A wind resource assessment of two sites in the Waterloo region, WRESTRC and RIM Park, was conducted using wind speed, wind direction, temperature and pressure data collected from meteorological towers for over two years. The study was undertaken as part of the W3 Wind Energy Project, and the equipment was purchased from NRG Systems and R. M. Young Company. The data was filtered to reduce the effect of icing and tower shadow, and was analyzed using MATLAB software. Based on the mean wind speeds, small wind turbines less than 50 kW in capacity would be appropriate at both sites. Wind speeds tended to be stronger during the winter than the summer, and during the afternoon than the rest of the day. Both sites also exhibited a strong dominant wind direction -- from the northwest. Due to the terrain, the wind shear and turbulence intensity at WRESTRC were moderate when the wind flowed from the dominant direction, but very high from other directions. The wind shear and turbulence intensity at RIM Park were consistently moderate in all directions. Although the terrain seems more complex at WRESTRC, the wind speed distribution and estimated annual energy production were higher at WRESTRC than at RIM Park, which indicates that it is a more viable site. The estimated capacity factors ranged from 9.4% to 22% depending on the hub height, which is not nearly high enough to suggest a commercial wind farm would be viable at either site. A small 5 kW to 15 kW wind turbine in the Waterloo region could offset the electricity usage of an average home. A two-parameter power law model of wind shear was explored and compared with the standard one-parameter model. In terms of goodness-of-fit, the two-parameter model did perform better. But in terms of accuracy of extrapolation, it was not conclusively better or worse than a one-parameter model forced through the known data point closest to the prediction height. The relationship between turbulence intensity and measurement interval was examined. Since atmospheric flow is unsteady, they are not independent. The perceived turbulence intensity was found to increase exponentially with time intervals under 24 hours. Two linear regression-based Measure-Correlate-Predict methods were evaluated using long-term data from a weather station also at WRESTRC. The ordinary least squares method was considered the baseline given its simplicity. The variance ratio method improved upon it by ensuring that the variance of the wind speed distribution at the target site was preserved.

A wind resource assessment of two sites in the Waterloo region, WRESTRC and RIM Park, was conducted using wind speed, wind direction, temperature and pressure data collected from meteorological towers for over two years. The study was undertaken as part of the W3 Wind Energy Project, and the equipment was purchased from NRG Systems and R. M. Young Company. The data was filtered to reduce the effect of icing and tower shadow, and was analyzed using MATLAB software. Based on the mean wind speeds, small wind turbines less than 50 kW in capacity would be appropriate at both sites. Wind speeds tended to be stronger during the winter than the summer, and during the afternoon than the rest of the day. Both sites also exhibited a strong dominant wind direction -- from the northwest. Due to the terrain, the wind shear and turbulence intensity at WRESTRC were moderate when the wind flowed from the dominant direction, but very high from other directions. The wind shear and turbulence intensity at RIM Park were consistently moderate in all directions. Although the terrain seems more complex at WRESTRC, the wind speed distribution and estimated annual energy production were higher at WRESTRC than at RIM Park, which indicates that it is a more viable site. The estimated capacity factors ranged from 9.4% to 22% depending on the hub height, which is not nearly high enough to suggest a commercial wind farm would be viable at either site. A small 5 kW to 15 kW wind turbine in the Waterloo region could offset the electricity usage of an average home. A two-parameter power law model of wind shear was explored and compared with the standard one-parameter model. In terms of goodness-of-fit, the two-parameter model did perform better. But in terms of accuracy of extrapolation, it was not conclusively better or worse than a one-parameter model forced through the known data point closest to the prediction height. The relationship between turbulence intensity and measurement interval was examined. Since atmospheric flow is unsteady, they are not independent. The perceived turbulence intensity was found to increase exponentially with time intervals under 24 hours. Two linear regression-based Measure-Correlate-Predict methods were evaluated using long-term data from a weather station also at WRESTRC. The ordinary least squares method was considered the baseline given its simplicity. The variance ratio method improved upon it by ensuring that the variance of the wind speed distribution at the target site was preserved.

The Supergen wind research consortium is a group of research centres which undertake research primarily aimed at reducing the cost of offshore wind farming. Research is undertaken to apply the WRF mesoscale NWP model to the field of offshore wind resource assessment to assess its potential as an operational tool. WRF is run in a variety of configurations for a number of locations to determine and optimise a level of performance and assess how accessible that performance might be to an end user. Three studies set out to establish a level of performance at two different sites and improve performance through optimisation of model setup and post processing techniques. WRF was found to simulate wind speed to an appreciable level by reference to similar studies, though performance was found to vary throughout the course of the model runs and depending on the location. An average correlation coefficient of 0.9 was found for the Shell Flats resource assessment at 6-hourly resolution with an RMSE of 1.7ms-1. Performance at Scroby Sands was not at as high a level as that seen for Shell Flats with an average correlation coefficient for wind speed of 0.64 with an RMSE of 2ms-1. A range of variables were simulated by the model in the Shell Flats investigation to test the flexibility of the model output. Wind direction was produced to a moderate level of accuracy at 10-minute resolution while aggregated stability statistics showed the model had a good appreciation of the frequency of cases observed. Areas of uncertainty in model performance were addressed through model optimisation techniques including the generation of two ensembles and observational nudging. Both techniques were found to add value to the model output as well as improving performance. The difference between performance observed at Shell Flats and Scroby Sands shows that while the model clearly has inherent skill it is sensitive to the environment to which it is applied. In order to maximise performance, as large a computing resource as possible is recommended with a concerted effort to optimise model setup with the aim of allowing it to perform to its best ability. There is room for improvement in the application of mesoscale NWP to the field of offshore wind resource assessment but these results confirm an inherent skill in model performance. With the addition of further validation, improvements to model setup on a case by case basis and the application of optimisation techniques, it is anticipated mesoscale NWP can perform to a level which would justify its adoption operationally by the industry. The flexibility which can be offered relating to spatial and temporal coverage as well as the range of variables which can be produced make it an attractive option to developers if performance of a consistently high level can be established.

No description available.

An accurate wind resource assessment (WRA) is crucial in energy prediction as the power is directly proportional to the wind speed cubed. This thesis analyses the discrepancy between operational assessment and WRA for a wind farm located on a moderately complex terrain in Ireland. As part of this research, a WRA was undertaken and the results were input to two wind farm design tools, WindPro and WindSim, to estimate the annual energy production. Two and a half years of data was available from a 50m met mast. The data was analysed and filtered to ascertain and limit the usage of erroneous data. The dataset was then correlated with an available online dataset utilising the Measure Correlate Predict (MCP) module in WindPro in order to estimate the long term wind resource at the site. The wind resource was then used to determine the annual energy produced at the site using both WindPro and WindSim. A loss of 8% was applied to the energy calculations for comparison with the original WRA. The results demonstrate the energy production from the original energy prediction, undertaken by a leading wind consultancy prior to construction, was overestimated by an average 10.19% over the three years of operation. The averaged wind speed at hub height in the original WRA was 8.2m/s. However, the prediction undertaken using WindPro in this study estimated an average hub height wind speed of 8.0m/s while WindSim estimated an average of 7.36m/s. These differing results had a significant contribution to the difference in Annual Energy Production (AEP). The calculated annual energy results were an overestimation of energy production by an average of 8.10% utilising WindPro, while WindSim underestimated the energy output by just 0.11%.

One of the Sustainable Development Goals set in 2015 by the United Nations aims to ensure access to affordable, reliable, sustainable, and modern energy for all, increasing the global share of renewable energy to 32-35% by 2030. Moving towards this goal, the University of Trento funded the interdepartmental strategic project ERiCSol (Energie Rinnovabili e Combustibili Solari), in order to promote the research on renewable energy storage and solar fuels. The research activity presented in this thesis lies in the framework of this project, focusing on the development of new advanced simulation approaches to improve the estimation of the wind resource availability and the related power production for Italian wind farms in complex terrain. The wind farms, operated by the company AGSM S.p.A., are located in two different geographical contexts: Rivoli Veronese and Affi are at the inlet of the Adige Valley, while Casoni di Romagna and Carpinaccio Firenzuola, are on the crest of the Apennines close to the borders between the provinces of Bologna e Firenze. The analysis of data from year-long field measurements highlighted the different peculiarities of these areas. The wind farms at the mouth of the Adige Valley are influenced by a daily periodic thermally-driven circulation, characterised by a nocturnal intense down-valley wind alternating with a diurnal weaker up-valley wind, while the Apennines wind farms are primarily affected by synoptic-scale winds. Simulations, with the mesoscale Weather Research and Forecasting (WRF) model, are performed and compared with field measurements in both cases, to highlight strengths and weaknesses. The results show that the model is able to capture with good accuracy wind speed and direction in the Apennines wind farms, while larger errors arise for Rivoli Veronese and Affi wind farms, where the intensity of the nocturnal down-valley wind is generally underestimated. Considering the former case, modelled and observed yearly wind speed density distributions are compared, in order to evaluate the impact of model errors in the estimation of the wind resource at these sites. Since reliable simulations of the wind resource are also essential to ensure the security in power transmission and to prevent penalties to energy operators, an analysis of the power production is also performed, to evaluate how errors in the estimate of the resource translate into errors in the estimate of the production. Considering the wind farms at the mouth of the Adige Valley, the research work mainly focuses on the evaluation of the impact of data assimilation by means of observational nudging on model results, in order to optimize the setup for operational forecasts. Different configurations are tested and compared, varying the temporal window for the assimilation of local data.

The UK has an abundant offshore wind resource with offshore wind farming set to grow rapidly over the coming years. Optimisation of energy production is of the utmost importance and accurate estimates of wind speed distributions are critical for the planning process. Synthetic aperture radar (SAR) data can provide synoptic, wide area wind field estimates at resolutions of a few kilometres and has great potential for wind resource assessment. This thesis addresses the key challenges for the operational implementation of SAR in this context; namely the accuracy of SAR wind retrievals and the ability of SAR to characterise the mean wind speed and wind power density. We consider the main stages of SAR wind retrieval; the retrieval algorithm; sources of a priori information; the optimal configuration of the retrieval system; and the challenges for and accuracy of SAR wind resource estimation. This study was conducted for the eastern Irish Sea in the UK, a region undergoing significant offshore wind energy development. A new wind retrieval algorithm was developed that implements a maximum a posterior probability (MAP) method drawn from Bayesian statistics. MAP was demonstrated to be less sensitive to input errors than the standard direction-based wind speed algorithm (DWSA) and provides a simple retrieval quality check via the error reduction ratio. Retrieval accuracy is strongly influenced by the quality of a priori information. The accuracy of two operationally viable a priori sources, mesoscale numerical weather prediction (NWP) data and WISAR image directions, was evaluated by comparison against in-situ wind observations and WERA coastal data. Results show that NWP wind speeds produce good wind speed and direction estimates with standard deviations of ¬±2 ms-1 and ±16o respectively. WISAR directions were less accurate producing standard deviations ranging from ±20o to ±29o, but were preferable when strong differences between NWP timesteps were observed. The accuracy of SAR wind retrievals was evaluated by comparison against in-situ wind observations. The MAP algorithm was found to provide modest improvements in retrieval accuracy over DWSA. Highest quality retrievals achieved using the CMOD5 forward model, producing wind speeds with a RMSE of 1.83 ms-1. Regarding the ability of SAR to estimate offshore wind resources, dataset density was found to be a controlling parameter. With 103 scenes available mean wind speeds were well characterised by comparison against in-situ observations and Wind Atlas results, while wind power density showed considerable errors. The accuracy of wind speed maps was further improved by accounting for wind direction and fetch effects upon the SAR wind distribution. A key strength of the SAR wind fields is their ability to identify the effect of mesoscale structures upon the surface wind field with atmospheric gravity waves observed in 30% of the images. These structures are shown to introduce wind speed fluctuations of up to ±2 ms-1 at scales of 5 to 10 km and may have significant implications for wind power prediction. These findings show that SAR may provide an important source of wide area wind speed observations as a complement to existing wind resource estimation techniques. SAR may be of particular use in coastal areas where complex wind fields are observed.

No description available.

The New European Wind Atlas (NEWA) was developed with an aim to provide high accuracy wind climate data for the region of EU and Turkey. Wind industry always seek for solid performance in wind resource assessment, and it is highly affected by the quality of modelled data. The aim of this study is to validate the newly developed wind atlas for two onshore sites in Sweden. Wind resource assessment is conducted using NEWA mesoscale data as wind condition of the sites. AEP estimation is performed using two different simulation tools, and the results of estimation are compared to the actual SCADA data for the validation of NEWA. During the process of simulation, downscaling is executed for the mesoscale data to reflect micro terrain effects. The result of the current study showed that NEWA mesoscale data represents wind climate very well for the onshore site with simple terrain. On the other hand, NEWA provided overestimated wind speeds for the relatively complex onshore site with forested areas. The overestimation of wind speed led to predict AEP significantly higher than the measurements. The result of downscaling showed only little difference to the original data, which can be explained by the sites’ low orographic complexity. This study contributes to a deeper understanding of NEWA and provides insights into its validity for onshore sites. It is beyond the scope of this study to investigate whole region covered by NEWA. A further study focusing on sites with higher orographic complexity or with cold climate is recommended to achieve further understanding of NEWA.

Many parameters used for Wind Resource Assessment (WRA) have uncertainty and variability, yet are input into the process as single values. The extent of the uncertainty or variance may not be known, and may or may not be significant enough to affect output. This Thesis focused on the energy calculation element of WRA, to assess the affect that errors (uncertainty) in three key user inputs had on the energy results. A parameter was chosen from each of the main groups influencing the energy calculation: wind speed (atmosphere), surface roughness (site conditions), and power curve (turbine technology). Reasonable variation due to uncertainty for wind speed and power curve were taken from other studies and their application simplified. Roughness change was assessed over the 5 classes (Class 0 (water) to 4 (dense forest/city)). WindPRO software was used to calculate the Annual Energy Production (AEP) and applied to three different wind turbine generators at the same coordinate. A sensitivity analysis was done on the AEP results using a hybrid One-At-a-Time Local Sensitivity Analysis by determining percentage changes from baselines and an overall rate of change for those key input parameters. The results showed that roughness class change effect was not linear. Changing from Class 0 to 1, AEP was on average -8±1%. Class 1 to 2 change was on average ‑12±1%. Class 2 to 3 change was on average -20±2%. Class 3 to 4 change was on average -29±2%. The wind speed change effect was found to be roughly linear. If mean wind speed has an error of ±10%, the AEP could be expected to be out by approximately +18/‑17% with a standard deviation of +4/-3%. The power curve change effect was also roughly linear. A PC±9% error leads to an approximate +6/-7% AEP error with a standard deviation of ±1%. Roughness class change was the most sensitive parameter to AEP with a 14.5 average rate of change, followed by wind speed at 1.8, then power curve with a 0.8 rate. Results compared reasonably well with other relevant studies.