applications. The behavior of OWTs with the hybrid monopile foundation in service conditions are investigated under lateral cyclic loadings, by considering the effects of wind, waves, and ice. A series of centrifuge tests are conducted in order to analyze these behaviors in detail, and OWT models with the original single-pile as well as wheel-only foundations are tested for comparison. Based on these tests, the accumulated lateral displacement and stiffness during cyclic loadings are presented, and the results indicate that the hybrid foundation exhibits a larger cyclic capacity than the other foundations. The influence of the cycle numbers, cyclic loading characteristics, and soil properties is examined during the tests; furthermore, the effects of these factors on the model deformation responses are illustrated. This study proposes the first analytical method for quantitatively estimating the cyclic lateral displacement of the new hybrid foundation in service conditions, and a degradation coefficient is recommended based on the test results. This method aims to provide a simple approach to predicting responses of OWTs with hybrid monopile foundations in service conditions.
The hybrid monopile-friction wheel foundation is an innovative alternative for offshore wind turbines. The concept has wider adaptability and can be used as reinforcement method for existing monopiles. A series of centrifuge tests was performed to investigate the lateral bearing capacities of the hybrid foundation under monotonic loads. Five foundation models and two soil types were considered. According to the recorded responses, the hybrid foundation demonstrated better lateral behaviors that both lateral bearing capacity and stiffness are enhanced. Two analytical methods were proposed and compared with the centrifuge test results. The bearing capacity of the hybrid foundation is smaller than the sum of individual pile and friction wheel, and a reduction factor is suggested for both friction wheels. The friction wheel restrains rotations of monopile and provides extra restoring moments; their effects are idealized as equivalent moments acting on the pile head. The analytical results provide possible solutions in estimating the lateral bearing capacity of the innovative hybrid foundation system for offshore wind turbines by using traditional theories.
Visual impact is one of the main factors influencing the acceptance of wind farms by the public and by the authorities. It therefore often sets the environmental and social limits of energy policy and energy use. However, the assessment of visual impacts is subjective, as is often pointed out by critics of the evaluation process. The study presented here for the first time uses accurately and objectively measurable landscape indices to directly predict the visual impact of onshore wind turbines. The method also for the first time evaluates map-based landscape indices in a panoramic simulation, and this provides a better match of visual preferences with landscape indices than the cartographic projection used until now. 400 respondents from four Central European countries (Austria, Germany, Poland and Czechia) provided an evaluation of their scenic perception of 32 different landscapes, in each case with and without wind turbines. At the same time, we analysed 12 indices characterizing the principal landscape components (relief, land cover and landscape pattern) on the basis of the 32 landscape photographs. These were further tested as predictors of visual impact. The most prominent predictors of visual impact were the Percentage of Industrial Area (including Commercial, Logistic and Mining Areas), Percentage of Forest Cover, Density of Technical Infrastructure, Number of Elevation Landmarks, and Elevation Variation. None of the three landscape pattern indices was statistically significant. On the basis of a regression model that is able to predict the potential visual impact in large areas of four Central European countries (over 830,000 km2), we present the general principles of an objectivized method for predicting the visual impact of onshore wind farms. The method makes an automatic assessment of the visual impact in large areas of entire regions or countries via a GIS analysis of Sentinel data and DEM data. This forms a good basis for both preventive evaluation and causal evaluation, and provides significant support for objectivizing the planning and decision process in order to mitigate negative environmental and social impacts of the use of wind energy.
This paper presents a new approach for the optimization of neighbouring offshore wind farms. Offshore wind energy is one of the most promising and developed low-carbon generation technologies. However, the high capital costs, which are strongly dependent on seabed depth, currently limit the geographical expansion of this technology to areas with relatively shallow waters and appropriate wind resource. This, along with the advantages of sharing a submarine transmission system among several projects, leads to a high concentration of offshore wind farms in certain zones, as happens, for example, in the North Sea. The presence of other neighbouring offshore wind farms has to be taken into account when a developer plans a new project, since the wake effect of wind turbines belonging to other neighbouring wind farms will affect the annual energy production and, consequently, the profitability of the project under study. However, not only already operating or installed neighbouring projects have to be borne in mind, but also the possible design of future neighbouring wind farms yet to be developed. In order to tackle this issue, an innovative co-evolutionary algorithm is proposed in this paper with the objective of determining a Nash equilibrium solution that would provide the best possible configuration of the wind farm under study by taking into account and limiting the disturbance introduced by other neighbouring projects. The performance of the proposed methodology has been successfully tested through the analysis of a realistic case and compared with other collaborative approaches and the classic single-project optimization methods already existing in the literature.
Most studies on offshore wind farm design assume a uniform wind farm, which consists of an identical type of wind turbines. In order to further reduce the cost of energy, we investigate the design of non-uniform offshore wind farms, i.e., wind farms with multiple types of wind turbines and hub-heights. Given a set of different types of wind turbines with a different default hub height for each type, we can specify the design of a wind farm by the types of turbines, number of turbines for each type, and turbine locations. We consider the optimization of such design to minimize the levelized cost of energy, which is calculated using a capital cost model that covers the turbine cost and the balance of plant cost. An empirical wind turbine design cost and scaling model is utilized to model the cost of turbines with different sizes. Constraints on wind farm boundary, wind turbine proximity and total capacity are also included. We solve the problem with a newly developed extended random search algorithm and tested it in a realistic design optimization problem based on the Horns Rev 1 offshore wind farm in Denmark. The optimized non-uniform designs are compared with their uniform counterparts. We find that a non-uniform design can achieve a lower levelized cost of energy than its uniform counterparts, when the capital cost per MW is slightly lower for the smaller size turbine. Comparison with the mixed-discrete particle swarm optimization algorithm is also carried out for a non-uniform wind farm design problem with a fixed number of turbines, which shows the effectiveness and superiority of the proposed algorithm. Finally, the advantages and possible disadvantages of non-uniform design are also identified and discussed.
Wind Energy is the one of the most promising renewable energy. Suction bucket foundation is considered to be a viable type of wind turbine foundation. Soil liquefaction caused by earthquakes at offshore seismic active area may lead to a significant degradation of soil strength and stiffness. In this study, nine centrifuge tests were carried out to investigate the seismic response of suction bucket foundation under earthquake loading. Both dry and saturate soil conditions were considered in tests. The geometric design of five suction bucket models considered the bucket diameter, penetration depth, and modified buckets with inside compartments. It was found that soil underlying and near the bucket foundation shown a better ability to resist liquefaction in saturated tests comparing to free field while no significant differences were observed in dry tests. The five bucket models performed quite differently, which demonstrated the aspect ratio effects and inside-bucket compartment effects. The results provide insight into optimized design of suction bucket foundation for wind turbine.
Wind resources are increasingly being investigated as a clean alternative for generating energy. This paper analyses the daily wind speed recorded at 46 automatic weather stations located in Navarre, northern Spain, in 2005-2015. Key points are the surface density of stations and the range of time that ensure a faithful depiction of wind speed together with surface calculations from image analysis and correlation with height. Different statistics were used. Median wind speed at 10 m was low, about 3.3 m s⁻¹ and its interquartile range was narrow, about 2.3 m s⁻¹. Nearly half the surface shows a median wind speed above 3.0 m s⁻¹. The method of moments was employed to calculate the parameters of the Weibull distribution. Around half of the surface presented a shape parameter above 2.25 and the scale parameter was above 4 m s⁻¹ for nearly 41% of the region. Although wind resources are not suitable for wind turbine applications in most of the region, since the wind speed is low in low-lying areas, about 12% of the region is suitable for stand-alone applications and, moreover, a substantial part of the region, around 23%, presents satisfactory wind resources for the installation of wind turbines.
The goal of this study was to develop a statistical bivariate wind speed-wind shear model (WSWS). The development of WSWS is based on near surface wind speed data available from 397 measurement stations distributed over Germany, as well as on ERA-Interim reanalysis wind speed data available in 1000 m above ground level (a.g.l.). These data were used (1) to calculate empirical distributions of wind speed in 1000 m a.g.l., (2) empirical distributions of the wind shear exponent, and (3) to fit theoretical distributions to the empirical wind speed and wind shear exponent distributions. It was found that the four parameter Johnson SB distribution reproduces the shape of the wind speed in 1000 m a.g.l. empirical distributions best. The four parameter Dagum distribution provided good fits to the empirical wind shear distributions. The parameterized wind speed and wind shear marginal distributions were then linked by 16 joint copulas. Goodness-of-fit evaluation of the joint copulas demonstrates that the Gaussian-Gaussian copula reproduces the empirical bivariate wind speed-wind shear distribution most accurately. By using WSWS it is possible to continuously calculate the wind speed probability density function in hub heights between 10 m a.g.l. and 200 m a.g.l. This allows WSWS to be applied to virtually any power curve for computing the wind energy yield and capacity factor in the analyzed hub height range. A one-time site-specific parametrization of WSWS is sufficient for a comprehensive height-dependent exploitation of the available wind resource.
Suction bucket foundation is a promising alternative for offshore wind turbine foundations. The lateral bearing behavior and failure mechanism are significant topics for optimizing its design criteria. In this study, a group of centrifuge tests were performed to investigate the lateral bearing capacity of suction bucket foundation with three aspect ratios. Force-controlled lateral static and cyclic tests were performed at a centrifuge acceleration of 50 g. Four soil conditions were considered in centrifuge tests with a combination of loose/dense and dry/saturated sand. The load-displacement and the stiffness-displacement relationships are presented in the paper, and it is concluded that the lateral bearing capacity can be reached when the normalized lateral displacement (displacement divided by the bucket diameter) reached 3% as the first method. Displacement rates are plotted against the lateral load to give the second method for defining the ultimate lateral capacity. In cyclic tests, the lateral displacement and stiffness are correlated to the cycle numbers in the first 5 cycles, but the change is less apparent in the rest. The results demonstrated the behavior of bucket foundation in service conditions. Finally, a simplified calculation method is used as the third method and checked with tests results.
Wind energy potential assessment is crucial for proper wind farm siting. Typically, this involves installation of tall and costly meteorological masts with anemometers. New technology such as Light Detection and Ranging (LiDAR) is an alternative mobile technology that serves such purpose. This paper describes the principle of LiDAR technology and presents case studies of its applications to evaluate the energy output potentials at the site near Lake Erie in northern Cleveland, Ohio, USA. A ZephIR® LiDAR system is used to monitor one-year of vertical wind data profile (at 30 m and 70 m height) from May 2011 to April 2012, from which the wind statistics are determined. These include the monthly average of wind speed, turbulence intensity, Weibull shape and scale factor, wind compass rose, and wind power density, etc. The wind speed data is used to evaluate the wind power capacity factors for prototype wind turbines that are subsequently installed in 2012. The data of power output by the turbines between 2013 and 2015 is used to compare with those predicted based on wind speed model derived from LiDAR measurement. The results show that the estimated wind turbine’s capacity factor from LiDAR data is satisfactory after excluding the maintenance days. This research demonstrates the potential of LiDAR technology as a cost effective way in providing reliable evaluation of wind energy potential.
The objective of this study is to assess the effect of hub height optimization on the Annual Energy Production (AEP) of a wind farm. The only optimization variable is the hub height of each wind turbine and all other characteristics of the wind farm, including base location, rotor diameter (D), and total number of wind turbines, remain unchanged. The first case study consisted of two wind turbines aligned with the wind direction, with the hub height of the upstream turbine fixed and that of the downstream turbine varying. Two competing effects were identified. On one hand, lowering the hub height of the downwind turbine exposes a larger fraction of the downwind rotor to the undisturbed wind and therefore increases its power production. On the other hand, turbines with lower hub heights experience lower wind speeds due to shear. The balance between these two effects is complicated by surface roughness and spacing between turbines. The maximum benefits are found offshore, with tight axial spacing (⩽5D). To put into perspective the effectiveness of using multiple hub heights in a realistic setting, the second case compared two 20-turbine wind farms with the same horizontal layout, turbine type, and wind direction, but one with all turbines installed at the same hub height (80 m) and the second with alternating rows of tall (100 m) and short (57.5 m) wind turbines. The vertically staggered configuration was found to produce approximately 5.4% more power. Lastly, a real 48-turbine wind farm (Lillgrund in Sweden) was optimized under all 360 wind directions and wind speeds ranging from the cut-in to the cut-out using a greedy search algorithm. The optimal configuration presented a non-intuitive and balanced mix of 26 tall and 22 short turbines. Although the entire range from a minimum to a maximum predefined hub height was searched to determine the optimal hub height for each wind turbine, the optimization algorithm ended up with an optimal layout in which the hub height of each turbine was equal to either the minimum hub height or the maximum hub height and no value in between was assigned to any of the turbines. The AEP of the optimized wind farm with multiple hub heights was approximately 2% (10 GWh) higher than that of the wind farm with a single hub height. The PARK model was used in this study to calculate wake losses and to perform the optimization. To validate its results, Large-Eddy Simulations (LES) of the flow around the turbines, represented as actuator lines, were conducted at fine spatial and temporal resolutions. The LES results confirmed that optimizing hub heights is beneficial and indicated that the net gains were actually higher (by up to a factor of two) than those calculated with PARK.
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Solar and wind generated power is expected to increase drastically in the future. Unlike fossil fuels, however, solar and wind resource extraction introduces challenges of variability and intermittency. Several recent studies around the world have shown that since dissimilar climatological factors are responsible for wind and solar resources, they can often operate in tandem to offset lulls in each other. While most research on solar and wind resource interaction has been undertaken over the Northern Hemisphere (America, Europe and China), there is a lack of understanding on how much (or even if) solar and wind resources complement one another in other parts of the world. To partially address this issue in the Southern Hemisphere, this study provides a systematic quantitative analysis of the complementary characteristics of solar and wind resources on the Australian continent. As such, wind power density and surface incident shortwave flux are derived from the hourly Modern Era Retrospective Analysis for Research and Applications (MERRA) product for the entire continent for the period from 1979 to 2014. It was found that the temporal synergy between solar and wind resource is maximum along the western and southern coast of Australia. Tasmania, south-eastern (parallel to eastern Great Dividing Range), and northern regions (Cairns and Kimberley Plateau) of the continent also showed significant synergy (≈40% within a distance of 93 km), which was mostly influenced by hours of daylight when the solar resource is available. Increasing the spatial extent increased the occurrence of synergy characteristics to 50% within a distance of 465 km. These findings are significant because most of the synergy (and intermittency) in solar and wind resources was found in proximity to transmission lines – locations where renewables are likely to be cited going forward. Amongst current large-scale solar and wind farms operating in south-eastern Australia, this study also finds that increased power production is possible by balancing existing assets with complementary solar and wind farms. While these results are limited to a single continent, the proposed approach (e.g. using similar metrics) can be readily applied to investigate synergies between solar and wind resource in other parts of the world using the global MERRA product.
A mathematical model for vertical extrapolation of the measurement data for wind speed taken at several measurement heights is presented. The model is based on the method of least squares (LES). By applying the proposed model on the sets of measured data taken at least at three measurement heights, one obtains a synthetic set of data at a desired height where the wind power potential is analyzed. The basic idea is that during the process of estimation of the wind power potential the measurement data are first extrapolated by the proposed method and then by using program WAsP the spatial extrapolation is carried out. The algorithm is tested by one year wind speed measurement data taken at three locations characterized by different topographies of the terrain and different climatic conditions. The performed analyses show that pre-processing of measurement data by the proposed method results in a better estimate of the wind power potential at a height which is greater than the measurement heights compared to that obtained by the standard application of WAsP program which makes use of measurement data taken at one measurement height.
Key words – wind speed data, vertical wind profile, least square method, wind resource, WAsP
A minor deviation in wind speed causes large deviation in the output power of wind turbine because of cubic bond association between these two parameters. Therefore, a precise assessment of wind resource over any site is considered of paramount significance. The investigations associated with the wind resource assessment have been proved of immense help for installation of different wind energy technologies such as nano, micro, small, medium, and large scale for wind energy generation. In order to provide a detailed information regarding the research in wind resource assessment a comprehensive literature review encompassing the different techniques, methodologies involved in development of wind power projects, and uncertainties associated with wind resource assessment as well as the preliminary assessment methodologies have been presented in this work. The advanced computational models namely wind atlas analysis and program, WindPro, computational fluid dynamics, and geographical information system are most frequently used software tools for mapping, modelling and annual energy estimation for a single or multiple wind turbines by considering the local conditions such as topography, surrounding obstacles, orography, and surface roughness based on the on-site measurements for a particular site. In addition, the statistical methods for short and long term data analysis, vertical wind speed profile, numerical weather prediction models, optimization of existing wind resource, and scope of hybrid wind energy systems have been delineated in this paper. Furthermore, current review presents a complete approach with reference to all the facets of the present status of research in the area of wind resource assessment worldwide.
Wind speed forecasting aids in estimating the energy produced from wind farms. The soaring energy demands of the world and minimal availability of conventional energy sources have significantly increased the role of non-conventional sources of energy like solar, wind, etc. Development of models for wind speed forecasting with higher reliability and greater accuracy is the need of the hour. In this paper, models for predicting wind speed at 10-min intervals up to 1 h have been built based on linear and non-linear autoregressive moving average models with and without external variables. The autoregressive moving average models based on wind direction and annual trends have been built using data obtained from Sotavento Galicia Plc. and autoregressive moving average models based on wind direction, wind shear and temperature have been built on data obtained from Centre for Wind Energy Technology, Chennai, India. While the parameters of the linear models are obtained using the Gauss–Newton algorithm, the non-linear autoregressive models are developed using three different data mining algorithms. The accuracy of the models has been measured using three performance metrics namely, the Mean Absolute Error, Root Mean Squared Error and Mean Absolute Percentage Error.
In the latest years the wind energy sector experienced an exponential growth all over the world. What started as a deployment of onshore projects, soon moved to offshore and, more recently to the urban environment within the context of smart cities and renewable micro-generation. However, urban wind projects using micro turbines do not have enough profit margins to enable the setup of comprehensive and expensive measurement campaigns, a standard procedure for the deployment of large wind parks. To respond to the wind assessment needs of the future smart cities a new and simple methodology for urban wind resource assessment was developed. This methodology is based on the construction of a surface involving a built area in order to estimate the wind potential by treating it as very complex orography. This is a straightforward methodology that allows estimating the sustainable urban wind potential, being suitable to map the urban wind resource in large areas. The methodology was applied to a case study and the results enabled the wind potential assessment of a large urban area being consistent with experimental data obtained in the case study area, with maximum deviations of the order of 10% (mean wind speed) and 20% (power density).
In current offshore wind turbine designs, many are basic concepts using standard land-based wind turbines ‘marinised’ using a platform from the offshore oil and gas industry with additional anti-corrosion and structural stiffness. These projects are also focused on fixed offshore wind turbines at depths of less than 50 m. The design conservatism observed is present to avoid many changes to the proven technology on land-based wind turbines and offshore fixed foundations, to assure technical feasibility and economic viability in the short term. However, exportation of onshore technology directly to the offshore environment may not be entirely advantageous. There are opportunities in new designs or configurations, which can potentially lower cost of energy in a less restrictive offshore environment.
This paper aims to review the current offshore wind technology and discusses comprehensively some of the factors and opportunities in selecting a downwind configuration for offshore wind turbines. In addition, current industry, research and developmental trends for downwind offshore wind turbines are described. Various technical challenges and gaps foreseen for this design motivation are also highlighted in the paper.
Based on a 3-year (2011–2013) dataset of 10-min records collected at 10, 20, 40, and 80 m from the met mast of Cabauw, a time-varying investigation of the wind shear coefficient (WSC) relationship with atmospheric stability was addressed. WSC interdaily and interannual variability was analysed according to a 2-D combined representation, which confirmed a clear oval-shaped “solar shadow” caused by solar warming observed during diurnal unstable hours, and large WSCs occurring under strong stable conditions during the summer nights.
Three different power law based approaches were compared to extrapolate wind resource to the turbine hub height according to the following WSC settings: (i) site's previously measured overall yearly average; (ii) site's previously measured stability-varying yearly averages; (iii) 10-min theoretically predicted values by applying the Panofsky and Dutton (PD) model. The latter proved to be the finest approach, providing extrapolated wind resource biased by 1–5% and energy yield by 5.51–10.57%, and showing the highest accuracy occurring under the most frequent (and most energetic) neutral conditions, when Weibull distribution's tail including the highest wind speed bins is particularly finely reproduced.
This work confirmed how instrumental availability of detailed information on site's atmospheric stability classification is for wind energy studies.
It has been globally recognized that the harvesting of renewable energy is of considerable importance for the achievement of sustainable development. As for Hong Kong, one of the most densely populated cities, the shortage of indigenous fossil sources has inevitably resulted in excessive dependence on external energy sources. Nevertheless, in consideration of the reduction of fossil fuel reserves, as well as the impact on the environment of fossil fuel uses, the exploration of usable renewable energy sources becomes increasingly important for Hong Kong's long-term development. Based on 6-year wind observations from three meteorological stations at three islands in Hong Kong, this study provides a statistical assessment of the wind characteristics and wind energy potential at offshore locations surrounding Hong Kong. The Weibull distribution function was applied to estimate the Weibull parameters which can be used to facilitate the evaluation of offshore wind energy potential. The variation of the mean wind speed, the Weibull parameters and the wind power density were established under various timescales. Significant yearly, seasonal and monthly variations of the Weibull parameters were observed, while the diurnal variation was relatively small. The veracity of the Weibull distribution model to represent offshore wind data was examined, and it was shown that the Weibull model gave an adequate description of the frequencies of actual wind data. Finally, the total wind power capacities at the three potential offshore wind farm locations were derived, which indicated that the Southeastern waters are the most promising locations for offshore wind farm development in Hong Kong.
Meteorological (met) station data is used as the basis for a number of influential studies into the impacts of the variability of renewable resources. Real turbine output data is not often easy to acquire, whereas meteorological wind data, supplied at a standardised height of 10 m, is widely available. This data can be extrapolated to a standard turbine height using the wind profile power law and used to simulate the hypothetical power output of a turbine. Utilising a number of met sites in such a manner can develop a model of future wind generation output. However, the accuracy of this extrapolation is strongly dependent on the choice of the wind shear exponent α. This paper investigates the accuracy of the simulated generation output compared to reality using a wind farm in North Rhins, Scotland and a nearby met station in West Freugh. The results show that while a single annual average value for α may be selected to accurately represent the long term energy generation from a simulated wind farm, there are significant differences between simulation and reality on an hourly power generation basis, with implications for understanding the impact of variability of renewables on short timescales, particularly system balancing and the way that conventional generation may be asked to respond to a high level of variable renewable generation on the grid in the future.
Based on power law (PL), a novel method is proposed to extrapolate surface wind speed to the wind turbine (WT) hub height, via assessment of wind shear coefficient (WSC), by only using surface turbulence intensity, a parameter actually regarded as a merely critical one in wind energy studies. A 2-year (2012–2013) dataset from the meteorological mast of Cabauw (Netherlands) was used, including 10-min records collected at 10, 20, 40, and 80 m. WT hub heights of 40 and 80 m have been targeted for the extrapolation, being accomplished based on turbulence intensity observations at 10 and 20 m. Trained over the year 2012, the method was validated over the year 2013.
Good scores were returned both in wind speed and power density extrapolations, with biases within 7 and 8%, respectively. Wind speed extrapolation was better predicted 10–40 m (NRMSE = 0.16, r = 0.95) than 10–80 and 20–80 m (NRMSE = 0.20–0.24, r = 0.86–0.91), while for power density even finer scores than wind speed were achieved (r = 0.98 at 40 m, and r = 0.96 at 80 m). Method's skills were also assessed in predicting wind energy yield. Application over sites with different terrain features and stability conditions is expected to provide further insight into its application field.
Increasing knowledge on wind shear models to strengthen their reliability appears as a crucial issue, markedly for energy investors to accurately predict the average wind speed at different turbine hub heights, and thus the expected wind energy output. This is particularly helpful during the feasibility study to abate the costs of a wind power project, thus avoiding installation of tall towers, or even more expensive devices such as LIDAR or SODAR. The power law (PL) was found to provide the finest representation of wind speed profiles and is hence the focus of the present study. Besides commonly used for vertical extrapolation of wind speed time series, the PL relationship between "instantaneous" wind profiles was demonstrated by Justus and Mikhail to be consistent with the height variation of Weibull distribution. Therefore, in this work a comparison is performed between these two different PL based extrapolation approaches to assess wind resource to the turbine hub height: (i) extrapolation of wind speed time series, and (ii) extrapolation of Weibull wind speed distribution. The models developed by Smedman-Hogstrom and Hogstrom (SH), and Panofsky and Dutton (PD) were used to approach (i), while those from Justus and Mikhail (JM) and Spera and Richards (SR) to approach (ii). Models skill in estimating wind shear coefficient was also assessed and compared. PL extrapolation models have been tested over a flat and rough location in Apulia region (Southern Italy), where the role played by atmospheric stability and surface roughness, along with their variability with time and wind characteristics, has been also investigated. A 3-year (1998-2000) 1-h dataset, including wind measurements at 10 and 50 m, has been used. Based on 10 m wind speed observations, the computation of 50 m extrapolated wind resource, Weibull distribution and energy yield has been made. This work is aimed at proceeding the research issue addressed within a previous study, where PL extrapolation models were tested and compared in extrapolating wind resource and energy yield from 10 to 100 m over a complex topography and smooth coastal site in Tuscany region (Central Italy). As a result, wind speed time series extrapolating models proved to be the most skilful, particularly PD, based on the similarity theory and thus addressing all stability conditions. However, comparable results are returned by the empirical JM Weibull distribution extrapolating model, which indeed proved to be preferable as being: (i) far easier to be used, as z(0)-, stability-, and wind speed time series independent; (ii) more conservative, as wind energy is underpredicted rather than overpredicted.
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
The use of wind as an energy source is becoming popular because of its non-polluting and renewable features. There is an urgent request to develop site-based estimation on wind engineering, which can be used for optimal design of wind turbines and wind farming. The wind speed for Zafarana Project in Suez Gulf, namely Site-3, based on monthly averaged data for 1 year as well as every 10 min for two days, one day in summer season and one day one winter season have been analyzed to estimate the most appropriate method to find Weibull distribution parameters for this site. The investigated methods are the mean wind speed method, the maximum likelihood method, the modified maximum likelihood method, the graphical method and the power density method. These methods results have been compared with the provided data to find their accuracy based on the root mean square errors. From the obtained results, the mean wind speed and the maximum likelihood method are recommended in estimating the wind speed distribution for the studied site in Zafarana wind farm.
The content of this article is a contribution to the limited amount of available strong-wind multi-level tower observations in the atmospheric surface layer, and is primarily intended for those engineers and scientists engaged in the field of wind engineering. The observations were used to evaluate the correctness of the predictions obtained from theoretical and empirical models, the latter used frequently by the wind engineering community. The comparisons included profiles of mean wind, turbulence intensity, and gust velocities. To test the mean-velocity models for the prediction of wind speeds at locations where no recording stations were present, observations at a reference location were used to predict and to compare with the simultaneous observations at a number of locations where wind speed observations were available.The analysis of the data revealed that under strong wind conditions thermal stability effects should not be ignored. For obstacle-free open terrain significant variations of the aerodynamic roughness length are observed. The height of the surface layer that increases with roughness and wind speed is at least 150 m. Davenport's “gradient” height, not a function of wind speed, is approximately twice the height of the surface layer that applies to the strong wind data analyzed. Estimation of wind speed at locations where normally no observations are available may exceed the actual speed by as much as 50%.
Long-term data from several tall meteorological towers were used to evaluate the ability of the 1/7 power law to account for the presence of low-level nocturnal jets at heights relevant to large wind turbines. It was found that for homogeneous terrain the 1/7 law was valid, but no single coefficient generated accurate velocity extrapolations during the night. It was determined that the flow frequently decouples at night at sites around the world, and a minimum power coefficient could be determined that signaled the onset of the decoupling, which was dependent on the Obhukov length for atmospheric stability. The transition to decoupling was at times associated with the occurrence of linear wind profiles. The long-term data indicated that the decoupling begins in the evening, reaches a maximum late at night, and a normal profile returns after sunrise. The magnitude of the power coefficient varies seasonally. It is concluded that a single power law is insufficient to adequately project the power available from the wind at a given site, and that consideration should be given to the added fatigue that a rotor and hub may experience due to the uneven shear forces induced by the jet.
Among all uncertainty factors affecting the wind power assessment at a site, wind speed extrapolation is probably one of most critical ones, particularly if considering the increasing size of modern multi-MW wind turbines, and therefore of their hub height. This work is intended as a contribution towards a possible harmonisation of methods and techniques, necessarily including surface roughness and atmospheric stability, aimed at extrapolating wind speed for wind energy purposes. Through the years, different methods have been used to this end, such as power law (PL), logarithmic law (LogL), and log-linear law (LogLL). Furthermore, aside from applying PL by using a mean wind shear coefficient observed between two heights ( ), a number of methods have been developed to estimate PL exponent α when only surface data are available, such as those by Spera and Richards (SR), Smedman-Högström and Högström (SH) and Panofsky and Dutton (PD).
Wind measurements are generally performed below wind turbine hub heights due to higher measurement and tower costs. In order to obtain the wind speed at the hub height of the turbine, the measurements are extrapolated, assuming that the wind shear is constant. This assumption may result in some critical errors between the estimated and actual energy outputs. In this paper wind data collected in Balıkesir from October 2008 to September 2009, has been used to show the effects of wind shear coefficient on energy production. Results of the study showed that, the difference between wind energy production using extrapolated wind data and energy production using measured wind data at hub height may be up to 49.6%.
The study presents the local values of wind shear coefficient (WSC) estimated using wind speed measurements made at 20, 30 and 40m above ground level (AGL) during November 01, 1998 and October 12, 2002. The study also includes the local values of air density calculated using temperature and pressure measurement made at 2m AGL during the same period. The mean wind speed above 4m/s and the standard deviation values were used to obtain the turbulence intensities (TI) at different heights. These local values of WSC were used to estimate the wind speed at hub-height of the wind turbines used in this study. Energy yield was calculated for a hypothetical wind form of 60MW installed capacity assumed to be consisting of 100, 60 and 30 wind turbines of 600, 1000 and 2000kW from DeWind, respectively.The study recommends a value of WSC of 0.255 for the estimation of wind at different heights AGL and local air density of 1.06kg/m3. The WSC values were found to be higher during nighttime and smaller during daytime while no evident seasonal trend could be identified. In case of air density, no diurnal change was evident but a seasonal trend, with higher values in winter and lower in summer months, was evident. The annual energy yield obtained using wind speed at different hub-heights calculated with WSC=0.255 was found to be 10–20% higher than the yield obtained with wind speeds calculated with WSC=0.143 corresponding to hub-heights of 60 and 100m, respectively. Similarly, higher plant capacity factors (PCFs) were obtained for energy yield estimated using WSC=0.255 compared to that with WSC=0.143. Higher values of TI were obtained during day time and lower during nighttime. Furthermore, lower values were obtained during November to March and higher during rest of the year. Finally, a decreasing pattern was observed in the values of TIs with increasing height.
The difficulties in estimating the long term mean wind speed and subsequently wind turbine energy output derive from the fact that more often than not, available data is taken at a level other than machine hub height. The 1/7th power law has been recognised as a handy tool to carry out vertical wind speed extrapolation to the desired hub height. It is also understood that using an exponent of 1/7th could lead to underestimation of the actual long-term mean wind speed aloft. This paper strives to evaluate the power law with respect to wind data taken on a 25 m mast on the central Mediterranean island of Malta. Whilst deriving a site-specific factor affiliated to a typical terrain type, it also strives to determine characteristic variations of the power law exponent over appropriate sampling intervals.
This paper provides realistic values of wind shear coefficients calculated using measured values of wind speed at 20, 30 and 40m above the ground for the first time in Saudi Arabia in particular and, to the best of the authors’ knowledge, in the Gulf region in general. The paper also presents air density values calculated using the measured air temperature and surface pressure and the effects of wind shear factor on energy production from wind machines of different sizes. The measured data used in the study covered a period of almost three years between June 17, 1995 and December 1998. An overall mean value of wind shear coefficient of 0.194 can be used with confidence to calculate the wind speed at different heights if measured values are known at one height. The study showed that the wind shear coefficient is significantly influenced by seasonal and diurnal changes. Hence, for precise estimations of wind speed at a height, both monthly or seasonal and hourly or night time and day time average values of wind shear coefficient must be used. It is suggested that the wind shear coefficients must be calculated either (i) using long term average values of wind speed at different heights or (ii) using those half hourly mean values of wind speed for which the wind shear coefficient lies in the range ⩾0 and ⩽0.51. The air density, calculated using measured temperature and pressure was found to be 1.18kg/m3. The air density values were also found to vary with the season of the year and hour of the day, and hence, care must be taken when precise calculations are to be made. The air density values, as shown in this paper, have no significant variation with height. The energy production analysis showed that the actual wind shear coefficient presented in this paper produced 6% more energy compared to that obtained using the 1/7 power law. Similarly, higher plant capacity factors were obtained with the wind shear factor of 0.194 compared to that with 0.143.
This paper statistically examine wind characteristics from seven meteorological stations within the North-West (NW) geo-political region of Nigeria using 36-year (1971–2007) wind speed data measured at 10m height subjected to 2-parameter Weibull analysis. It is observed that the monthly mean wind speed in this region ranges from 2.64m/s to 9.83m/s. The minimum monthly mean wind speed was recorded in Yelwa in the month of November while the maximum value is observed in Katsina in the month of June. The annual wind speeds range from 3.61m/s in Yelwa to 7.77m/s in Kano. It is further shown that Sokoto, Katsina and Kano are suitable locations for wind turbine installations with annual mean wind speeds of 7.61, 7.45 and 7.77m/s, respectively. The results also suggest that Gusau and Zaria should be applicable for wind energy development using taller wind turbine towers due to their respective annual mean speeds and mean power density while Kaduna is considered as marginal. In addition, higher wind speeds were recorded in the morning hours than afternoon periods for this region. A technical electricity generation assessment using four commercial wind turbines were carried out. The results indicate that, while the highest annual power is obtained with Nordex N80–2.5MW as 14233.53kW/year in Kano, the lowest is in Yelwa having 618.06kW/year for Suzlon S52. It is further shown that the highest capacity factor is 64.95% for Suzlon S52–600kW in Kano while the lowest is 3.82% for Vestas V80–2MW in Yelwa.
The importance of characterizing the wind-shear at a given site for a utility-scale wind turbine cannot be overemphasized. Such characterization is needed for an accurate prediction of its power output. Thus, the objective of this work based on the use of several US tall tower wind data sets was to determine the accuracies of different wind-shear enumeration methods, especially when used at sites having hills and/or forests. In addition, average wind shear variations with respect seasonal and annual effects and data length are presented for various long data sets, recorded to between 1995 and 2005. Wind direction and atmospheric stability were not a factor in the analysis. At some of the sites the greatest average wind-shear was found during the summer. For the site with the most complex terrain, the average annual all-direction wind-shear varied by up to 7% between different years; this was partly due to year-to-year variations of the directions distribution. There was found to be no significant difference between the performance of the log and power laws; using either may give inaccurate predictions of hub-height mean wind speeds.
This study presents calculated values of wind shear coefficients (WSE) using measured values of wind speed at 20, 30, and 40m above ground level (AGL), for Dhahran, Saudi Arabia. The study also includes the air density estimated using measured air temperature and surface pressure and effect of wind shear coefficient on energy yield from a wind farm of 60MW installed capacity developed using 40 wind turbines of 1500kW size. The data used in the determination of wind shear coefficient covered a period of almost 5 years between 4 October 1995 and 30 November 2000.The study suggests a value of 0.189 of wind shear coefficient for the calculation of wind speed at different heights if measured values are known at one height. No regular seasonal trend was observed in the values of wind shear coefficients. In case of diurnal variation, higher values were observed during nighttime and early hours of the day and comparatively smaller values during day light hours. The air density, calculated using measured temperature and pressure was found to be 1.18kg/m3. The energy yield obtained using RetScreen software, showed that the actual wind shear coefficient presented in this paper produced around 11–12% more energy compared to that obtained using 1/7 power law. Accordingly, 2–3% higher plant capacity factors were achieved using actual site-dependent wind shear coefficient instead of 1/7th wind power law exponent for the calculation of wind speed at hub-height.