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Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore

Author

Listed:
  • Joongjin Shin

    (School of Energy Science and Technology, Chungnam National University, Daejeon 34028, Korea)

  • Seokheum Baek

    (CAE Team, DNDE Inc., Busan 48059, Korea)

  • Youngwoo Rhee

    (School of Energy Science and Technology, Chungnam National University, Daejeon 34028, Korea)

Abstract

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.

Suggested Citation

  • Joongjin Shin & Seokheum Baek & Youngwoo Rhee, 2020. "Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore," Energies, MDPI, vol. 14(1), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:146-:d:470196
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    References listed on IDEAS

    as
    1. Bansal, Jagdish Chand & Farswan, Pushpa, 2017. "Wind farm layout using biogeography based optimization," Renewable Energy, Elsevier, vol. 107(C), pages 386-402.
    2. Kyoungboo Yang, 2020. "Determining an Appropriate Parameter of Analytical Wake Models for Energy Capture and Layout Optimization on Wind Farms," Energies, MDPI, vol. 13(3), pages 1-17, February.
    3. Archer, Cristina L. & Vasel-Be-Hagh, Ahmadreza & Yan, Chi & Wu, Sicheng & Pan, Yang & Brodie, Joseph F. & Maguire, A. Eoghan, 2018. "Review and evaluation of wake loss models for wind energy applications," Applied Energy, Elsevier, vol. 226(C), pages 1187-1207.
    4. Yang, Kyoungboo & Kwak, Gyeongil & Cho, Kyungho & Huh, Jongchul, 2019. "Wind farm layout optimization for wake effect uniformity," Energy, Elsevier, vol. 183(C), pages 983-995.
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    Cited by:

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    2. Sunghun Kim & Youngjin Park & Seungbeom Yoo & Ocktaeck Lim & Bernike Febriana Samosir, 2023. "Development of Machine Learning Algorithms for Application in Major Performance Enhancement in the Selective Catalytic Reduction (SCR) System," Sustainability, MDPI, vol. 15(9), pages 1-20, April.

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