16 Jun 2023
 | 16 Jun 2023
Status: this preprint is currently under review for the journal WES.

Speeding up large wind farms layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout

Rafael Valotta Rodrigues, Mads Mølgaard Pedersen, Jens Peter Schøler, Julian Quick, and Pierre Réthoré

Abstract. As the use of wind energy expands worldwide, the wind energy industry is considering building larger clusters of turbines. Existing computational methods to design and optimize the layout of wind farms are well suited for medium-sized plants; however, these approaches need to be improved to ensure efficient scaling to large wind farms. This work investigates strategies for covering this gap, focusing on Gradient-Based (GB) approaches. We investigated the main bottlenecks of the problem, including the computational time per iteration, multi-start for GB optimization, and the number of iterations to achieve convergence. The open-source tools PyWake and TOPFARM were used to carry out the numerical experiments. The results show Algorithmic Differentiation (AD) as an effective strategy for reducing the time per iteration. The speedup reached by AD scales linearly with the number of wind turbines, reaching 75 times for a wind farm with 500 wind turbines. However, memory requirements may make AD unfeasible in personal computers or for larger farms. Moreover, flow case parallelization was found to reduce the time per iteration, but the speedup remains roughly constant with the number of wind turbines. Therefore, top-level parallelization of each multi-start was found to be a more efficient approach for GB optimization. The handling of spacing constraints was found to dominate the iteration time for large wind farms. In this study, we ran the optimizations without spacing constraints and observed that all wind turbines were separated by at least 1.4D. The number of iterations until convergence was found to scale linearly with the number of wind turbines by a factor of 2.3, but further investigation is necessary for generalizations. Furthermore, we have found that initializing the layouts using a heuristic approach called Smart-Start (SMAST) significantly reduced the number of multi-starts during GB optimization. Running only one optimization for a wind farm with 279 turbines initialized with SMAST resulted in a higher final AEP than 5,000 optimizations initialized with random layouts. Finally, estimates for the total time reduction were made assuming the trends found in this work for the time per iteration, number of iterations, and number of multi-starts holds for larger wind farms. One optimization of a wind farm with 500 wind turbines combining SMAST, AD, flow case parallelization, and without spacing constraints takes 15.6 h, whereas 5,000 optimizations with random initial layouts, Finite-Differences, spacing constraints, and top-level parallelization are expected to take around 300 years.

Rafael Valotta Rodrigues et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2023-61', Pietro Bortolotti, 04 Aug 2023
  • RC2: 'Comment on wes-2023-61', Anonymous Referee #2, 08 Aug 2023

Rafael Valotta Rodrigues et al.

Rafael Valotta Rodrigues et al.


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Short summary
The use of wind energy has been growing over the last decades, and further increase is predicted. As the wind energy industry is starting to consider larger wind farms, the existing numerical methods to analyze for small and medium wind farms need to be improved. In this article, we have explored different strategies to tackle the problem on a feasible and timely way. The final product is a set of recommendations when carrying out trade-off analysis on large wind farms.