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.

Validation of the FALL3D ash dispersion model using observations of the 2010 Eyjafjallajökull volcanic ash clouds

During April-May 2010 volcanic ash clouds from the Icelandic Eyjafjallajökull volcano reached Europe causing an unprecedented disruption of the EUR/NAT region airspace. Civil aviation authorities banned all flight operations because of the threat posed by volcanic ash to modern turbine aircraft. New quantitative airborne ash mass concentration thresholds, still under discussion, were adopted for discerning regions contaminated by ash. This has implications for ash dispersal models routinely used to forecast the evolution of ash clouds. In this new context, quantitative model validation and assessment of the accuracies of current state-of-the-art models is of paramount importance. The passage of volcanic ash clouds over central Europe, a territory hosting a dense network of meteorological and air quality observatories, generated a quantity of observations unusual for volcanic clouds. From the ground, the cloud was observed by aerosol lidars, lidar ceilometers, sun photometers, other remote-sensing instru- ments and in-situ collectors. From the air, sondes and multiple aircraft measurements also took extremely valuable in-situ and remote-sensing measurements. These measurements constitute an excellent database for model validation. Here we validate the FALL3D ash dispersal model by comparing model results with ground and airplane-based measurements obtained during the initial 14e23 April 2010 Eyjafjallajökull explosive phase. We run the model at high spatial resolution using as input hourly- averaged observed heights of the eruption column and the total grain size distribution reconstructed from field observations. Model results are then compared against remote ground-based and in-situ aircraft-based measurements, including lidar ceilometers from the German Meteorological Service, aerosol lidars and sun photometers from EARLINET and AERONET networks, and flight missions of the German DLR Falcon aircraft. We find good quantitative agreement, with an error similar to the spread in the observations (however depending on the method used to estimate mass eruption rate) for both airborne and ground mass concentration. Such verification results help us understand and constrain the accuracy and reliability of ash transport models and it is of enormous relevance for designing future operational mitigation strategies at Volcanic Ash Advisory Centers.

Discrete demand side control performance under dynamic building simulation: a heat pump application

This study presents the findings of applying a Discrete Demand Side Control (DDSC) approach to the space heating of two case study buildings. High and low tolerance scenarios are implemented on the space heating controller to assess the impact of DDSC upon buildings with different thermal capacitances, light-weight and heavy-weight construction. Space heating is provided by an electric heat pump powered from a wind turbine, with a back-up electrical network connection in the event of insufficient wind being available when a demand occurs. Findings highlight that thermal comfort is maintained within an acceptable range while the DDSC controller maintains the demand/supply balance. Whilst it is noted that energy demand increases slightly, as this is mostly supplied from the wind turbine, this is of little significance and hence a reduction in operating costs and carbon emissions is still attained.

Reconstruction of gusty wind speed time series from autonomous data logger records

The collection of wind speed time series by means of digital data loggers occurs in many domains, including civil engineering, environmental sciences and wind turbine technology. Since averaging intervals are often significantly larger than typical system time scales, the information lost has to be recovered in order to reconstruct the true dynamics of the system. In the present work we present a simple algorithm capable of generating a real-time wind speed time series from data logger records containing the average, maximum, and minimum values of the wind speed in a fixed interval, as well as the standard deviation. The signal is generated from a generalized random Fourier series. The spectrum can be matched to any desired theoretical or measured frequency distribution. Extreme values are specified through a postprocessing step based on the concept of constrained simulation. Applications of the algorithm to 10-min wind speed records logged at a test site at 60 m height above the ground show that the recorded 10-min values can be reproduced by the simulated time series to a high degree of accuracy.

Regional changes in wind energy potential over Europe using regional climate model ensemble projections

The impact of climate change on wind power generation potentials over Europe is investigated by considering ensemble projections from two regional climate models (RCMs) driven by a global climate model (GCM). Wind energy density and its interannual variability are estimated based on hourly near-surface wind speeds. Additionally, the possible impact of climatic changes on the energy output of a sample 2.5-MW turbine is discussed. GCM-driven RCM simulations capture the behavior and variability of current wind energy indices, even though some differences exist when compared with reanalysis-driven RCM simulations. Toward the end of the twenty-first century, projections show significant changes of energy density on annual average across Europe that are substantially stronger in seasonal terms. The emergence time of these changes varies from region to region and season to season, but some long-term trends are already statistically significant in the middle of the twenty-first century. Over northern and central Europe, the wind energy potential is projected to increase, particularly in winter and autumn. In contrast, energy potential over southern Europe may experience a decrease in all seasons except for the Aegean Sea. Changes for wind energy output follow the same patterns but are of smaller magnitude. The GCM/RCM model chains project a significant intensification of both interannual and intra-annual variability of energy density over parts of western and central Europe, thus imposing new challenges to a reliable pan-European energy supply in future decades.

Estimating the potential yield of small wind turbines in urban areas: a case study for Greater London, UK

To optimise the placement of small wind turbines in urban areas a detailed understanding of the spatial variability of the wind resource is required. At present, due to a lack of observations, the NOABL wind speed database is frequently used to estimate the wind resource at a potential site. However, recent work has shown that this tends to overestimate the wind speed in urban areas. This paper suggests a method for adjusting the predictions of the NOABL in urban areas by considering the impact of the underlying surface on a neighbourhood scale. In which, the nature of the surface is characterised on a 1 km2 resolution using an urban morphology database. The model was then used to estimate the variability of the annual mean wind speed across Greater London at a height typical of current small wind turbine installations. Initial validation of the results suggests that the predicted wind speeds are considerably more accurate than the NOABL values. The derived wind map therefore currently provides the best opportunity to identify the neighbourhoods in Greater London at which small wind turbines yield their highest energy production. The model does not consider street scale processes, however previously derived scaling factors can be applied to relate the neighbourhood wind speed to a value at a specific rooftop site. The results showed that the wind speed predicted across London is relatively low, exceeding 4 ms-1 at only 27% of the neighbourhoods in the city. Of these sites less than 10% are within 10 km of the city centre, with the majority over 20 km from the city centre. Consequently, it is predicted that small wind turbines tend to perform better towards the outskirts of the city, therefore for cities which fit the Burgess concentric ring model, such as Greater London, ‘distance from city centre’ is a useful parameter for siting small wind turbines. However, there are a number of neighbourhoods close to the city centre at which the wind speed is relatively high and these sites can only been identified with a detailed representation of the urban surface, such as that developed in this study.

Statistical-dynamical downscaling for wind energy potentials: evaluation and applications to decadal hindcasts and climate change projections

A statistical–dynamical downscaling (SDD) approach for the regionalization of wind energy output (Eout) over Europe with special focus on Germany is proposed. SDD uses an extended circulation weather type (CWT) analysis on global daily mean sea level pressure fields with the central point being located over Germany. Seventy-seven weather classes based on the associated CWT and the intensity of the geostrophic flow are identified. Representatives of these classes are dynamically downscaled with the regional climate model COSMO-CLM. By using weather class frequencies of different data sets, the simulated representatives are recombined to probability density functions (PDFs) of near-surface wind speed and finally to Eout of a sample wind turbine for present and future climate. This is performed for reanalysis, decadal hindcasts and long-term future projections. For evaluation purposes, results of SDD are compared to wind observations and to simulated Eout of purely dynamical downscaling (DD) methods. For the present climate, SDD is able to simulate realistic PDFs of 10-m wind speed for most stations in Germany. The resulting spatial Eout patterns are similar to DD-simulated Eout. In terms of decadal hindcasts, results of SDD are similar to DD-simulated Eout over Germany, Poland, Czech Republic, and Benelux, for which high correlations between annual Eout time series of SDD and DD are detected for selected hindcasts. Lower correlation is found for other European countries. It is demonstrated that SDD can be used to downscale the full ensemble of the Earth System Model of the Max Planck Institute (MPI-ESM) decadal prediction system. Long-term climate change projections in Special Report on Emission Scenarios of ECHAM5/MPI-OM as obtained by SDD agree well to the results of other studies using DD methods, with increasing Eout over northern Europe and a negative trend over southern Europe. Despite some biases, it is concluded that SDD is an adequate tool to assess regional wind energy changes in large model ensembles.

Projected changes in wind energy potentials over Iberia

Wind energy potential in Iberia is assessed for recent–past (1961–2000) and future (2041–2070) climates. For recent–past, a COSMO-CLM simulation driven by ERA-40 is used. COSMO-CLM simulations driven by ECHAM5 following the A1B scenario are used for future projections. A 2 MW rated power wind turbine is selected. Mean potentials, inter-annual variability and irregularity are discussed on annual/seasonal scales and on a grid resolution of 20 km. For detailed regional assessments eight target sites are considered. For recent–past conditions, the highest daily mean potentials are found in winter over northern and eastern Iberia, particularly on high-elevation or coastal regions. In northwestern Iberia, daily potentials frequently reach maximum wind energy output (50 MWh day−1), particularly in winter. Southern Andalucía reveals high potentials throughout the year, whereas the Ebro valley and central-western coast show high potentials in summer. The irregularity in annual potentials is moderate (<15% of mean output), but exacerbated in winter (40%). Climate change projections show significant decreases over most of Iberia (<2 MWh day−1). The strong enhancement of autumn potentials in Southern Andalucía is noteworthy (>2 MWh day−1). The northward displacement of North Atlantic westerly winds (autumn–spring) and the strengthening of easterly flows (summer) are key drivers of future projections.

The importance of accurate wind resource assessment for evaluating the economic viability of small wind turbines

The techno-economic performance of a small wind turbine is very sensitive to the available wind resource. However, due to financial and practical constraints installers rely on low resolution wind speed databases to assess a potential site. This study investigates whether the two site assessment tools currently used in the UK, NOABL or the Energy Saving Trust wind speed estimator, are accurate enough to estimate the techno-economic performance of a small wind turbine. Both the tools tend to overestimate the wind speed, with a mean error of 23% and 18% for the NOABL and Energy Saving Trust tool respectively. A techno-economic assessment of 33 small wind turbines at each site has shown that these errors can have a significant impact on the estimated load factor of an installation. Consequently, site/turbine combinations which are not economically viable can be predicted to be viable. Furthermore, both models tend to underestimate the wind resource at relatively high wind speed sites, this can lead to missed opportunities as economically viable turbine/site combinations are predicted to be non-viable. These results show that a better understanding of the local wind resource is a required to make small wind turbines a viable technology in the UK.

Future changes of wind energy potentials over Europe in a large CMIP5 multi-model ensemble

A statistical-dynamical downscaling method is used to estimate future changes of wind energy output (Eout) of a benchmark wind turbine across Europe at the regional scale. With this aim, 22 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble are considered. The downscaling method uses circulation weather types and regional climate modelling with the COSMO-CLM model. Future projections are computed for two time periods (2021–2060 and 2061–2100) following two scenarios (RCP4.5 and RCP8.5). The CMIP5 ensemble mean response reveals a more likely than not increase of mean annual Eout over Northern and Central Europe and a likely decrease over Southern Europe. There is some uncertainty with respect to the magnitude and the sign of the changes. Higher robustness in future changes is observed for specific seasons. Except from the Mediterranean area, an ensemble mean increase of Eout is simulated for winter and a decreasing for the summer season, resulting in a strong increase of the intra-annual variability for most of Europe. The latter is, in particular, probable during the second half of the 21st century under the RCP8.5 scenario. In general, signals are stronger for 2061–2100 compared to 2021–2060 and for RCP8.5 compared to RCP4.5. Regarding changes of the inter-annual variability of Eout for Central Europe, the future projections strongly vary between individual models and also between future periods and scenarios within single models. This study showed for an ensemble of 22 CMIP5 models that changes in the wind energy potentials over Europe may take place in future decades. However, due to the uncertainties detected in this research, further investigations with multi-model ensembles are needed to provide a better quantification and understanding of the future changes.

Probabilistic wind power forecasts with an inverse power curve transformation and censored regression

Forecasting wind power is an important part of a successful integration of wind power into the power grid. Forecasts with lead times longer than 6 h are generally made by using statistical methods to post-process forecasts from numerical weather prediction systems. Two major problems that complicate this approach are the non-linear relationship between wind speed and power production and the limited range of power production between zero and nominal power of the turbine. In practice, these problems are often tackled by using non-linear non-parametric regression models. However, such an approach ignores valuable and readily available information: the power curve of the turbine's manufacturer. Much of the non-linearity can be directly accounted for by transforming the observed power production into wind speed via the inverse power curve so that simpler linear regression models can be used. Furthermore, the fact that the transformed power production has a limited range can be taken care of by employing censored regression models. In this study, we evaluate quantile forecasts from a range of methods: (i) using parametric and non-parametric models, (ii) with and without the proposed inverse power curve transformation and (iii) with and without censoring. The results show that with our inverse (power-to-wind) transformation, simpler linear regression models with censoring perform equally or better than non-linear models with or without the frequently used wind-to-power transformation.

Bond durability in erbium:yttrium-aluminum-garnet laser-irradiated enamel

This study sought to evaluate the influence of thermocycling and water storage on the microtensile bond strength of composite resin bonded to erbium:yttrium-aluminum-garnet (Er:YAG)-irradiated and bur-prepared enamel. Eighty bovine incisors were selected and sectioned. Specimens were ground to produce a flat enamel surface. Samples were randomly assigned according to cavity preparation device: (I) Er:YAG laser and (II) high-speed turbine, and were subsequently restored with composite resin. They were subdivided according to the duration of water storage (WS)/number of thermocycles (TCs): 24 h WS/no TCs; 7 days WS/500 TCs; 1 month WS/2,000 TCs; 6 months WS/12,000 TCs. The teeth were sectioned into 1.0 mm(2)-thick slabs and subjected to tensile stress in a universal testing machine. Data were submitted to two-way analysis of variance (ANOVA) and Tukey`s test at a 0.05 significance level. The different periods of water storage and thermocycling did not influence the microtensile bond strength (A mu TBS) values in the Er:YAG laser-prepared groups. In bur-prepared enamel, the group submitted to 12,000 TCs/6 months` WS (IID) showed a significant decrease in bond strength values when compared to the group stored in water for 24 h and not submitted to thermocycling (IIA), but values were statistically similar to those obtained in all Er:YAG laser groups and in the bur- prepared groups degraded with 500 TCs/1 week WS (IIB) or 2,000 TCs/1 month WS (IIC). It may be concluded that adhesion of an etch-and-rinse adhesive to Er:YAG laser-irradiated enamel was not affected by the methods used to simulate degradation of the adhesive interface and was similar to adhesion in the bur-prepared groups in all periods of water storage and thermocycling.

Influence of dentin cavity surface finishing on micro-tensile bond strength of adhesives

The current trend toward minimal-invasive dentistry has introduced innovative techniques for cavity preparation. Chemical vapor deposition (CVD) and laser-irradiation technology have been employed as an alternative to the common use of regular burs in high-speed turbines. Objectives. The purpose of this study was to assess the influence of alternative techniques for cavity preparation on the bonding effectiveness of different adhesives to dentin, and to evaluate the morphological characteristics of dentin prepared with those techniques. Methods. One etch&rinse adhesive (OptiBond FL, Kerr) and three self-etch systems (Adper Prompt L-Pop, 3M ESPE; Clearfil SE Bond, Kuraray; Clearfil S3 Bond, Kuraray) were applied on dentin prepared with a regular bur in a turbine, with a CVD bur in a turbine, with a CVD tip in ultrasound and with an ErCr:YSGG laser. The micro-tensile bond strength (mu TBS) was determined after storage in water for 24 h at 37 degrees C, and morphological evaluation was performed by means of field -emission -gun scanning electron microscopy (Feg-SEM). Results. Feg-SEM evaluation revealed different morphological features on the dentin surface after the usage of both the conventional and alternative techniques for cavity preparation, more specifically regarding smear-layer thickness and surface roughness. CVD bur-cut, CVD ultra-sonoabraded and laser-irradiated dentin resulted in lower mu TBSs than conventionally bur-cut dentin, irrespective of the adhesive employed. Significance. The techniques, such as CVD diamond-bur cutting, CVD diamond ultra-sonoabrasion and laser-irradiation, used for cavity preparation may affect the bonding effectiveness of adhesives to dentin, irrespective of their acidity or approach. (C) 2007 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

Influence of Er,Cr : YSGG laser treatment on the microtensile bond strength of adhesives to dentin

Purpose: In light of the concept of minimally invasive dentistry, erbium lasers have been considered as an alternative technique to the use of diamond burs for cavity preparation. The purpose of this study was to assess the bonding effectiveness of adhesives to Er,Cr:YSGG laser-irradiated dentin using irradiation settings specific for cavity preparation. Materials and Methods: Fifty-four midcoronal dentin surfaces, obtained from sound human molars, were irradiated with an Er,Cr:YSGG laser or prepared with a diamond bur using a high-speed turbine. One etch-and-rinse (Optibond FL/Kerr) and three self-etching adhesives (Adper Prompt L-Pop/3M ESPE, Clearfil SE Bond/Kuraray, and Clearfil S-3 Bond/Kuraray) were used to bond the composite to dentin. The microtensile bond strength (mu TBS) was determined after 24 h of storage in water at 37 degrees C. The Kruskal-Wallis test was used to determine pairwise statistical differences (p < 0.05). Prepared dentin surfaces, adhesive interfaces, and failure patterns were analyzed using a stereo microscope and Field-emission gun Scanning Electron Microscopy (Feg-SEM). Results: Significantly lower mu TBS was observed to laser-irradiated than to bur-cut dentin (p < 0.05), irrespective of the adhesive employed. Feg-SEM photomicrographs of lased dentin revealed an imbricate patterned substrate and the presence of microcracks at the dentin surface. Conclusion: Morphological alterations produced by Er,Cr:YSGG laser-irradiation adversely influence the bonding effectiveness of adhesives to dentin. Keywords: dentin, adhesion, adhesives, laser, ErCr:YSGG.