Preprints
https://doi.org/10.5194/wes-2022-90
https://doi.org/10.5194/wes-2022-90
 
09 Nov 2022
09 Nov 2022
Status: this preprint is currently under review for the journal WES.

A Comparison of Eight Optimization Methods Applied to a Wind Farm Layout Optimization Problem

Jared J. Thomas1,5, Nicholas F. Baker1, Paul Malisani2, Erik Quaeghebeur3, Sebastian Sanchez Perez-Moreno4, John Jasa5, Christopher Bay5, Federico Tilli6, David Bieniek4, Nick Robinson7, Andrew P. J. Stanley5, Wesley Holt1, and Andrew Ning1,5 Jared J. Thomas et al.
  • 1Brigham Young University, Department of Mechanical Engineering, Provo, UT 84602, USA
  • 2IFP Energies nouvelles, Applied Mathematics Department, 1 et 4 avenue de Bois-Préau, 92852 Rueil-Malmaison, France
  • 3Eindhoven University of Technology, Uncertainty in AI Group, 5612 AZ Eindhoven, The Netherlands
  • 4RWE Renewables GmbH, 20354 Hamburg, Germany
  • 5National Renewable Energy Laboratory, Golden, CO, 80401, USA
  • 6TU Delft, 2628 CD Delft, Netherlands
  • 7UL Renewables, Kelowna, BC, Canada

Abstract. Selecting a wind farm layout optimization method is difficult. Comparisons between optimization methods in different papers can be uncertain due to the difficulty of exactly reproducing the objective function. Comparisons by just a few authors in one paper can be uncertain if the authors do not have experience using each algorithm. In this work we provide an algorithm comparison for a wind farm layout optimization case study between eight optimization methods applied, or directed, by researchers who developed those algorithms or who had other experience using them. We provided the objective function to each researcher to avoid ambiguity about relative performance due to a difference in objective function. While these comparisons are not perfect, we try to treat each algorithm more fairly by having researchers with experience using each algorithm apply each algorithm and by having a common objective function provided for analysis. The case study is from the IEA Wind Task 37, based on the Borssele III and IV wind farms with 81 turbines. Of particular interest in this case study is the presence of disconnected boundary regions and concave boundary features. The optimization methods studied represent a wide range of approaches, including gradient-free, gradient-based, and hybrid methods; discrete and continuous problem formulations; single-run and multi-start approaches; and mathematical and heuristic algorithms. We provide descriptions and references (where applicable) for each optimization method as well as lists of pros and cons to help readers determine an appropriate method for their use case. All the optimization methods perform similarly, with optimized wake loss values between 15.48 % and 15.70 % as compared to 17.28 % for the unoptimized provided layout. Each of the layouts found were different, but all layouts exhibited similar characteristics. Strong similarities across all the layouts include tightly packing wind turbines along the outer borders, loosely spacing turbines in the internal regions, and allocating similar numbers of turbines to each discrete boundary region. The best layout by AEP was found using a new sequential allocation method, discreet exploration-based optimization (DEBO). Based on the results in this study, it appears that using an optimization algorithm can significantly improve wind farm performance, but there are many optimization methods that can perform well on the wind farm layout optimization problem given that they are applied correctly.

Jared J. Thomas et al.

Status: open (until 13 Jan 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Jared J. Thomas et al.

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Data and code repository Jared J. Thomas, Nicholas F. Baker, Paul Malisani, Erik Quaeghebeur, Sebastian Sanchez Perez-Moreno, John Jasa, Christopher Bay, Federico Tilli, David Bieniek, Nick Robinson, Andrew P. J. Stanley, Wesley Holt, and Andrew Ning https://zenodo.org/badge/latestdoi/543173996

Jared J. Thomas et al.

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Short summary
This work compares 8 optimization algorithms (including gradient-based, gradient-free, and hybrid) on a wind farm optimization problem with 4 discrete regions, concave boundaries, and 81 wind turbines. Algorithms were each run by researchers experienced with that algorithm. All optimized layouts were unique but with similar annual energy production. Common characteristics included tightly-spaced turbines on the outer perimeter and turbines loosely-spaced, and roughly on a grid, on the interior.