Wind farm layout optimization can greatly improve wind farm performance. However, past wind farm design has been limited in several ways. Wind farm design usually assumes that all the turbines throughout the farm should be exactly the same. Oftentimes, the location of every turbine is optimized individually, which is computationally expensive. Furthermore, designers fail to consider turbine loads during layout optimization. This dissertation presents four studies which provide partial solutions to these limitations and greatly improve wind farm layout optimization. Two studies explore differing turbine designs in wind farms. In these studies, Wind farm layouts are optimized simultaneously with turbine design. We found that for small rotor diameters and closely spaced wind turbines, wind farms with different heights have a 5–10% reduction in cost of energy compared to farms with all the same turbine height. Coupled optimization of turbine layout and full turbine design results in an 2–5% reduction in cost of energy compared to optimizing sequentially for wind farms with turbine spacings of 8.5–11 rotor diameters. Wind farms with tighter spacing benefit even more from coupled optimization. Furthermore, we found that heterogeneous turbine design can produce up to an additional 10% cost of energy reduction compared to wind farms with identical turbines throughout the farm, especially when the wind turbines are closely spaced. The third study presents the boundary-grid parameterization method to reduce the computational expense of optimizing wind farms. This parameterization uses only five variables to define the layout of a wind farm with any number of turbines. For a 100 turbine wind farm, we show that optimizing the five variables of the boundary-grid method produces wind farms that perform just as well as farms where the location of each turbine is optimized individually, which requires 200 design variables. The presented method facilitates the study for both gradient-free and gradient-based optimization of large wind farms. The final study presents a model to calculate fatigue damage caused by partial waking on a wind turbine which is computationally efficient and can be included in wind farm layout optimization. Compared to high fidelity simulation data, the model accurately predicts the damage trends of various waking conditions. We also perform a wind farm layout optimization with the presented model in which we maximize the annual energy production of a wind farm while constraining the damage of each turbine. The results of the optimization show that the turbine damage can be constrained with only a very small sacrifice of less than 1% to the annual energy production.

This thesis focuses on providing the means to use vertical-axis wind turbines (VAWTs) in wind farms as an alternative form of harnessing wind energy in offshore and urban environments where both wake and acoustic effects of turbines are important considerations. In order for VAWTs to be used in wind farm layout analysis and optimization, a reduced-order wake model is needed to calculate velocities around a turbine quickly and accurately. However, a VAWT wake model has not been available to accomplish this task. Using vorticity data from computational fluid dynamic (CFD) simulations of VAWTs and cross-validated Gaussian distribution and polynomial surface fitting, a wake model is produced that can estimate a wake velocity deficit of an isolated VAWT at any downstream and lateral position based on nondimensional parameters describing the turbine speed and geometry. When compared to CFD, which takes over a day to run one simulation, the wake model predicts the velocity deficit at any location with a normalized root mean squared error of 0.059 in about 0.02 seconds. The model agrees with two experimental VAWT wake studies with a percent difference of the maximum wake deficit of 6.3% and 14.6%. Using the actuator cylinder model with predicted wake velocities of multiple turbines, aerodynamic loads can be calculated on the turbine blades to estimate the power production of a VAWT wind farm. As VAWTs could be used in urban environments near residential areas, the noise disturbance coming from the turbine blades is an important consideration in the layout of a wind farm. Noise restrictions may be imposed on a wind farm to limit the disturbance, often impacting the wind farm's power producing capability. Two specific horizontal-axis wind turbine farm designs are studied and optimized using the FLORIS wake model and an acoustic model based on semi-empirical turbine noise calculations to demonstrate the impact a noise level constraint has on maximizing wind farm power production. When a noise level constraint was not active, the average power production increased, up to 8.01% in one wind farm and 3.63% in the other. Including a noise restriction in the optimization had about a 5% impact on the optimal average power production over a 5 decibel range. By analyzing power and noise together, the multi-modality of the optimization problem can be used to find solutions were noise impact can be improved while still maximizing wind farm power production.

Wind farm layout optimization is a multidisciplinary undertaking, requiring students and researchers to integrate many skillsets in order to optimize turbine placement. There is currently a lack of useful benchmarking exercises for participants in the field to compare the efficacy of their methods. This work details the construction and completion of a set of four case studies meant to satisfy this need, with the hope of providing some insight into useful layout optimization approaches. These case studies are intended to also serve as instructive introductory exercises with which newcomers researching wind energy may incrementally practice and increase their abilities.The first two case studies were released globally and attracted participants from around the world who attempted the optimization problems. A detailed analysis of their results is presented herein.The second two case studies are currently being worked on by researchers in the field, with initial feed back regarding the formulations also included.

Wind farm layout optimization is a multidisciplinary undertaking, requiring students and researchers to integrate many skillsets in order to optimize turbine placement. There is currently a lack of useful benchmarking exercises for participants in the field to compare the efficacy of their methods. This work details the construction and completion of a set of four case studies meant to satisfy this need, with the hope of providing some insight into useful layout optimization approaches. These case studies are intended to also serve as instructive introductory exercises with which newcomers researching wind energy may incrementally practice and increase their abilities.The first two case studies were released globally and attracted participants from around the world who attempted the optimization problems. A detailed analysis of their results is presented herein.The second two case studies are currently being worked on by researchers in the field, with initial feed back regarding the formulations also included.