Robust wind farm layout optimization

• Published in: Journal of physics. Conference series 2024-06, Vol.2767 (3), p.032036
• Main Authors: Sinner, Michael, Fleming, Paul
• Format: Article
• Language: English
• Subjects: Algorithms; genetic algorithm; Genetic algorithms; Initial conditions; layout optimization; Layouts; MATHEMATICS AND COMPUTING; Offshore energy sources; Optimization; Parameter sensitivity; random search; Robustness (mathematics); Search algorithms; Tuning; WIND ENERGY; Wind farms; Wind power; Wind turbines
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2767/3/032036

Wake interactions in wind farms cause losses in annual energy production (AEP) on the order of 10%. Wind farm designers optimize the layout of the farm to mitigate wake losses, especially in the dominant site-specific wind directions. As wind turbines and wind farms grow in scale, optimization becomes more complex. Offshore wind farms regularly comprise more than 100 wind turbines and are characterized by complex boundaries due to shipping lanes, neighboring wind farms, and other constraints.Layout optimization methods are broadly split between gradient-based and gradient-free approaches. Gradient-based approaches can converge quickly and perform well for smaller, academic problems but are often sensitive to initial conditions and tuning parameters and require expert knowledge to use. On the other hand, gradient-free approaches can be more robust to problem complexities. We present a robust layout optimization approach based on a random search algorithm. The algorithm is intended for those who are not optimization experts and has few tuning parameters that need specification to achieve satisfactory results. Unlike off-the-shelf methods, which use generally available, non-domain-specific optimization routines that accept as inputs an optimization function and constraint definitions, this approach takes advantage of the relative computational costs of the different evaluations by evaluating cheaper computations first (boundary and minimum distance constraints) and running expensive AEP evaluations only if all other checks pass. Moreover, an outer genetic algorithm allows multiple solutions to evolve in parallel, enabling rapid solution development on high-performance computers. We discuss the relative ease of selecting necessary tuning parameters and demonstrate the efficacy of the genetic random search on a complex layout problem consisting of placing 70 turbines in a nonconvex and unconnected boundary region.