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Gualtieri G.,CNR Institute for Biometeorology | Secci S.,Fedi Impianti Srl
Renewable Energy | Year: 2011

In the present work a computation of wind shear coefficients (WSCs) based on 1-h measured wind data has been performed by three stations located over coastal sites in Southern Italy, i.e., Brindisi (BR), Portoscuso (PS) and Termini Imerese (TI). Wind observations have been collected through a 6-year period (January 1, 1997 to December 31, 2002) by wind mast recording at the same two sensor heights (i.e., 10 and 50m AGL), thus enabling a proper wind profile analysis. WSC overall mean values were found to be 0.271 at BR, 0.232 at PS, and 0.150 at TI. In addition, a detailed analysis has been carried out to describe the WSC yearly, monthly and diurnal variation, as well as by wind direction. The characteristics of z0 surface roughness length have been also investigated as an estimate for neutral stability conditions only, resulting in overall mean values of 0.526m at BR, 0.287m at PS, and 0.027m at TI. The z0 variation by year, month and hour of the day, as well as by wind direction, has been analysed, too. The European "Corine Land Cover 2000" classification of the study areas has been employed to deeply investigate the land use influence on both WSC and z0 characteristics as a function of wind direction.Based on temperature and pressure surface measurements, the computation of site-specific mean air density as well as monthly variation has been also performed.Site-related 50-m wind resource has been assessed by means of wind roses and wind speed frequency distributions, as well as Weibull's parameters. The potential turbine-converted wind energy yield has been also investigated, enabling to detect, for each site, the most suitable 50-m hub height turbine model regardless of its rated power. Furthermore, a number of comparisons have been made to assess the discrepancy in 50-m energy yield resulting if using data extrapolated from 10 m, both with 0.143 default and overall mean WSC value, instead of actually 50-m measured data. © 2010 Elsevier Ltd. Source


Gualtieri G.,CNR Institute for Biometeorology | Secci S.,Fedi Impianti Srl
Renewable Energy | Year: 2011

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 (α), 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).The main purpose of this work is to analyse and compare the skill of some of most commonly used extrapolation methods once applied to a case study over a coastal location in Southern Italy. These are LogLL, LogL, as well as PL by using different approaches to estimate α (i.e., PL-α, PL-SR, PL-SH, and PL-PD). In doing so, the influence of atmospheric stability and surface roughness (z0), with special attention to their variability with time and wind characteristics, has been also investigated. In addition, a comparison among the three α-estimating methods by SR, SH and PD has been carried out. A 6-year (1997-2002) 1-h meteorological dataset, including wind measurements at 10 and 50 m, has been used. In particular, the first 5 years were used to analyse site meteorology, stability conditions, and wind pattern, derive α and z0, as well as compare α-estimating methods, while the latter (2002) to test the skill of the extrapolation methods. Starting from 10-m wind speed observations, the computation of 50-m wind speed and power density, as well as wind resource and energy yield, has been made. The Weibull distribution and related parameters have been used for the wind resource assessment, while AF, CF and AEY were calculated to evaluate the potential wind energy yield. © 2011 Elsevier Ltd. Source


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. Thepower 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 50m, 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 100m 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. © 2013 Elsevier Ltd. Source


Gualtieri G.,CNR Institute for Biometeorology | Secci S.,Fedi Impianti Srl
Renewable Energy | Year: 2012

An accurate wind shear model is crucial to extrapolate the observed wind resource from the available lower heights to the steadily increasing hub height of modern wind turbines. Among power law (PL) and logarithmic law (LogL), i.e., the two most commonly used analytical models, the former was found to give a better representation of wind speed profiles and thus set as the reference model addressed by the present study. As well as commonly used for vertical extrapolation of 1-h wind speed records, the PL wind profile was proved to be consistent with the Weibull wind speed distribution. As a matter of fact, Justus and Mikhail suggested being more useful to deal with the full range of wind speed, such as required to specify the wind speed probability distribution, rather than using the "instantaneous" records. Therefore, in this work a comparison is proposed between these two PL-based extrapolation approaches to the turbine hub height, not only in terms of wind resource and energy yield computation skill, but also of simplicity and usefulness: (i) extrapolation of 1-h wind speed records, and (ii) extrapolation of the Weibull distribution. In particular, the models of 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). In addition, a comparison of models in estimating wind shear coefficient was carried out. PL extrapolation models have been tested over a coastal and complex-topography location in Tuscany, Italy, where thus the role played by atmospheric stability and surface roughness (z 0), as well as their variability with time and wind characteristics, required to be deeply investigated. A 5-year (1997-2001) 1-h dataset, including wind measurements at 10 and 100m, has been used. Starting from 10-m wind speed observations, the computation of 100-m extrapolated wind resource, Weibull distribution and energy yield has been made, where the latter was performed once a site most efficient 100-m hub height turbine was detected and then applied. © 2012 Elsevier Ltd. Source

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