Wind farm layout optimization using genetic algorithm and its application to Daegwallyeong wind farm
This paper proposes a new wind farm layout optimization methodology based on a genetic algorithm by implementing a simulation model considering wake effect. This method consists of (1) batch optimization to efficiently obtain a rough wind farm layout for the maximum energy production in a large scal...
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| Vydáno v: | JMST Advances Ročník 1; číslo 4; s. 249 - 257 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Seoul
Korean Society of Mechanical Engineers
01.12.2019
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| ISSN: | 2524-7905, 2524-7913 |
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| Abstract | This paper proposes a new wind farm layout optimization methodology based on a genetic algorithm by implementing a simulation model considering wake effect. This method consists of (1) batch optimization to efficiently obtain a rough wind farm layout for the maximum energy production in a large scale, and (2) post-optimization to obtain a refined layout to further improve the energy production in a small scale. The proposed two-step optimization enables to efficiently optimize wind farm layout and thus can be applicable to layout optimization of large-scale wind farms. A case study with the actual Daegwallyeong wind farm shows that wake loss is improved by 2.3% point after the proposed layout optimization which means about 2.5% more energy production compared with the existing layout. |
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| AbstractList | This paper proposes a new wind farm layout optimization methodology based on a genetic algorithm by implementing a simulation model considering wake effect. This method consists of (1) batch optimization to efficiently obtain a rough wind farm layout for the maximum energy production in a large scale, and (2) post-optimization to obtain a refined layout to further improve the energy production in a small scale. The proposed two-step optimization enables to efficiently optimize wind farm layout and thus can be applicable to layout optimization of large-scale wind farms. A case study with the actual Daegwallyeong wind farm shows that wake loss is improved by 2.3% point after the proposed layout optimization which means about 2.5% more energy production compared with the existing layout. |
| Author | Lee, Yoon Seung Park, Jeong Woo An, Bo Sung Lee, Ikjin Jung, Hyunsuk |
| Author_xml | – sequence: 1 givenname: Jeong Woo surname: Park fullname: Park, Jeong Woo organization: Korea Advanced Institute of Science and Technology – sequence: 2 givenname: Bo Sung surname: An fullname: An, Bo Sung organization: Daejeon Science High School – sequence: 3 givenname: Yoon Seung surname: Lee fullname: Lee, Yoon Seung organization: Daejeon Science High School – sequence: 4 givenname: Hyunsuk surname: Jung fullname: Jung, Hyunsuk organization: Daejeon Science High School – sequence: 5 givenname: Ikjin orcidid: 0000-0002-3470-7341 surname: Lee fullname: Lee, Ikjin email: ikjin.lee@kaist.ac.kr organization: Korea Advanced Institute of Science and Technology |
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| CitedBy_id | crossref_primary_10_1016_j_heliyon_2024_e40135 crossref_primary_10_3390_su142416960 crossref_primary_10_1016_j_energy_2024_133106 crossref_primary_10_3390_jmse12030424 crossref_primary_10_1016_j_energy_2025_137885 crossref_primary_10_1016_j_suscom_2021_100606 crossref_primary_10_1016_j_rser_2020_110047 crossref_primary_10_1155_2024_9406519 crossref_primary_10_1016_j_oceaneng_2022_112807 crossref_primary_10_1016_j_energy_2020_119244 crossref_primary_10_1002_ese3_987 crossref_primary_10_1016_j_enconman_2025_120528 crossref_primary_10_1007_s40313_020_00600_0 |
| Cites_doi | 10.1016/j.solener.2019.09.027 10.1115/1.4027512 10.1016/j.enconman.2013.10.003 10.1007/BF02990229 10.1016/j.renene.2004.05.007 10.1016/j.solener.2017.05.093 10.1016/j.neucom.2006.05.017 10.1016/0167-6105(94)90080-9 10.1115/1.4006997 10.1016/j.rser.2015.12.229 10.1071/PH560511 10.3795/KSME-B.2010.34.10.901 10.7836/kses.2011.31.2.072 10.1109/ISAP.2007.4441654 |
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| Keywords | Wind farms Jensen’s model Daegwallyeong wind farm Genetic algorithm Optimization |
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| Title | Wind farm layout optimization using genetic algorithm and its application to Daegwallyeong wind farm |
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