A comparison of eight optimization methods applied to a wind farm layout optimization problem
<p>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...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-06-01
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| Series: | Wind Energy Science |
| Online Access: | https://wes.copernicus.org/articles/8/865/2023/wes-8-865-2023.pdf |
| _version_ | 1797814162885181440 |
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| author | J. J. Thomas J. J. Thomas N. F. Baker P. Malisani E. Quaeghebeur S. Sanchez Perez-Moreno J. Jasa C. Bay F. Tilli D. Bieniek N. Robinson A. P. J. Stanley W. Holt A. Ning A. Ning |
| author_facet | J. J. Thomas J. J. Thomas N. F. Baker P. Malisani E. Quaeghebeur S. Sanchez Perez-Moreno J. Jasa C. Bay F. Tilli D. Bieniek N. Robinson A. P. J. Stanley W. Holt A. Ning A. Ning |
| author_sort | J. J. Thomas |
| collection | DOAJ |
| description | <p>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 International Energy Association (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 annual energy production (AEP) was found using a new sequential allocation method, discrete 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.</p> |
| first_indexed | 2024-03-13T08:03:30Z |
| format | Article |
| id | doaj.art-b682ddd398514aa0acbaa88c1fbaa638 |
| institution | Directory Open Access Journal |
| issn | 2366-7443 2366-7451 |
| language | English |
| last_indexed | 2024-03-13T08:03:30Z |
| publishDate | 2023-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Wind Energy Science |
| spelling | doaj.art-b682ddd398514aa0acbaa88c1fbaa6382023-06-01T09:55:35ZengCopernicus PublicationsWind Energy Science2366-74432366-74512023-06-01886589110.5194/wes-8-865-2023A comparison of eight optimization methods applied to a wind farm layout optimization problemJ. J. Thomas0J. J. Thomas1N. F. Baker2P. Malisani3E. Quaeghebeur4S. Sanchez Perez-Moreno5J. Jasa6C. Bay7F. Tilli8D. Bieniek9N. Robinson10A. P. J. Stanley11W. Holt12A. Ning13A. Ning14Department of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USANational Renewable Energy Laboratory, Golden, CO 80401, USADepartment of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USAApplied Mathematics Department, IFP Energies nouvelles, 1 et 4 avenue de Bois-Préau, 92852 Rueil-Malmaison, FranceUncertainty in AI Group, Eindhoven University of Technology, 5612 AZ Eindhoven, the NetherlandsRWE Renewables GmbH, 20354 Hamburg, GermanyNational Renewable Energy Laboratory, Golden, CO 80401, USANational Renewable Energy Laboratory, Golden, CO 80401, USATU Delft, 2628 CD Delft, NetherlandsRWE Renewables GmbH, 20354 Hamburg, GermanyUL Renewables, British Columbia, Kelowna, CanadaNational Renewable Energy Laboratory, Golden, CO 80401, USADepartment of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USADepartment of Mechanical Engineering, Brigham Young University, Provo, UT 84602, USANational Renewable Energy Laboratory, Golden, CO 80401, USA<p>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 International Energy Association (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 annual energy production (AEP) was found using a new sequential allocation method, discrete 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.</p>https://wes.copernicus.org/articles/8/865/2023/wes-8-865-2023.pdf |
| spellingShingle | J. J. Thomas J. J. Thomas N. F. Baker P. Malisani E. Quaeghebeur S. Sanchez Perez-Moreno J. Jasa C. Bay F. Tilli D. Bieniek N. Robinson A. P. J. Stanley W. Holt A. Ning A. Ning A comparison of eight optimization methods applied to a wind farm layout optimization problem Wind Energy Science |
| title | A comparison of eight optimization methods applied to a wind farm layout optimization problem |
| title_full | A comparison of eight optimization methods applied to a wind farm layout optimization problem |
| title_fullStr | A comparison of eight optimization methods applied to a wind farm layout optimization problem |
| title_full_unstemmed | A comparison of eight optimization methods applied to a wind farm layout optimization problem |
| title_short | A comparison of eight optimization methods applied to a wind farm layout optimization problem |
| title_sort | comparison of eight optimization methods applied to a wind farm layout optimization problem |
| url | https://wes.copernicus.org/articles/8/865/2023/wes-8-865-2023.pdf |
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