• Energy Research

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.

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.

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 10-50m 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 (10-80m 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(?)-?lower and the PL(?)-?I 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 ?=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.

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 10-50m 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 (10-80m 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(?)-?lower and the PL(?)-?I 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 ?=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 reliability of ERA5 reanalyses for directly predicting wind resources and energy production has been assessed against observations from six tall towers installed over very heterogeneous sites around the world. Scores were acceptable at the FINO3 (Germany) offshore platform for both wind speed (bias within 1%, r = 0.95−0.96) and capacity factor (CF, at worst biased by 6.70%) and at the flat and sea-level site of Cabauw (Netherlands) for both wind speed (bias within 7%, r = 0.93−0.94) and CF (bias within 6.82%). Conversely, due to the ERA5 limited resolution (~31 km), large under-predictions were found at the Boulder (US) and Ghoroghchi (Iran) mountain sites, and large over-predictions were found at the Wallaby Creek (Australia) forested site. Therefore, using ERA5 in place of higher-resolution regional reanalysis products or numerical weather prediction models should be avoided when addressing sites with high variation of topography and, in particular, land use. ERA5 scores at the Humansdorp (South Africa) coastal location were generally acceptable, at least for wind speed (bias of 14%, r = 0.84) if not for CF (biased by 20.84%). However, due to the inherent sea–land discontinuity resulting in large differences in both surface roughness and solar irradiation (and thus stability conditions), a particular caution should be paid when applying ERA5 over coastal locations.

The reliability of ERA5 reanalyses for directly predicting wind resources and energy production has been assessed against observations from six tall towers installed over very heterogeneous sites around the world. Scores were acceptable at the FINO3 (Germany) offshore platform for both wind speed (bias within 1%, r = 0.95−0.96) and capacity factor (CF, at worst biased by 6.70%) and at the flat and sea-level site of Cabauw (Netherlands) for both wind speed (bias within 7%, r = 0.93−0.94) and CF (bias within 6.82%). Conversely, due to the ERA5 limited resolution (~31 km), large under-predictions were found at the Boulder (US) and Ghoroghchi (Iran) mountain sites, and large over-predictions were found at the Wallaby Creek (Australia) forested site. Therefore, using ERA5 in place of higher-resolution regional reanalysis products or numerical weather prediction models should be avoided when addressing sites with high variation of topography and, in particular, land use. ERA5 scores at the Humansdorp (South Africa) coastal location were generally acceptable, at least for wind speed (bias of 14%, r = 0.84) if not for CF (biased by 20.84%). However, due to the inherent sea–land discontinuity resulting in large differences in both surface roughness and solar irradiation (and thus stability conditions), a particular caution should be paid when applying ERA5 over coastal locations.

Exploiting wind resource is a good alternative in spite of using traditional not renewable and polluting energy sources. However, besides landscape restrictions and administrative practice complexity, it is generally hard to locate a site eligible for aeolic exploitation as well as to assess its related wind resource. As a matter of fact, an expensive wind measuring campaign should be carried out for that, through at least one year long period, at the height aeolic plants typically work (60 to 80 m), or to vertically extrapolate data collected by a 10-m anemometer. The present paper is the sequel of one previously carried out, which proved the use of meteorological model wind estimations to provide aeolic efficiency performances being comparable with those based on experimental data. In particular, the WRF-NMM prognostic meteorological model has been used to calculate wind estimations, which actually are part of a meteorological archive which was developed at LaMMA laboratory starting from numerical elaborations provided by the weather forecasting service. A sample application was performed through the installation of an aeolic plant in the industrial harbour of Livorno, Italy. After the wind resource pattern has been analysed by using typical distributions and statistical indicators, a site energy efficiency assessment has been carried out by comparing three different kind of wind turbines basing on rated power: the sizes of 1300, 2000 and 3000 KW have been taken into account. In particular, the comparison has been made between NORDEX N60, ENERCON E82 and ECOTÈCNIA 100 wind turbines.

Exploiting wind resource is a good alternative in spite of using traditional not renewable and polluting energy sources. However, besides landscape restrictions and administrative practice complexity, it is generally hard to locate a site eligible for aeolic exploitation as well as to assess its related wind resource. As a matter of fact, an expensive wind measuring campaign should be carried out for that, through at least one year long period, at the height aeolic plants typically work (60 to 80 m), or to vertically extrapolate data collected by a 10-m anemometer. The present paper is the sequel of one previously carried out, which proved the use of meteorological model wind estimations to provide aeolic efficiency performances being comparable with those based on experimental data. In particular, the WRF-NMM prognostic meteorological model has been used to calculate wind estimations, which actually are part of a meteorological archive which was developed at LaMMA laboratory starting from numerical elaborations provided by the weather forecasting service. A sample application was performed through the installation of an aeolic plant in the industrial harbour of Livorno, Italy. After the wind resource pattern has been analysed by using typical distributions and statistical indicators, a site energy efficiency assessment has been carried out by comparing three different kind of wind turbines basing on rated power: the sizes of 1300, 2000 and 3000 KW have been taken into account. In particular, the comparison has been made between NORDEX N60, ENERCON E82 and ECOTÈCNIA 100 wind turbines.