CyberSEES: Type 1: Collaborative Research: Large-Scale, Integrated, and Robust Wind Farm Optimization Enabled by Coupled Analytic Gradients

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Wind provides a renewable source of energy and is one of the most cost-effective sources for new energy installations. Today, wind turbines are designed for an isolated environment, as are their power regulation strategies. When turbines are assembled into a wind farm their wakes significantly interfere with other turbines resulting in energy underproduction of 10-20% relative to expectations. This underproduction is a major barrier to increased wind energy growth. This project hypothesizes that a significant increase in power production is possible through simultaneous design of wind turbine layouts, power-regulation strategies, and the turbines themselves, all in the presence of stochastic inputs.Simultaneous layout-control-turbine design is challenging, especially when considering uncertain inputs. Current research and industry practices use simulation models that are non-differentiable or do not provide gradients. As a result, most wind farm layout optimizations are limited to around 10-100 variables, rely on sequential design processes, and only include uncertainty in simple ways if at all. To enable design problems of larger size and complexity, wake and turbine models must be reimplemented with scalable optimization in mind, and new methods for uncertainty quantification must be developed. The investigators' recent work suggests that by developing wind turbine wake models that provide exact derivatives, wind farm layout can be done effectively with 100 to 1,000 times more variables than those solved by the industry today. This scalability will enable wind farm optimization that includes a large number of design variables, integrates multiple disciplines, and incorporates uncertainty in the design process. These proposed contributions seek to advance energy sustainability, scientific computing, and education. The wake and turbine models will be large-scale-optimization ready to allow designers to solve problems that were previously out of reach. The new uncertainty quantification methodologies will be widely applicable to multiple disciplines, particularly as more industries move towards integrated system design. Finally, a dedicated website will serve as a teaching tool to introduce optimization and uncertainty quantification concepts to a general audience through interactive wind farm design problems.Concurrently, the investigators will focus on foundational methods for scalable uncertainty quantification that can be used for both forward uncertainty propagation and statistical inversion. The emphasis on scalability is required to address challenges related to the number of random input variables, the number of output quantities of interest, and the efficiency of parallel implementations on extreme-scale computers. As an example, the research team is developing new methodologies for scalable uncertainty quantification that take advantage of the exact derivatives provided by these turbine and wake models. The research plan focuses on three main goals: 1) develop new wake models with exact gradients, 2) perform integrated layout-control-turbine optimization, and 3) develop scalable uncertainty quantification methods to demonstrate expected performance improvements on robust wind farm layout problems.