• Energy Research

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.

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 50 m 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 z(0) surface roughness length have been also investigated as an estimate for neutral stability conditions only, resulting in overall mean values of 0.526 m at BR, 0.287 m at PS, and 0.027 m at TI. The z(0) 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 z(0) 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.

A novel method was developed to detect the optimal onshore wind farm layout driven by the characteristics of all commercially-available wind turbines. A huge number of turbine combinations (577) was processed, resulting in 22,721 generated layouts. Various assumptions and constraints were considered, mostly derived from the literature, including site features, wind conditions, and layout design. For the latter, an irregularly staggered turbine array configuration was assumed. Wake effects were simulated through the Jensen's model, while a typical turbine thrust coefficient curve as a function of wind speed was originally developed. A detailed cost model was used, with levelized cost of energy selected as primary and capacity factor as secondary objective function. The self-organizing maps were used to address a thorough analysis, proving to be a powerful means to straightforwardly achieve a comprehensive pattern of wind farm layout optimization. In general, the two optimization functions basically match, while for higher wind potential sites, increasing capacity factor did not necessarily result in decreasing levelized cost of energy. The latter may be minimised by reducing the total number of turbines or the overall wind farm capacity, as well as maximising rotor diameters or minimising rated wind speeds; increasing rated power or hub height is only beneficial for mid-potential sites. The mere maximisation of wind farm energy production is a misleading target, as corresponding to mid-to-high values of levelized cost of energy. In contrast to previous studies, the use of turbines with different rated power, rotor diameter or hub height should be avoided.

A wind turbine (WT) site optimization procedure was developed and applied on two different onshore and one offshore sites, supplied with tall met masts and belonging to exemplary wind climates. In addition to detecting the most suitable WT for each site, Kohonen's self-organizing maps (SOMs) were employed to improve investigation of those parameters mostly influencing site optimization. Three years (2013-2015) of 1-h vertical observations from local met masts and a database of 377 onshore and 23 offshore commercial WTs were used. As a result, maximizing capacity factor (CF) was confirmed as a good objective function, though not the best, which was minimizing levelized cost of energy (LCoE). In general, these two conditions do not necessarily match: variously setting WT parameters may either result in an LCoE reduction or CF increase, but both conditions do not occur concurrently. A key finding was that minimum LCoE cannot be achieved by indefinitely increasing the WT hub height, but rather through detection of an optimum value obtained as a unique solution of the optimization procedure. Furthermore, the capability of SOM to recognise the cluster structure of all parameters influencing WT site optimization shed further light on their mutual relationship, thus proving to be an ideal tool to address the non-convex nature of this issue.

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.

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.

Among the main grid-based wind farm layout optimization studies addressed in the literature, 14 layouts have been recomputed by selecting the levelized cost of energy as a primary objective function. Relying on 120 wind turbine combinations, a previously developed optimization method targeting best turbine selection has then been applied. All literature layouts were optimized, as capacity factors were (slightly) increased (78.89-80.90 to 83.02-83.07%), while levelized costs of energy were (significantly) reduced (130.37-370.42 to 54.01-142.64 $/MWh). This study concluded that neither the discrete nor the continuous optimization model can be recommended in all scenarios. In general, a capacity factor increase does not necessarily imply a decrease in levelized cost of energy. The latter may be minimized by decreasing the overall wind farm capacity, the number of turbines, or selecting turbines with lower rotor diameters or rated powers. By contrast, capacity factor may be maximized by installing turbines with higher hub heights or lower rated speeds. Contradicting various findings, using turbines with different rotor diameters, rated powers or hub heights is not recommended to minimize the levelized cost of energy. Although addressed within several optimization studies, maximization of energy production is a misleading target, as involving the highest costs of energy.

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 (alpha) over bar, a number of methods have been developed to estimate PL exponent alpha when only surface data are available, such as those by Spera and Richards (SR), Smedman-Hogstrom and Hogstrom (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 a (i.e., PL-(alpha) over bar, PL-SR, PL-SH, and PL-PD). In doing so, the influence of atmospheric stability and surface roughness (z(0)), with special attention to their variability with time and wind characteristics, has been also investigated. In addition, a comparison among the three alpha-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 a and z(0), as well as compare alpha-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.

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-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). 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 100 m, 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.