This dissertation develops sophisticated data analytic methods to analyze structural loads on, and power generation of, wind turbines. Wind turbines, which convert the kinetic energy in wind into electrical power, are operated within stochastic environments. To account for the influence of environmental factors, we employ a conditional approach by modeling the expectation or distribution of response of interest, be it the structural load or power output, conditional on a set of environmental factors. Because of the different nature associated with the two types of responses, our methods also come in different forms, conducted through two studies. The first study presents a Bayesian parametric model for the purpose of estimating the extreme load on a wind turbine. The extreme load is the highest stress level that the turbine structure would experience during its service lifetime. A wind turbine should be designed to resist such a high load to avoid catastrophic structural failures. To assess the extreme load, turbine structural responses are evaluated by conducting field measurement campaigns or performing aeroelastic simulation studies. In general, data obtained in either case are not sufficient to represent various loading responses under all possible weather conditions. An appropriate extrapolation is necessary to characterize the structural loads in a turbine’s service life. This study devises a Bayesian spline method for this extrapolation purpose and applies the method to three sets of load response data to estimate the corresponding extreme loads at the roots of the turbine blades. In the second study, we propose an additive multivariate kernel method as a new power curve model, which is able to incorporate a variety of environmental factors in addition to merely the wind speed. In the wind industry, a power curve refers to the functional relationship between the power output generated by a wind turbine and the wind speed at the time of power generation. Power curves are used in practice for a number of important tasks including predicting wind power production and assessing a turbine’s energy production efficiency. Nevertheless, actual wind power data indicate that the power output is affected by more than just wind speed. Several other environmental factors, such as wind direction, air density, humidity, turbulence intensity, and wind shears, have potential impact. Yet, in industry practice, as well as in the literature, current power curve models primarily consider wind speed and, with comparatively less frequency, wind speed and direction. Our model provides, conditional on a given environmental condition, both the point estimation and density estimation of the power output. It is able to capture the nonlinear relationships between environmental factors and wind power output, as well as the high-order inter- action effects among some of the environmental factors. To illustrate the application of the new power curve model, we conduct case studies that demonstrate how the new method can help with quantifying the benefit of vortex generator installation, advising pitch control adjustment, and facilitating the diagnosis of faults.

The release of carbon dioxide from the burning of fossil fuels for energy is thought to be one of the main contributors to increasing greenhouse gas concentrations in the atmosphere. This increase is reported to be causing irreversible changes to the earth’s climate, giving rise to temperature increases and other consequent alterations in weather patterns. Amid growing concern about climate change and its impact on the world, targets have been set through agreements such as the Kyoto Protocol and via European Union and government legislation to force countries to work towards decreasing their greenhouse gas emissions. Increasing the contribution that renewable sources make to energy production is a major part of most countries’ strategies to meet these targets. The UK has arguably the greatest potential for wind power generation in Europe and the government is seeking to build upon this strength by exploiting the resource further. The liberalised electricity market infers a requirement for private investment in order to develop the wind portfolio and this in turn requires financial and economic feasibility. Given the changes in weather patterns that are projected to occur over the course of the coming century, the possibility that this could change the UK’s wind resource, and hence the financial viability of wind power developments, must be addressed. Other aspects of how changes in the wind resource could impact on the operation of the fragmented electricity system ought also to be considered in this context. This thesis attempts to understand how the current generation of climate models project surface wind climate to change, and seeks to make the model information relevant at a site level by using statistical and physical modelling techniques. The projected changes indicated by the models are small, and it has been assessed that potential impacts on the electricity system, from project feasibility to the potential for inclusion of wind in the generation mix, will be limited.

No description available.

Increasing wind penetration adds to the importance of enhancing the reliability of wind, to mitigate the magnitude and frequency of changes in electricity generation. This work addresses how improvements can be made to reliability through the geographic dispersal of wind farms in Ontario, Canada, using modeled North American Regional Reanalysis data. Optimal configurations of wind farm locations are determined according to two criteria. The first selects combinations of wind farms that follow temporal demand patterns, by maximizing the difference between the energy price and the cost of electricity. The second attempts to select combinations of wind farms that minimize the coefficient of variation in the aggregate output. It is found that there are no wind regimes in Ontario that match demand sufficiently for a viable development strategy, but that combinations of as few as three locations can reduce the coefficient of variation by over 30%, compared to a single region.

Increasing wind penetration adds to the importance of enhancing the reliability of wind, to mitigate the magnitude and frequency of changes in electricity generation. This work addresses how improvements can be made to reliability through the geographic dispersal of wind farms in Ontario, Canada, using modeled North American Regional Reanalysis data. Optimal configurations of wind farm locations are determined according to two criteria. The first selects combinations of wind farms that follow temporal demand patterns, by maximizing the difference between the energy price and the cost of electricity. The second attempts to select combinations of wind farms that minimize the coefficient of variation in the aggregate output. It is found that there are no wind regimes in Ontario that match demand sufficiently for a viable development strategy, but that combinations of as few as three locations can reduce the coefficient of variation by over 30%, compared to a single region.

As the demand for safe and clean electricity increases, the New Zealand wind energy industry seems poised to expand. Many generating companies have projects in the planning stage and there are likely to be many more potential sites yet to be identified. Reliable wind climate predictions over a wide area and for different heights above grounds are often vital to determine the viability of wind farm projects. This study investigates the use of meteorological mesoscale models to determine the wind and energy resource, particularly in areas of complex terrain. Complex terrain environments are likely to be typical of where New Zealand wind energy developments will take place. Using the prognostic mesoscale meteorological model TAPM (The Air Pollution Model), regions of relatively high mean wind speed were identified for a number of regions, including Banks Peninsula and parts of Canterbury and Otago. The simulations were conducted for a one-year period (2001) and at different heights above ground level. Depending on the resolution of the model calculations, speed-up effects from the forcing of some topographic features were accounted for by this model. Where the modelling was considered reliable, hourly wind data were obtained from grid points within the inner grid and used as input data for the industry-standard wind energy assessment model WAsP (The Wind Atlas Analysis and Application Program). As WAsP is able to account for detailed topography and surface roughness features, wind and energy predictions at a specific site or over a wider area surrounding the site were made. Limitations of both models in complex terrain were identified. These limitations were due to a number of factors, including the grid spacing used for mesoscale model calculations, the complexity of the terrain, and difficulties in modelling some regional scale airflow regimes. Being aware of when and where model limitations are likely to occur is important in being able to overcome and account for them.

No description available.

Incorporation of wind energy into the electricity generation system requires a detailed analysis of wind speed in order to minimize system balancing cost and avoid a significant mismatch between supply and demand. Power generation and consumption in the electricity networks have to be balanced every minute, therefore it is necessary to study wind speed on a one-minute time scale. In this thesis, we examine the statistical characteristics of one-minute average values of wind speed. One-minute wind speed is available from a single site in Great Britain while there are records of ten-minute wind speed available. We apply a modified Gibbs sampling algorithm to generate one-minute wind speed required for optimization modelling from the available ten-minute wind speed. System balancing costs are estimated through optimization modelling of the short-term electricity generation with wind energy contributing to the total supply. Two main drivers of additional system cost caused by wind energy are variability and unpredictability of one-minute wind speed. Further, a linear mathematical optimization model for a problem of short-term electricity generation is presented to calculate an additional balancing cost that appears as a result of wind energy variability. It is then shown that this additional balancing cost can be estimated using the statistical characteristics of wind energy present in the system. The unpredictable characteristic of wind speed is analysed with the techniques of stochastic programming. Uncertainty of the expected wind speed is represented through scenario trees and stochastic linear optimization models are used to calculate the extra cost due to uncertainty. Alternative optimization models are compared by calculating the additional balancing cost and the extent of imbalance between power generation and consumption in the system.

No description available.

Wind power is one of the world fastest growing electricity sources in the world and has since 1990 roughly been doubling in capacity every four years. The industry boom and the followed technology development has made the clean and inexhaustible wind energy, price competitive with fossil fuels, making wind power a sustainable tool to address climate change. The European and the North American market was long the dominant wind power markets in the world. Due to the rapid development in emerging economies a change in both market and turbine manufactures has occurred, making Asia the largest market in the world, housing five of the top ten wind power manufactures in 2015. This thesis focus on business model research in Envision Energy, a Chinese wind turbine manufacture, which in short time has gone from being a new entrant to one of the top manufactures in the world, focusing on quality and technology innovation. The research combines primary data from a semi structured interview with secondary data about Envision, the wind industry and the Chinese wind industry. By exploring Envisions business model and the context they operate in, this study found some key factors for Envision successful business model and drivers to enter the international market. As a Chinese wind turbine manufacture, Envisions have done many things that stands out compared to their domestic competitors. Already in their initial stage they set out to establish international innovation centers to be present in the global technology hot spots and be up to date with the newest technology and solutions. Their global presence was made possible by requiting industry experts from competitors, which also came to Envision with the necessary technical know-how, market knowledge and industry networks. Focusing on technical innovation and emphasizing on quality, Envisions has developed new innovative turbine and software solutions, using globally recognized suppliers instead of the domestic suppliers mostly used by their Chinese competitors. Envision has become recognized as quality wind turbine provider in the Chinese market who takes system integrated life cycle approach to lower the cost of energy. With their vision of revolutionize the energy sector on a global scale, Envision recently entered the international space on three continents, with a generic internationalization strategy. The human resources are the core of Envisions business model and are the underlying factor of their rapid success in the Chinese market as they managed to provide reliable turbines when their competitors struggled. With the human resources Envision could establish themselves internationally short after their foundation. The combined knowledge from the international organization enabled them to develop innovative wind turbine solutions, while emphasizing on quality. With a system integrated life cycle approach Envision focus to lower the cost energy with wind turbine solutions and a software system which can enhance asset life and performance on any renewable energy asset. The vision to make an impact on the energy sector has been the main driver behind Envisions internationalization, even though external drivers are imminent.