CNR Institute for Biometeorology

Bologna, Italy

CNR Institute for Biometeorology

Bologna, Italy
SEARCH FILTERS
Time filter
Source Type

Gualtieri G.,CNR Institute for Biometeorology
Journal of Wind Engineering and Industrial Aerodynamics | Year: 2017

Only relying on surface information, the Deaves and Harris (DH) model was tested in predicting annual mean wind speed, Weibull distribution and energy yield at turbine hub heights of 40, 80 and 140 m. Based on 3-year (2011–2013) 10-min observations from the mast of Cabauw, the DH model was compared to the power law. The DH model was forced being applied for all stability conditions, although actually developed for the strongest neutral wind conditions (when about 75% overall wind energy may be extracted). The DH model was the finest at higher levels and its accuracy generally increased with height: at 80 m, biases of 2% (mean wind speed) and 6.06% (energy yield) were achieved, while at 140 m biases of 1% and 6.16% were obtained, respectively. Since affected by a high sensitivity to site's roughness length, an accurate assessment of this parameter proved to be main model's shortcoming. In any case, currently-achieved scores encourage further applications of the DH model, which should be deemed as a challenging wind energy research topic. Since valid over the entire boundary layer, it may be regarded as an ideal and certainly forward-looking tool for addressing modern multi-MW turbines whose hub heights steadily increase. © 2017 Elsevier Ltd


Gualtieri G.,CNR Institute for Biometeorology
Energy Conversion and Management | Year: 2017

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. © 2017 Elsevier Ltd


Originally developed and validated at the Cabauw (Netherlands) topographically flat onshore location, the α–I wind resource extrapolating method was tested at the FINO3 offshore site in the North Sea (Germany). The aim was to prove its validity also when applied over a substantially different environment in terms of surface characteristics and stability conditions. Data from local mast at 30, 80, and 100 m were used, with extrapolations to 80-m and 100-m turbine hub heights accomplished based on 30-m turbulence intensity observations. Trained over a 2-year period (2011–2012), the method was validated on year 2013. Similarly to the onshore application, the method was reliable in extrapolating wind speed to both 80 m and 100 m, with bias within 5%, NRMSE = 0.20 and r = 0.94. Conversely, scores were largely better than at the onshore site in predicting the annual energy yield, biased by 0.41–1.02% at 80 m, and 1.12–1.36% at 100 m. The method proved to be highly sensitive to the stability classification, as not considering this option increased its biases to 4.51–5.93% at 80 m, and 7.46–8.23% at 100 m. Method's reliability might suitably help reduce the number of masts installed throughout a large offshore area. © 2017 Elsevier Ltd


Haworth M.,CNR Institute for Biometeorology | Elliott-Kingston C.,University College Dublin | McElwain J.C.,University College Dublin
Oecologia | Year: 2013

Plant stomata display a wide range of short-term behavioural and long-term morphological responses to atmospheric carbon dioxide concentration ([CO2]). The diversity of responses suggests that plants may have different strategies for controlling gas exchange, yet it is not known whether these strategies are co-ordinated in some way. Here, we test the hypothesis that there is co-ordination of physiological (via aperture change) and morphological (via stomatal density change) control of gas exchange by plants. We examined the response of stomatal conductance (Gs) to instantaneous changes in external [CO2] (Ca) in an evolutionary cross-section of vascular plants grown in atmospheres of elevated [CO2] (1,500 ppm) and sub-ambient [O2] (13. 0 %) compared to control conditions (380 ppm CO2, 20. 9 % O2). We found that active control of stomatal aperture to [CO2] above current ambient levels was not restricted to angiosperms, occurring in the gymnosperms Lepidozamia peroffskyana and Nageia nagi. The angiosperm species analysed appeared to possess a greater respiratory demand for stomatal movement than gymnosperm species displaying active stomatal control. Those species with little or no control of stomatal aperture (termed passive) to Ca were more likely to exhibit a reduction in stomatal density than species with active stomatal control when grown in atmospheres of elevated [CO2]. The relationship between the degree of stomatal aperture control to Ca above ambient and the extent of any reduction in stomatal density may suggest the co-ordination of physiological and morphological responses of stomata to [CO2] in the optimisation of water use efficiency. This trade-off between stomatal control strategies may have developed due to selective pressures exerted by the costs associated with passive and active stomatal control. © 2012 Springer-Verlag.


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.


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.


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.


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.


Gualtieri G.,CNR Institute for Biometeorology
Renewable Energy | Year: 2015

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 80m. WT hub heights of 40 and 80m have been targeted for the extrapolation, being accomplished based on turbulence intensity observations at 10 and 20m. 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 10-40m (NRMSE=0.16, r=0.95) than 10-80 and 20-80m (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 40m, and r=0.96 at 80m). 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. © 2015 Elsevier Ltd.


Maselli F.,CNR Institute for Biometeorology
International Journal of Remote Sensing | Year: 2011

The objective of this article is to develop and test a methodology capable of using medium spatial resolution satellite imagery to improve forest-area statistics derived from ground sampling. The methodology builds on the evidence that multitemporal Normalized Difference Vegetation Index (NDVI) images bring significant information on the spatial distribution of forest surfaces. Consequently, Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI images are potentially useful to improve forest-area assessment based on ground data. This expectation is verified in Tuscany (central Italy) using forest-area references extracted from the Coordination of Information on Environment (CORINE) land-cover map. The accuracy of forest-area statistics obtained at province level by different reference samplings is first assessed. Next, locally calibrated regression analyses are applied to multitemporal MODIS NDVI images in order to obtain per-pixel forest-area estimates. Two statistical methods (the direct expansion and the regression estimator) are finally used to combine these estimates with the ground data and produce corrected per-province statistics. The experimental results confirm that MODIS NDVI data contain relevant information on forest distribution, which can be efficiently extended over the land surface by locally calibrated regressions. The obtained estimates can be combined with the ground data for enhancing forest-area assessment at province level. To this aim, the regression estimator gives the best performance for all sampling densities of the reference data. © 2011 Taylor & Francis.

Loading CNR Institute for Biometeorology collaborators
Loading CNR Institute for Biometeorology collaborators