Location

Virtual

Start Date

5-10-2021 11:00 AM

End Date

5-10-2021 11:10 AM

Description

In this paper we present a continuation optimization method for reducing multi-modality in the wind farm layout optimization problem that we call Wake Expansion Continuation (WEC). We achieve the reduction in multi-modality by starting with an increased wake diameter while maintaining normal velocity deficits at the center of the wakes, and then reducing the wake diameter for each of a series of optimization runs until the accurate wake diameter is used. We applied and demonstrated the effectiveness of WEC with two different wake models. We tested WEC on four optimization case studies with a gradient-based optimization method and a gradient-free optimization method. We found a significant improvement in the mean, standard deviation, and minimum wake loss for optimization with WEC compared to optimization without WEC for all test cases. We found the gradient-free optimization algorithm resulted in less optimal layouts on average for all cases than the gradient-based algorithm with WEC. We also applied WEC to the gradient-free algorithm for one case study with significantly improved results, but there was more improvement when we applied WEC to a gradient-based algorithm. WEC enables gradient-based algorithms to search the wind farm layout optimization space more globally, and provides more optimal results more consistently than optimization without WEC.

Available for download on Tuesday, May 10, 2022

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May 10th, 11:00 AM May 10th, 11:10 AM

Wake Expansion Continuation: Multi-Modality Reduction in the Wind Farm Layout Optimization Problem

Virtual

In this paper we present a continuation optimization method for reducing multi-modality in the wind farm layout optimization problem that we call Wake Expansion Continuation (WEC). We achieve the reduction in multi-modality by starting with an increased wake diameter while maintaining normal velocity deficits at the center of the wakes, and then reducing the wake diameter for each of a series of optimization runs until the accurate wake diameter is used. We applied and demonstrated the effectiveness of WEC with two different wake models. We tested WEC on four optimization case studies with a gradient-based optimization method and a gradient-free optimization method. We found a significant improvement in the mean, standard deviation, and minimum wake loss for optimization with WEC compared to optimization without WEC for all test cases. We found the gradient-free optimization algorithm resulted in less optimal layouts on average for all cases than the gradient-based algorithm with WEC. We also applied WEC to the gradient-free algorithm for one case study with significantly improved results, but there was more improvement when we applied WEC to a gradient-based algorithm. WEC enables gradient-based algorithms to search the wind farm layout optimization space more globally, and provides more optimal results more consistently than optimization without WEC.