The influence of deployment of planned wind farms on the power output and energy yield of wind farms located in close proximity at downwind sites is investigated. The atmospheric model Weather Research and Forecasting (WRF) has been employed to simulate wind resource and energy yield from wind farms in the Baja California region of Northern Mexico. Accuracy of predicted wind speed and wind turbine energy supply are evaluated against full-scale measurements from a met-mast and from each of five 2 MW turbines at the La Rumorosa wind-farm. For this wind farm location, wind speed distribution is predicted to within 1.4% and the energy supply from the farm predicted to within 5.25%. Accuracy depends on the boundary layer model and atmospheric dataset employed. Wind farms are modelled using the scheme developed by Fitch et al. (2012) in which a momentum sink and turbulent kinetic energy source are defined as a function of the turbine thrust coefficient and power output, each of which vary with wind speed as defined by the manufacturer. Planned farms of up to 72 MW installed capacity are defined in terms of turbine number, rated power and spacing at four sites such that each farm operates with a typical capacity factor. For a single farm of 2 MW turbines located 10 km upwind, wind speed at the case study wind-farm is reduced by 3.00% and power output reduced by up to 5.84%. These deficits increase if 5 MW turbines are deployed rather than 2 MW turbines due to the development of a longer far-wake. The net energy supply from several sites in the region is assessed.

O setor eólico está em processo de consolidação no Brasil desde o início dos anos 2000, e oportunidades de pesquisas e desenvolvimento estão presentes em todas as etapas do processo. Este trabalho apresenta uma relação entre os parâmetros de velocidade do vento, energia, efeito de esteira e intensidade de turbulência com diferentes alturas de turbinas existentes no mercado brasileiro, em três regiões distintas, Triunfo/PE, São João do Cariri/PB e São Martinho da Serra/RS. A finalidade do trabalho é auxiliar os desenvolvedores e os investidores de parques eólicos na tomada de decisão sobre as alturas de aerogeradores a serem consideradas em seus projetos eólicos, antecipando a avaliação criteriosa do recurso eólico local. Para tal, primeiramente são citadas referências de trabalhos similares disponíveis na literatura bem como é realizada a fundamentação teórica do estudo com as principais equações e modelos utilizados na área. A metodologia do cálculo é teórica e aplicada às ferramentas computacionais do WAsP para a modelagem do vento e do WindFarmer para avaliar a produção de energia elétrica, com adaptações específicas para cada projeto. Procedimentos estatísticos são efetuados a fim de se garantir que as análises contenham o menor nível possível de incerteza na identificação do recurso eólico de cada região. Os resultados do trabalho são apresentados comparativamente entre os sete modelos de aerogerador testados e também entre as três áreas estudadas. O comportamento das turbinas é consideravelmente diferente em todas as opções estudadas. Os modelos de aerogerador A e D são os que apresentam o maior ganho energético percentual com o incremento da altura da turbina com valores médios de 0,42% e 0,44% a cada metro. Já os modelos C e D apresentam as melhorias mais consideráveis em termos de redução de intensidade de turbulência e diminuição de perdas por efeito de esteira, conforme se aumenta a altura das turbinas. As áreas apresentam, também, recursos eólicos distintos entre elas. O projeto eólico de Triunfo é o que apresenta a maior geração de energia dentre os estudados, sendo 24,2% maior que em São Martinho da Serra e 45,0% maior que em São João do Cariri. Verifica-se, também, que caso a velocidade média do vento de longo prazo fosse dobrada em Triunfo, a energia líquida teria um acréscimo de 88%. Já em São Martinho da Serra este valor chegaria em 170% e em São João do Cariri em 220%. / The wind energy sector has been under consolidation in Brazil since the early 2000s. Opportunities for researches and developments are present at all stages of the process. This paper presents a link among wind speed, energy, wake effect and turbulence intensity parameters and the height of existing wind turbine models in Brazil, considering three distinct regions as Triunfo/PE, São João do Cariri/PB and São Martinho da Serra/RS. This paper also aims to support developers and investors in the decision making process in the wind turbines height that should be considered in its wind farms. To this end, the references of similar studies as well as the theoretical basis for the study, including the main equations and models, are presented. The calculation methodology is theoretical and it has been applied to the computational tools WAsP (wind modeling) and WindFarmer (evaluate the energy production), considering specific adaptations for each project. Statistical procedures are performed in order to ensure that the analyses contain the lowest possible level of uncertainty in the characterization of the wind resources in each region. The results are presented comparatively among the seven tested turbine models and also among the three studied areas. The turbine models behavior is considerably different for all options. The wind turbine models A and D are those with the highest percentage energy increase with increasing the turbine height. Their average values are 0.42% and 0.44% per meter height. The models C and D present the most considerable improvements in terms of turbulence intensity and wake effect reductions with increasing the turbine height. The different locations also present distinguished wind resources among them. Triunfo wind farm is the one with the highest energy generation, 24.2% higher than in São Martinho da Serra and 45.0% higher than in São João do Cariri. It is also noted that if the long term mean wind speed was doubled in the project locations, the energy would have an increase of 88% in Triunfo, 170% in São Martinho da Serra and 220% in São João do Cariri.

O setor eólico está em processo de consolidação no Brasil desde o início dos anos 2000, e oportunidades de pesquisas e desenvolvimento estão presentes em todas as etapas do processo. Este trabalho apresenta uma relação entre os parâmetros de velocidade do vento, energia, efeito de esteira e intensidade de turbulência com diferentes alturas de turbinas existentes no mercado brasileiro, em três regiões distintas, Triunfo/PE, São João do Cariri/PB e São Martinho da Serra/RS. A finalidade do trabalho é auxiliar os desenvolvedores e os investidores de parques eólicos na tomada de decisão sobre as alturas de aerogeradores a serem consideradas em seus projetos eólicos, antecipando a avaliação criteriosa do recurso eólico local. Para tal, primeiramente são citadas referências de trabalhos similares disponíveis na literatura bem como é realizada a fundamentação teórica do estudo com as principais equações e modelos utilizados na área. A metodologia do cálculo é teórica e aplicada às ferramentas computacionais do WAsP para a modelagem do vento e do WindFarmer para avaliar a produção de energia elétrica, com adaptações específicas para cada projeto. Procedimentos estatísticos são efetuados a fim de se garantir que as análises contenham o menor nível possível de incerteza na identificação do recurso eólico de cada região. Os resultados do trabalho são apresentados comparativamente entre os sete modelos de aerogerador testados e também entre as três áreas estudadas. O comportamento das turbinas é consideravelmente diferente em todas as opções estudadas. Os modelos de aerogerador A e D são os que apresentam o maior ganho energético percentual com o incremento da altura da turbina com valores médios de 0,42% e 0,44% a cada metro. Já os modelos C e D apresentam as melhorias mais consideráveis em termos de redução de intensidade de turbulência e diminuição de perdas por efeito de esteira, conforme se aumenta a altura das turbinas. As áreas apresentam, também, recursos eólicos distintos entre elas. O projeto eólico de Triunfo é o que apresenta a maior geração de energia dentre os estudados, sendo 24,2% maior que em São Martinho da Serra e 45,0% maior que em São João do Cariri. Verifica-se, também, que caso a velocidade média do vento de longo prazo fosse dobrada em Triunfo, a energia líquida teria um acréscimo de 88%. Já em São Martinho da Serra este valor chegaria em 170% e em São João do Cariri em 220%. / The wind energy sector has been under consolidation in Brazil since the early 2000s. Opportunities for researches and developments are present at all stages of the process. This paper presents a link among wind speed, energy, wake effect and turbulence intensity parameters and the height of existing wind turbine models in Brazil, considering three distinct regions as Triunfo/PE, São João do Cariri/PB and São Martinho da Serra/RS. This paper also aims to support developers and investors in the decision making process in the wind turbines height that should be considered in its wind farms. To this end, the references of similar studies as well as the theoretical basis for the study, including the main equations and models, are presented. The calculation methodology is theoretical and it has been applied to the computational tools WAsP (wind modeling) and WindFarmer (evaluate the energy production), considering specific adaptations for each project. Statistical procedures are performed in order to ensure that the analyses contain the lowest possible level of uncertainty in the characterization of the wind resources in each region. The results are presented comparatively among the seven tested turbine models and also among the three studied areas. The turbine models behavior is considerably different for all options. The wind turbine models A and D are those with the highest percentage energy increase with increasing the turbine height. Their average values are 0.42% and 0.44% per meter height. The models C and D present the most considerable improvements in terms of turbulence intensity and wake effect reductions with increasing the turbine height. The different locations also present distinguished wind resources among them. Triunfo wind farm is the one with the highest energy generation, 24.2% higher than in São Martinho da Serra and 45.0% higher than in São João do Cariri. It is also noted that if the long term mean wind speed was doubled in the project locations, the energy would have an increase of 88% in Triunfo, 170% in São Martinho da Serra and 220% in São João do Cariri.

O setor eólico está em processo de consolidação no Brasil desde o início dos anos 2000, e oportunidades de pesquisas e desenvolvimento estão presentes em todas as etapas do processo. Este trabalho apresenta uma relação entre os parâmetros de velocidade do vento, energia, efeito de esteira e intensidade de turbulência com diferentes alturas de turbinas existentes no mercado brasileiro, em três regiões distintas, Triunfo/PE, São João do Cariri/PB e São Martinho da Serra/RS. A finalidade do trabalho é auxiliar os desenvolvedores e os investidores de parques eólicos na tomada de decisão sobre as alturas de aerogeradores a serem consideradas em seus projetos eólicos, antecipando a avaliação criteriosa do recurso eólico local. Para tal, primeiramente são citadas referências de trabalhos similares disponíveis na literatura bem como é realizada a fundamentação teórica do estudo com as principais equações e modelos utilizados na área. A metodologia do cálculo é teórica e aplicada às ferramentas computacionais do WAsP para a modelagem do vento e do WindFarmer para avaliar a produção de energia elétrica, com adaptações específicas para cada projeto. Procedimentos estatísticos são efetuados a fim de se garantir que as análises contenham o menor nível possível de incerteza na identificação do recurso eólico de cada região. Os resultados do trabalho são apresentados comparativamente entre os sete modelos de aerogerador testados e também entre as três áreas estudadas. O comportamento das turbinas é consideravelmente diferente em todas as opções estudadas. Os modelos de aerogerador A e D são os que apresentam o maior ganho energético percentual com o incremento da altura da turbina com valores médios de 0,42% e 0,44% a cada metro. Já os modelos C e D apresentam as melhorias mais consideráveis em termos de redução de intensidade de turbulência e diminuição de perdas por efeito de esteira, conforme se aumenta a altura das turbinas. As áreas apresentam, também, recursos eólicos distintos entre elas. O projeto eólico de Triunfo é o que apresenta a maior geração de energia dentre os estudados, sendo 24,2% maior que em São Martinho da Serra e 45,0% maior que em São João do Cariri. Verifica-se, também, que caso a velocidade média do vento de longo prazo fosse dobrada em Triunfo, a energia líquida teria um acréscimo de 88%. Já em São Martinho da Serra este valor chegaria em 170% e em São João do Cariri em 220%. / The wind energy sector has been under consolidation in Brazil since the early 2000s. Opportunities for researches and developments are present at all stages of the process. This paper presents a link among wind speed, energy, wake effect and turbulence intensity parameters and the height of existing wind turbine models in Brazil, considering three distinct regions as Triunfo/PE, São João do Cariri/PB and São Martinho da Serra/RS. This paper also aims to support developers and investors in the decision making process in the wind turbines height that should be considered in its wind farms. To this end, the references of similar studies as well as the theoretical basis for the study, including the main equations and models, are presented. The calculation methodology is theoretical and it has been applied to the computational tools WAsP (wind modeling) and WindFarmer (evaluate the energy production), considering specific adaptations for each project. Statistical procedures are performed in order to ensure that the analyses contain the lowest possible level of uncertainty in the characterization of the wind resources in each region. The results are presented comparatively among the seven tested turbine models and also among the three studied areas. The turbine models behavior is considerably different for all options. The wind turbine models A and D are those with the highest percentage energy increase with increasing the turbine height. Their average values are 0.42% and 0.44% per meter height. The models C and D present the most considerable improvements in terms of turbulence intensity and wake effect reductions with increasing the turbine height. The different locations also present distinguished wind resources among them. Triunfo wind farm is the one with the highest energy generation, 24.2% higher than in São Martinho da Serra and 45.0% higher than in São João do Cariri. It is also noted that if the long term mean wind speed was doubled in the project locations, the energy would have an increase of 88% in Triunfo, 170% in São Martinho da Serra and 220% in São João do Cariri.

In this thesis, a mixed integer linear program is used to formulate the optimization process of a wind farm. As a start point, a grid was superimposed into the wind farm, in which grid points represent possible wind turbine locations. During the optimization process, proximity and wind interference between wind turbines were considered in order to found the power loss of the wind farm. Power loss was analyzed by using wind interference coefficient, which is a function of wind intensity interference factor (WIIF), weibull distribution and power of the wind turbines. Two different programs / Genetic Algorithm and Lingo, were used to solve the MILP optimization formula and results were compared for different cases in the conclusion part.

This research proposes an efficient reliability modeling and simulation methodology in power systems to include photovoltaic units, wind farms and storage. Energy losses by wake effect in a wind farm are incorporated. Using the wake model, wind shade, shear effect and wind direction are also reflected. For solar modules with titled surface, more accurate hourly photovoltaic power in a specific location is calculated with the physical specifications. There exists a certain level of correlation between renewable energy and load. This work uses clustering algorithms to consider those correlated variables. Different approaches are presented and applied to the composite power system, and compared with different scenarios using reliability analysis and simulation. To verify the results, reliability indices are compared with those from original data. As the penetration of renewables increases, the reliability issues will become more important because of the intermittent and non-dispatchable nature of these sources of power. Storage can provide the ability to regulate these fluctuations. The use of storage is investigated in this research. To determine the operating states and transition times of all turbines, Monte Carlo is used for system simulation in the thesis. A conventional power system from IEEE Reliability Test Systems is used with transmission line capacity, and wind and solar data are from National Climatic Data Center and National Renewal Energy Laboratory. The results show that the proposed technique is effective and efficient in practical applications for reliability analysis.

The need for optimizing wind farms’ production and maximizing the profitability of projects necessitates power performance analysis. Nowadays, the use of remote sensing devices for this purpose becomes more and more popular due to many advantages this technology has over traditional met masts. The main objective of this study is to assess the performance of a wind turbine in wake and wake-free conditions through measurements performed with a nacelle mounted lidar. The analysis is based on the data obtained during a Power Curve Measurement campaign performed on an onshore wind farm in Sweden. The power production and power curves are compared for a range of wind direction sectors in order to assess turbine performance in different wake conditions. Surprisingly low power output is observed in wake-free sectors [180°, 240°) and [240°, 300°), whereas production in wake wind directions [300°, 360°) is relatively high. The main reason for this is the wind speed distribution, however the terrain complexity and roughness should also be considered as possible factors. Generally, the wind speed distribution seems to have more influence on the results than the wake conditions. Moreover, the correlation between the met mast and lidar datasets is investigated in the study. The results indicate a good agreement between the wind speed measurement from the two devices, however, a poor correlation is found for turbulence intensity and wind shear exponent. Additionally, the influence of turbulence intensity and wind shear on the power production was analyzed. Generally, the results were in line with the reviewed literature: at low and moderate wind speeds the power production was higher for higher TI values, while the opposite was observed for higher wind speeds, where the higher TI resulted in lower production. As for the wind shear, a pattern is observed for moderate wind speeds, where the higher wind shear resulted in lower power production.

This paper sets out to analyze the observed wake effect at Block Island Wind Farm. A comparison is made between several wake simulation methods and the observed data at Block Island using analytic and CFD (Computational Fluid Dynamics) modelling methods.  The observed wake results at Block Island show a similar trend evident in earlier papers- a large power deficit found between the first two Wind Turbine Generators (WTGs) in the row followed by a slight variation in power along the row for the remainder of the WTGs. A noticeable difference is seen between the last two WTGs in the row where an increase in power is found. This increase in power is thought to be due to the alignment of the wind farm. Nevertheless, when the observed data is compared with the modeled results, the observed data seem to underestimate the wake effect due to misalignment issue with the nacelle wind direction measurement. A sensitivity analysis is conducted on the Wake Decay Constant (WDC) and Turbulence Intensity (TI) values. The results show a maximum power variation of ≈30% between a WDC value of 0.07 and 0.04 and ≈18% for TI values between 8% and 14%. The findings show that a value in the higher range of the examined WDC (0.06 and 0.07) and TI (12% and 14%) values represent a better comparison to the observed data. Nevertheless, it is not recommended to alter these parameters to fit the observed data. Furthermore, due to high uncertainty in the data measurements, and hence observed results, a clear conclusion indicating which wake model best represents the wake effect at Block Island cannot be stated.

With the rapid expansion of offshore wind power capacity in the world in the last decade, innovative offshore solutions are designed in order to meet the upcoming power capacity installations. As in all other energy sectors, offshore wind power has certain conditions that have to be met to increase the efficacy of the outcome.In this thesis, wake effect impact on the production results of Hexicon AB’s innovative floating and rotating offshore wind power platform project with 3 turbines located in the southern part of Sweden are analyzed through the application of “Analytical wake models” and the “Actuator Disc method”, with the help of WindSim.The results of Analytical models and Actuator Disc method were found to be independent of one another. Even though analytical wake models did not find any wake effect impact among the turbines, the results can be considered as logical. However, the Actuator Disc method created unexpected results which might stem from the WindSim – AD combination. It is therefore recommended to further explore these scenarios with other (more) advanced simulation tools.

The interaction between wind turbines in simple wind farm layouts is investigated with the purpose of observing the influence of wake loss phenomenon on the energy production of downwind turbines. Following an intensive exploration stage about wind farm aerodynamics and wake modeling subjects, several tests cases are designed to represent various wind farm configurations, consisting of different number of wind turbines. These cases are simulated by using DNV GL WindFarmer software which provides the opportunity of performing simulations with two different wake modeling techniques, namely Modified PARK and Eddy Viscosity. Various terrain and ambient turbulence intensity conditions are considered during the test cases. Also three different turbine types having different hub heights, rotor diameters and power-thrust coefficients are used in order to observe the effect of turbine characteristics on wake formation. Besides WindFarmer, WAsP and MATLAB tools are used in some simulation stages in order to generate input data such as wind and terrain conditions or farm layout configurations; and to process the data obtained in the end of these test cases. Simulations which are executed in the presence of a predominant wind direction from a narrow direction bin indicate that, even though there exists no significant interaction between the turbines placed in abreast configurations, successive turbine rows affect each other strongly due to the existence of the wake region of upwind turbines. It is observed that downwind spacing between turbine rows required to recover wake deficit up to a certain level changes depending on terrain and ambient turbulence intensity conditions together with turbine characteristics. For instance increasing surface roughness length (or ambient turbulence intensity) of a given site by keeping all the other parameters constant can provide up to 20% (or 30%) decrease in the required downstream distance to reduce wake loss to 5% level in a simple tandem layout consisting of two wind turbines. Further test cases are executed with various numbers of wind turbines in different configurations to observe the effect of partial, full and multiple wake regions on total farm efficiency. The results obtained from these cases are used in order to have a comparison between several farm layouts and evaluate their advantages and drawbacks.