[pt] À medida que o mundo enfrenta a urgente questão das mudanças climáticas, a energia eólica se destaca como uma fonte crítica de energia limpa. No entanto, realizar seu pleno potencial depende da otimização dos layouts de parques eólicos, especialmente à luz do complexo efeito de esteira. Esta dissertação adentra na Otimização de Layout de Parques Eólicos (WFLO, na sigla em inglês) usando o Modelo de Efeito de Esteira de Bastankhah. O escopo deste estudo vai além do design de layout; abrange a intrincada tarefa de mitigar o impacto do efeito de esteira, juntamente com a busca por uma estratégia de negociação com aversão ao risco e maximização de valor. Para contabilizar a aversão ao risco, uma combinação entre o Valor Esperado e os funcionais de medida de risco baseados no quantil esquerdo, a medida de Valor em Risco Condicional (CVaR). Para apoiar esta pesquisa, um pacote de código aberto OptimalLayout.jl foi desenvolvido. Este pacote co-otimiza o posicionamento das turbinas eólicas para mitigar o impacto do efeito de esteira e a estratégia de contratação de um agente/gerador avesso ao risco. Através de uma série de estudos de casos práticos em diversos ambientes dinâmicos, esta pesquisa ilustra a aplicabilidade do WFLO no mundo real. Estas investigações examinam detalhadamente a sua influência na produção de energia e na dinâmica das receitas, oferecendo informações valiosas sobre soluções energéticas sustentáveis. / [en] As the world confronts the pressing issue of climate change, wind power stands out as a critical source of clean energy. However, realizing its full potential relies on the optimization of wind farm layouts, particularly in light of the complex wake effect. This dissertation delves into Wind Farm Layout Optimization (WFLO) using the Bastankhah Wake Model. The scope of this study goes beyond layout design; it encompasses the intricate task of mitigating the wake effect s impact along with the seek for a risk-averse-value maximizing trading strategy. To account for risk-averseness, a combination between Expected Value and the left-side-quantile-based risk-measure functionals, the Conditional Value-at-Risk (CVaR) measure. To support this research, an opensource package OptimalLayout.jl was developed. This package co-optimizes the positioning of wind turbines to mitigate wake effect impact,and the contracting strategy of a Risk-Averse agent/generator. Through a series of practical case studies across diverse dynamic environments, this research illustrates the real-world applicability of WFLO. These investigations intricately examine its influence on power production and revenue dynamics, offering valuable insights into sustainable energy solutions.

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.