Extrapolating wind speed time series vs. Weibull distribution to assess wind resource to the turbine hub height: A case study on coastal location in Southern Italy

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–Högström and Högström (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 z₀–, stability–, and wind speed time series independent; (ii) more conservative, as wind energy is underpredicted rather than overpredicted.     

Fitting wind speed distributions: A case study

The wind speed data represented in the form of frequency curves show the shape of a potential model. The Weibull and Lognormal models are used for this purpose, with hourly mean wind speed data. This study deals with the estimation of the annual Weibull and Lognormal parameters from 20 locations in Navarre. The suitability of both distributions is judged from the R² coefficient with a linear regression for the Weibull distribution and a nonlinear regression for the Lognormal distribution. Both approaches give a good fit, giving better results for the Weibull distribution. A comparison between the estimation and the production for a wind farm is offered.     

两种风速短临预报方法对比研究

利用BP神经网络法和最小二乘法,对不同地形条件下的4个测站的10 s量级和15 min量级平均风速进行短临预报实验。研究发现,最小二乘法预报误差小,满足预报误差小于35%的日数比较大。无论是10 s量级预报,还是15 min量级预报,对于风速较大的01号站和04号站,最小二乘法优于BP神经网络法;对于风速较小的02号站和03号站,两种预报方法的预报效果相近;在10 s量级和15 min量级的风速短临预报方面,算法复杂的BP神经网络法并无明显优势。因此,在选取预报方法前,应结合预报方法本身的特征,充分考虑预报方法对地形、地貌和气候特征以及预报时效的适应性,最好对几个备选方法进行预报效果比对。     

倾斜面太阳辐射量计算方法研究

倾斜面辐射数据是保证准确设计太阳能利用系统的基础数据,一般由水平面数据计算得出。针对现有计算模型误差较大的现状考虑,试验测试了水平面及不同倾角斜面上的太阳辐射数据,提出了针对直接辐射转换系数的修正方法;通过倾斜面散射辐射数据的计算和分析,在散射辐射模型的基础上,建立耦合计算模型。试验与模型计算结果表明,散射辐射的各向同性受天气工况的影响,耦合模型具有较高的准确性,计算值与实测值的偏差可控制在5.3%以内。     

A novel empirical model for vertical profiles of downburst horizontal wind speed

This study proposes an empirical model for preliminary wind-resist design of downburst flow. Existing empirical models were compared with field data and found to underpredict horizontal wind speed below the height corresponding to the maximum radial velocity, due to the neglect of viscous effects and the evolution of vertical wind profiles along radial direction. To address these deficiencies, semi-empirical piecewise functions including wall shear effects in the local turbulent boundary layer and interpolation functions were proposed to improve the accuracy of existing models. The wind profile based on Coles' theory was found to agree well with field data, with the parabola interpolation function being the most desirable. Using the proposed method, the vertical profile of horizontal wind speed at different local radial locations can be predicted for wind resist design given the inlet wind speed of the downburst flow. Overall, this model improves upon existing empirical models and allows for more accurate wind-resist design.     

A study of the wind speed and wind energy availability in Bangladesh

Characteristics of wind speed data for three recent years, recorded at 14 stations of the Bangladesh Meteorological Department, have been studied. The data have been used to compute the monthly average wind speed and the wind energy availability for the stations. Average values of monthly wind speed for 1931–1960 have been employed to obtain the energy availability from the energy pattern factor, and the two sets of results have been compared. It has been found that, for the Chittagong station, the frequency distributions have good fits of the Weibull type.     

A new strategy for wind speed forecasting using artificial intelligent methods

A new strategy in wind speed prediction based on fuzzy logic and artificial neural networks was proposed. The new strategy for fuzzy logic not only provides significantly less rule base but also has increased estimated wind speed accuracy when compared to traditional one. Meanwhile, applying the proposed approach to artificial neural network leads to less neuron numbers and less learning time process along with accurate wind speed prediction results. The experimental results demonstrate that the proposed method not only provides less computational time but also a better wind speed prediction performance.     

Exergy analysis in a wind speed prognostic model as a wind farm sitting selection tool: A case study in Southern Greece

In the present paper, the wind potential of Central Peloponnese in Greece has been studied and the Exergy Analysis methodology was implemented as a wind farm sitting selection tool. The wind speed of the chosen regions of Central Peloponnese was studied and correlated based on the measurements of three specific sites in the wider area using a software based prognostic model using intercomparisons of cross-predictions among these sites. The Exergy Analysis implemented in this innovative wind speed forecasting model is used to identify the actual use of energy from the existing available energy and to evaluate the proposed sites appropriate for wind farm development ending up to an accurate wind map of the area.     

A hybrid statistical method to predict wind speed and wind power

Accurate forecasting of wind speed and wind power is important for the safety of renewable energy utilization. Compared with physical methods, statistical methods are usually simpler and more suitable for small farms. Based on the methods of wavelet and classical time series analysis, a new short-term forecasting method is proposed. Simulation upon actual time data shows that: (1) the mean relative error in multi-step forecasting based on the proposed method is small, which is better than classical time series method and BP network method; (2) the proposed method is robust in dealing with jumping data; and (3) the proposed method is applicable to both wind speed and wind power forecasting.     

A simple recipe for estimating atmospheric stability solely based on surface‐layer wind speed profile

The wind energy community is gradually recognizing the significance of atmospheric stability in both power production and structural loading. However, estimating stability requires temperature gradient data which are not commonly measured by the wind farm developers or operators. To circumvent this problem, we propose a simple approach á la Swinbank, to estimate stability from only 3 levels of wind speed measurements. As such, this approach is ideally suited for sodar‐ and lidar‐based wind measurements owing to their high vertical resolution in the surface layer.     

基于不同风切变指数算法的风场风速推算精度分析

文章采用内蒙古某风场测风塔3 a逐10 min风速实测数据,基于5种算法计算不同高度层间风切变指数,分别进行风速实例推算,对比分析各种算法的精度。结果表明:去除小风速数据算法(M2)较其他4种算法的变异系数小,推算风速精度总体最高;同一算法中,不同高度层间风切变指数随高度层高度和层间高度差增加而增大,非相邻层间风切变指数随高度差增加而增大;同一算法中,年平均风速推算比月平均风速推算精度更高,相邻层间风速推算较非相邻层间风速推算精度高,且相邻高度层越高精度越高,非相邻层间风速推算,高度差越小精度越高。该研究结果对风场建设可行性论证和风资源评估,及开展风场轮毂高度风速的推算、预测具有较好的指导意义。     

Evaluation of monthly capacity factor of WECS using chronological and probabilistic wind speed data: A case study of Taiwan

Monthly energy outputs and capacity factors of an existing median-scale wind power station located in Mailiao, Taiwan, have been analyzed by using the measured half-hourly wind speed data over a period spanned between 2002 and 2005. The wind speed data in the chronological (time-series) format and probabilistic (Weibull) format are used to estimate the monthly capacity factors of Vestas V47–660 KW turbines in Mailiao. The monthly capacity factors calculated from the chronological and probabilistic approaches are compared with and validated by the measured capacity factors obtained in Mailiao. In addition, due to the climatic features of the Asia monsoon, there are two prominent wind periods within a year in Taiwan, i.e., the strong (October–March) and weak (April–September) wind periods. This study also investigates the differences of the measured and calculated capacity factors in the strong and weak wind periods of Mailiao. It is shown that the capacity factors calculated from the time-series approach have better agreement with the actual capacity factors than the Weibull approach. Both approaches have slightly underestimated the capacity factors in the strong wind period, but have significantly overestimated the capacity factors in the weak wind period.     

A methodology to generate statistically dependent wind speed scenarios

Wind power – a renewable energy source increasingly attractive from an economic viewpoint – constitutes an electricity production alternative of growing relevance in current electric energy systems. However, wind power is an intermittent source that cannot be dispatched at the will of the producer. Modeling wind power production requires characterizing wind speed at the sites where the wind farms are located. The wind speed at a particular location can be described through a stochastic process that is spatially correlated with the stochastic processes describing wind speeds at other locations. This paper provides a methodology to characterize the stochastic processes pertaining to wind speed at different geographical locations via scenarios. Each one of these scenarios embodies time dependencies and is spatially dependent of the scenarios describing other wind stochastic processes. The scenarios generated by the proposed methodology are intended to be used within stochastic programming decision models to make informed decisions pertaining to wind power production. The methodology proposed is accurate in reproducing wind speed historical series as well as computationally efficient. A comprehensive case study is used to illustrate the capabilities of the proposed methodology. Appropriate conclusions are finally drawn.     

Wind tunnel study on wind and turbulence intensity profiles in wind turbine wake

In recent years, there has been a rapid development of the wind farms in Japan. It becomes very important to investigate the wind turbine arrangement in wind farm, in order that the wake of one wind turbine does not to interfere with the flow in other wind turbines. In such a case, in order to achieve the highest possible efficiency from the wind, and to install as many as possible wind turbines within a limited area, it becomes a necessity to study the mutual interference of the wake developed by wind turbines. However, there is no report related to the effect of the turbulence intensity of the external flow on the wake behind a wind turbine generated in the wind tunnel. In this paper, the measurement results of the averaged wind profile and turbulence intensity profile in the wake in the wind tunnel are shown when the turbulence intensity of the external wind was changed. The wind tunnel experiment is performed with 500mm-diameter two-bladed horizontal axis wind turbine and the wind velocity in wake is measured by an I-type hot wire probe. As a result, it is clarified that high turbulence intensities enable to the entrainment of the main flow and the wake and to recover quickly the velocity in the wake.     

The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: A case study over the UK

Over recent years there has been an increasing deployment of renewable energy generation technologies, particularly large-scale wind farms. As wind farm deployment increases, it is vital to gain a good understanding of how the energy produced is affected by climate variations, over a wide range of time-scales, from short (hours to weeks) to long (months to decades) periods.By relating wind speed at specific sites in the UK to a large-scale climate pattern (the North Atlantic Oscillation or “NAO”), the power generated by a modelled wind turbine under three different NAO states is calculated. It was found that the wind conditions under these NAO states may yield a difference in the mean wind power output of up to 10%. A simple model is used to demonstrate that forecasts of future NAO states can potentially be used to improve month-ahead statistical forecasts of monthly-mean wind power generation.The results confirm that the NAO has a significant impact on the hourly-, daily- and monthly-mean power output distributions from the turbine with important implications for (a) the use of meteorological data (e.g. their relationship to large-scale climate patterns) in wind farm site assessment and, (b) the utilisation of seasonal-to-decadal climate forecasts to estimate future wind farm power output. This suggests that further research into the links between large-scale climate variability and wind power generation is both necessary and valuable.     

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

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

一种基于大气稳定度的风资源评估方法

在风资源评估过程中,平均风速、风切变指数、风功率密度等是必须测量的特性参数,这些参数的测量均受地形地貌、大气稳定度、测风时间、测风设备的影响。在目前的风资源评估中,大气稳定度的影响基本都被忽略,因此,影响了风资源评估的准确性,甚至会带来选址的决策性失误。文章研究了大气稳定度对风资源特性的影响,并以美国某地4年的测风数据为例,研究大气稳定度对风切变指数,风能玫瑰图,风功率密度等的影响,建立了考虑大气稳定度的轮毂高度风速外推模型,解决了目前风资源评估中外推轮毂高度风速时由于使用整个风电场的平均风切变指数而带来风资源评估误差的问题。算例结果表明,该模型结构简单,外推结果精度高,具有较强的工程实用价值。     

A surface-layer wind speed correction: A case-study of Darling station

In previous study, the vertical wind speed extrapolation from measurement station to modern turbine hubs over an open homogenous terrain was considered. It was presented that an assumption of wind shear exponent under different stability conditions was an inaccurate representation of the actual wind climates as the precise knowledge of the site's wind characteristics at different levels and seasons are essential for planning and implementation of a proposed energy project. In this study, the surface-layer wind speed correction at Darling using the WRF modeling with mesoscale terrain corrections is presented. An hourly mesoscale modeled winds at 3 km grid spacing obtained for one month are postprocessed for estimation of local wind speed profiles at 10 and 50 m height AGL. The sensitivity of the modeled winds to surface terrain corrections is investigated using mesoscale topography parameterizations. Furthermore, 6-hourly mesoscale modeled and satellite observed winds as well as measurements from Darling station are utilized for validation of the statistical downscaling method utilized for the postprocessing of the boundary layer winds over land. It is presented that the precision of the mesoscale modeled winds for local wind speed estimates at potential site without historical measurements can be significantly improved. The confidence in the validity of this methodology for local wind speed correction is estimated at 96–98%.     

Experimental study of variable speed wind generator contribution to primary frequency control

The work presented in this paper analyses, with the help of experiments on a 2.2 kW test bench, the possibility to participate in the primary frequency control with a variable speed wind generator. A power reserve is obtained with the help of the generator torque control by following a power reference value lower than the maximum power which must be extracted from the wind. This approach allows also using a part of the kinetic energy in the blades inertia to contribute to this reserve.The dynamic tests carried out on the test bench, by using medium and high variable wind speeds, confirm the capacity of the wind turbine generator (WTG) to participate in the primary frequency control.     

High value wind: A method to explore the relationship between wind speed and electricity locational marginal price

Wind and solar resources are, by nature, spatially distributed and temporally variable. The process of siting generators that use these renewable resources and integrating them into the electricity system therefore raises different issues than the same process for combustion facilities does. A method for discovering wind power sites with the highest value to the electricity system was developed and is illustrated here using data for the state of Michigan. This method combines readily available hourly average 10 m wind speed data with wholesale electricity price data, as hourly locational marginal price (LMP). The 10 m wind speed data from 72 sites were extrapolated vertically to 80 m turbine hub height, converted to wind power density, and interpolated horizontally via kriging to reconstruct a continuous surface. LMP data from 178 generator nodes were allocated across space using Thiessen polygons. High LMP was interpreted as a signal of insufficiency or weakness in the electricity system, and wind energy was considered a possible remedy. The method, implemented in a GIS, identifies when and where peaks in LMP and wind power density co-occur and highlights these events as high value. As the drive to incorporate more renewable generators into the electricity system increases, this method will help locate the most desirable sites based on wind resource characteristics and the structure of the larger electricity system. Proposing a new way to think about the value of the wind resource to the electricity system is a primary contribution of this work.