A low-complexity evolutionary algorithm for wind farm layout optimization
The wind farm layout determines the power generation ability, and its optimization remains challenging in terms of algorithm complexity and performance. Several greedy-derived and evolutionary-derived algorithms have been proposed to solve or alleviate the layout planning problems. However, greedy a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723007229 |
| _version_ | 1797781853843750912 |
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| author | Xingwang Huang Zhijin Wang Chaopeng Li Min Zhang |
| author_facet | Xingwang Huang Zhijin Wang Chaopeng Li Min Zhang |
| author_sort | Xingwang Huang |
| collection | DOAJ |
| description | The wind farm layout determines the power generation ability, and its optimization remains challenging in terms of algorithm complexity and performance. Several greedy-derived and evolutionary-derived algorithms have been proposed to solve or alleviate the layout planning problems. However, greedy algorithms are efficient in finding the optimal layout but their exploration ability is poor. In contrast, evolutionary algorithms own better global search ability but require huge computational effort to find the optimal layout. Considering that all wind turbines are deployed in the same flat area (i.e. the coordinates of each wind turbine are of dimension 2, and each dimension of all turbines has the same search range), a low-complexity grey wolf optimization technique using a 2-D encoding mechanism (GWOEM) is proposed in this work. which is low-complexity and accelerates search efficiency. The proposed GWOEM effectively combines the advantages of the greedy algorithms and the evolutionary algorithms, and improves the search efficiency while maintaining accuracy. To validate the performance of the proposed GWOEM, a comparative analysis is performed based on two wind scenarios. Simulation results show that the proposed GWOEM is competitive with other state-of-the-art techniques. Compared with TDA, ADE, ADE-GRNN, and DEEM, the average power output in the two wind scenarios considered herein increases by 44.41%, 34.45%, 34.97%, and 37.06%, respectively. In terms of execution time, GWOEM is shorter than TDA, ADE, and DEEM except for ADEGRNN which increases by 12.18%. |
| first_indexed | 2024-03-13T00:02:48Z |
| format | Article |
| id | doaj.art-5e1b8ab5018a4f219fcbb65268b5b263 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-13T00:02:48Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-5e1b8ab5018a4f219fcbb65268b5b2632023-07-13T05:30:07ZengElsevierEnergy Reports2352-48472023-12-01957525761A low-complexity evolutionary algorithm for wind farm layout optimizationXingwang Huang0Zhijin Wang1Chaopeng Li2Min Zhang3Computer Engineering College, Jimei University, Xiamen, 361021, Fujian, ChinaComputer Engineering College, Jimei University, Xiamen, 361021, Fujian, ChinaSchool of Ocean Information Engineering, Jimei University, Xiamen, 361021, Fujian, ChinaComputer Engineering College, Jimei University, Xiamen, 361021, Fujian, China; Corresponding author.The wind farm layout determines the power generation ability, and its optimization remains challenging in terms of algorithm complexity and performance. Several greedy-derived and evolutionary-derived algorithms have been proposed to solve or alleviate the layout planning problems. However, greedy algorithms are efficient in finding the optimal layout but their exploration ability is poor. In contrast, evolutionary algorithms own better global search ability but require huge computational effort to find the optimal layout. Considering that all wind turbines are deployed in the same flat area (i.e. the coordinates of each wind turbine are of dimension 2, and each dimension of all turbines has the same search range), a low-complexity grey wolf optimization technique using a 2-D encoding mechanism (GWOEM) is proposed in this work. which is low-complexity and accelerates search efficiency. The proposed GWOEM effectively combines the advantages of the greedy algorithms and the evolutionary algorithms, and improves the search efficiency while maintaining accuracy. To validate the performance of the proposed GWOEM, a comparative analysis is performed based on two wind scenarios. Simulation results show that the proposed GWOEM is competitive with other state-of-the-art techniques. Compared with TDA, ADE, ADE-GRNN, and DEEM, the average power output in the two wind scenarios considered herein increases by 44.41%, 34.45%, 34.97%, and 37.06%, respectively. In terms of execution time, GWOEM is shorter than TDA, ADE, and DEEM except for ADEGRNN which increases by 12.18%.http://www.sciencedirect.com/science/article/pii/S2352484723007229Grey wolf optimization (GWO)Wind farm layoutWake effectLow complexity |
| spellingShingle | Xingwang Huang Zhijin Wang Chaopeng Li Min Zhang A low-complexity evolutionary algorithm for wind farm layout optimization Energy Reports Grey wolf optimization (GWO) Wind farm layout Wake effect Low complexity |
| title | A low-complexity evolutionary algorithm for wind farm layout optimization |
| title_full | A low-complexity evolutionary algorithm for wind farm layout optimization |
| title_fullStr | A low-complexity evolutionary algorithm for wind farm layout optimization |
| title_full_unstemmed | A low-complexity evolutionary algorithm for wind farm layout optimization |
| title_short | A low-complexity evolutionary algorithm for wind farm layout optimization |
| title_sort | low complexity evolutionary algorithm for wind farm layout optimization |
| topic | Grey wolf optimization (GWO) Wind farm layout Wake effect Low complexity |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723007229 |
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