Wind farm layout optimization (WFLO) is the design of wind turbine layout, subject to various financial and engineering objectives and constraints. The first topic of this thesis focuses on solving two variations of WFLO that have different analytical aerodynamic models, and illustrate deep integration of the wake models into mixed-integer programs and constraint programs. Formulating WFLO as MIP and CP enables more quantitative analysis than previous studies could do with heuristics, and allows the practitioners to use an off-the-shelf optimization solver to tackle the WFLO problem. The second topic focuses on another version of WFLO that has two competing objectives: minimization of noise and maximization of energy. A genetic algorithm (NSGA-II) is used. Under these two objectives, solutions are presented to illustrate the flexibility of this optimization framework in terms of supplying a spectrum of design choices with different numbers of turbines and different levels of noise and energy output.

Wind farm layout optimization (WFLO) is the design of wind turbine layout, subject to various financial and engineering objectives and constraints. The first topic of this thesis focuses on solving two variations of WFLO that have different analytical aerodynamic models, and illustrate deep integration of the wake models into mixed-integer programs and constraint programs. Formulating WFLO as MIP and CP enables more quantitative analysis than previous studies could do with heuristics, and allows the practitioners to use an off-the-shelf optimization solver to tackle the WFLO problem. The second topic focuses on another version of WFLO that has two competing objectives: minimization of noise and maximization of energy. A genetic algorithm (NSGA-II) is used. Under these two objectives, solutions are presented to illustrate the flexibility of this optimization framework in terms of supplying a spectrum of design choices with different numbers of turbines and different levels of noise and energy output.

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Layout optimization of offshore wind farms seeks to automate the design of the wind farm and the placement of wind turbines such that the proposed wind farm maximizes its potential. The optimization of an offshore wind farm layout therefore seeks to minimize the costs of the wind farm while maximizing the energy extraction while considering the effects of wakes on the resource; the electrical infrastructure required to collect the energy generated; the cost variation across the site; and all technical and consenting constraints that the wind farm developer must adhere to. As wakes, electrical losses, and costs are non-linear, this produces a complex optimization problem. This thesis describes the design, development, validation, and initial application of a new framework for the optimization of offshore wind farm layouts using either a genetic algorithm or a particle swarm optimizer. The developed methodology and analysis tool have been developed such that individual components can either be used to analyze a particular wind farm layout or used in conjunction with the optimization algorithms to design and optimize wind farm layouts. To accomplish this, separate modules have been developed and validated for the design and optimization of the necessary electrical infrastructure, the assessment of the energy production considering energy losses, and the estimation of the project costs. By including site-dependent parameters and project specific constraints, the framework is capable of exploring the influence the wind farm layout has on the levelized cost of energy of the project. Deploying the integrated framework using two common engineering metaheuristic algorithms to hypothetical, existing, and future wind farms highlights the advantages of this holistic layout optimization framework over the industry standard approaches commonly deployed in offshore wind farm design leading to a reduction in LCOE. Application of the tool to a UK Round 3 site recently under development has also highlighted how the use of this tool can aid in the development of future regulations by considering various constraints on the placement of wind turbines within the site and exploring how these impact the levelized cost of energy.

This thesis challenges the argument that farms must grow larger and more specialized in order to survive. The root of this thesis stems from my disdain towards current mainstream industrial methods of farming, attempting to compete in the world market. In theory, current government farm policies are meant to assist and protect farmers on the global market but typically result in protecting the large companies responsible for manifesting the problems in the first place. These key factors have changed the face of the agricultural landscape of North America and have lead to a lost connection between society and their food. These factors have lead me to build upon more sustainable and value-added farm philosophies. Such practices highlight the benefits of small farm enterprises for the farmer, the animals, the environment and society as a whole. The design is in combination a response to the landscape and the local economic niche it functions within. Through physical connection, the architecture highlights the cycles of individual farm elements working together to strengthen the whole farm as system.

As wind energy technology continues to mature, farm sizes grow and wind farm layout design becomes more difficult, in part due to the number of design variables and constraints. Wind farm layout optimization is typically approached using gradient-free methods because of the highly multi-modal shape of the wind farm layout design space. Gradient-free method performance generally degrades with increasing problem size, making it difficult to find optimal layouts for larger wind farms. However, gradient-based optimization methods can effectively and efficiently solve large-scale problems with many variables and constraints. To pave the way for effective and efficient wind farm layout optimization for large-scale wind farms, we have worked to overcome the primary barriers to applying gradient-based optimization to wind farm layout optimization. To improve model/algorithm compatibility, we adjusted wake and wind farm models, adding more realistic curvature and smoothness to enable optimization algorithms to travel through areas in the design space where they had previously gotten stuck. We reduced the number of function calls required for gradient-based wind farm layout optimization by over three orders of magnitude for large farms by using algorithmic differentiation to compute derivatives. We reduced the multi-modality of the wind farm layout design space using wake expansion continuation (WEC). We developed WEC to work with existing optimization algorithms, enabling them to get out of local optima while remaining fully gradient-based. Across four case studies, WEC found results with lower wake loss, on average, than the other methods we tested. To resolve concerns about optimization algorithms exploiting model inaccuracies, we compared the initial and optimized layouts to large-eddy simulation (LES) results. The simple models predicted an AEP improvement of 7.7% for a low-TI case, and LES predicted 9.3%. For a high-TI case, the simple models predicted a 10.0% improvement in AEP and LES predicted 10.7%. To resolve uncertainty regarding relative solution quality for gradient-based and gradient-free methods, we collaborated with seven organizations to compare eight optimization methods. Each method was managed by researchers experienced with them. All methods found solutions of similar quality, with optimized wake loss between 15.48 % and 15.70 %. WEC with SNOPT was the only purely gradient-based method included and found the third-to-best solution.