Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore
This paper examines the solution to the problem of turbine arrangement in offshore wind farms. The two main objectives of offshore wind farm planning are to minimize wake loss and maximize annual energy production (AEP). There is more wind with less turbulence offshore compared with an onshore case,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/1/146 |
| _version_ | 1797543140115087360 |
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| author | Joongjin Shin Seokheum Baek Youngwoo Rhee |
| author_facet | Joongjin Shin Seokheum Baek Youngwoo Rhee |
| author_sort | Joongjin Shin |
| collection | DOAJ |
| description | This paper examines the solution to the problem of turbine arrangement in offshore wind farms. The two main objectives of offshore wind farm planning are to minimize wake loss and maximize annual energy production (AEP). There is more wind with less turbulence offshore compared with an onshore case, which drives the development of the offshore wind farm worldwide. South Korea’s offshore wind farms, which are deep in water and cannot be installed far off the coast, are affected by land complex terrain. Thus, domestic offshore wind farms should consider the separation distance from the coastline as a major variable depending on the topography and marine environmental characteristics. As a case study, a 60 MW offshore wind farm was optimized for the coast of the Busan Metropolitan City. For the analysis of wind conditions in the candidate site, wind conditions data from the meteorological tower and Ganjeolgot AWS at Gori offshore were used from 2001 to 2018. The optimization procedure is performed by evolutionary algorithm (EA) and particle swarm optimization (PSO) algorithm with the purpose of maximizing the AEP while minimizing the total wake loss. The optimization procedure can be applied to the optimized placement of WTs within a wind farm and can be extended for a variety of wind conditions and wind farm capacity. The results of the optimization were predicted to be 172,437 MWh/year under the Gori offshore wind potential, turbine layout optimization, and an annual utilization rate of 26.5%. This could convert 4.6% of electricity consumption in the Busan Metropolitan City region in 2019 in offshore wind farms. |
| first_indexed | 2024-03-10T13:40:28Z |
| format | Article |
| id | doaj.art-37860e4ccb5244f1a6bc486b47174ffa |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T13:40:28Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-37860e4ccb5244f1a6bc486b47174ffa2023-11-21T03:01:31ZengMDPI AGEnergies1996-10732020-12-0114114610.3390/en14010146Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea OffshoreJoongjin Shin0Seokheum Baek1Youngwoo Rhee2School of Energy Science and Technology, Chungnam National University, Daejeon 34028, KoreaCAE Team, DNDE Inc., Busan 48059, KoreaSchool of Energy Science and Technology, Chungnam National University, Daejeon 34028, KoreaThis paper examines the solution to the problem of turbine arrangement in offshore wind farms. The two main objectives of offshore wind farm planning are to minimize wake loss and maximize annual energy production (AEP). There is more wind with less turbulence offshore compared with an onshore case, which drives the development of the offshore wind farm worldwide. South Korea’s offshore wind farms, which are deep in water and cannot be installed far off the coast, are affected by land complex terrain. Thus, domestic offshore wind farms should consider the separation distance from the coastline as a major variable depending on the topography and marine environmental characteristics. As a case study, a 60 MW offshore wind farm was optimized for the coast of the Busan Metropolitan City. For the analysis of wind conditions in the candidate site, wind conditions data from the meteorological tower and Ganjeolgot AWS at Gori offshore were used from 2001 to 2018. The optimization procedure is performed by evolutionary algorithm (EA) and particle swarm optimization (PSO) algorithm with the purpose of maximizing the AEP while minimizing the total wake loss. The optimization procedure can be applied to the optimized placement of WTs within a wind farm and can be extended for a variety of wind conditions and wind farm capacity. The results of the optimization were predicted to be 172,437 MWh/year under the Gori offshore wind potential, turbine layout optimization, and an annual utilization rate of 26.5%. This could convert 4.6% of electricity consumption in the Busan Metropolitan City region in 2019 in offshore wind farms.https://www.mdpi.com/1996-1073/14/1/146offshore wind farm layout optimizationpark wake modelmetamodelevolutionary algorithmparticle swarm optimizationKorea offshore |
| spellingShingle | Joongjin Shin Seokheum Baek Youngwoo Rhee Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore Energies offshore wind farm layout optimization park wake model metamodel evolutionary algorithm particle swarm optimization Korea offshore |
| title | Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore |
| title_full | Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore |
| title_fullStr | Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore |
| title_full_unstemmed | Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore |
| title_short | Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore |
| title_sort | wind farm layout optimization using a metamodel and ea pso algorithm in korea offshore |
| topic | offshore wind farm layout optimization park wake model metamodel evolutionary algorithm particle swarm optimization Korea offshore |
| url | https://www.mdpi.com/1996-1073/14/1/146 |
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