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Investigation into the optimal wind turbine layout patterns for a Hong Kong offshore wind farm

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  • Gao, Xiaoxia
  • Yang, Hongxing
  • Lu, Lin

Abstract

Optimal WT (wind turbine) layout patterns relate in detail to the specific conditions of OWF (offshore wind farm) environments and therefore each is different. This paper presents an investigation into optimal WT layout patterns for three OWF configurations (aligned, staggered, scattered) in HK (Hong Kong) waters. A hypothetical OWF (6930 m × 9072 m) are analysed based on twenty years of wind data (1992–2011). For the aligned and staggered WFs, different WT layout separations are studied. The separations varied between 5.0D and 15.0D along the PWD (prevailing wind direction) and 5.0D to 12.0D in the CWD (crosswind direction), where D is the WT rotor diameter. A range of 25 and 45 WTs are placed in the scattered WF, with their layout optimized using the Multi-Population Genetic Algorithm. WF performance is reported for the best ten layout patterns following studies of many different layouts. Results show for this hypothetical OWF, that the optimal WT separation is 14.5D in the PWD and 11.0D in the CWD for the aligned and staggered cases. Thirty WTs are recommended as the optimum number for the scattered WF. The LCOE (levelized costs of energy) were calculated in HK$ terms 1.474/kWh (aligned), 1.467/kWh (staggered), and 1.290/kWh (scattered). APG (annual energy generation) is determined to be 40.80 × 108 kWh (aligned), 40.42 × 108 kWh (staggered), and 33.98 × 108 kWh (scattered), representing 9.48% (aligned), 9.39% (staggered), and 7.89% (scattered) of the annual electricity consumption for HK in 2012. The approach presented can be regarded as a generic method for WT layout optimization.

Suggested Citation

  • Gao, Xiaoxia & Yang, Hongxing & Lu, Lin, 2014. "Investigation into the optimal wind turbine layout patterns for a Hong Kong offshore wind farm," Energy, Elsevier, vol. 73(C), pages 430-442.
    • Handle: RePEc:eee:energy:v:73:y:2014:i:c:p:430-442
    DOI: 10.1016/j.energy.2014.06.033
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    1. Emami, Alireza & Noghreh, Pirooz, 2010. "New approach on optimization in placement of wind turbines within wind farm by genetic algorithms," Renewable Energy, Elsevier, vol. 35(7), pages 1559-1564.
    2. Pookpunt, Sittichoke & Ongsakul, Weerakorn, 2013. "Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-varying acceleration coefficients," Renewable Energy, Elsevier, vol. 55(C), pages 266-276.
    3. Marmidis, Grigorios & Lazarou, Stavros & Pyrgioti, Eleftheria, 2008. "Optimal placement of wind turbines in a wind park using Monte Carlo simulation," Renewable Energy, Elsevier, vol. 33(7), pages 1455-1460.
    4. Lu, Lin & Yang, Hongxing & Burnett, John, 2002. "Investigation on wind power potential on Hong Kong islands—an analysis of wind power and wind turbine characteristics," Renewable Energy, Elsevier, vol. 27(1), pages 1-12.
    5. Snyder, Brian & Kaiser, Mark J., 2009. "Ecological and economic cost-benefit analysis of offshore wind energy," Renewable Energy, Elsevier, vol. 34(6), pages 1567-1578.
    6. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
    7. González, Javier Serrano & Gonzalez Rodriguez, Angel G. & Mora, José Castro & Santos, Jesús Riquelme & Payan, Manuel Burgos, 2010. "Optimization of wind farm turbines layout using an evolutive algorithm," Renewable Energy, Elsevier, vol. 35(8), pages 1671-1681.
    8. Grady, S.A. & Hussaini, M.Y. & Abdullah, M.M., 2005. "Placement of wind turbines using genetic algorithms," Renewable Energy, Elsevier, vol. 30(2), pages 259-270.
    9. Schleisner, L, 2000. "Life cycle assessment of a wind farm and related externalities," Renewable Energy, Elsevier, vol. 20(3), pages 279-288.
    10. Tremeac, Brice & Meunier, Francis, 2009. "Life cycle analysis of 4.5Â MW and 250Â W wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 2104-2110, October.
    11. Hong, Lixuan & Möller, Bernd, 2011. "Offshore wind energy potential in China: Under technical, spatial and economic constraints," Energy, Elsevier, vol. 36(7), pages 4482-4491.
    12. Kusiak, Andrew & Song, Zhe, 2010. "Design of wind farm layout for maximum wind energy capture," Renewable Energy, Elsevier, vol. 35(3), pages 685-694.
    13. Li, G, 2000. "Feasibility of large scale offshore wind power for Hong Kong — a preliminary study," Renewable Energy, Elsevier, vol. 21(3), pages 387-402.
    14. Kusiak, Andrew & Zheng, Haiyang, 2010. "Optimization of wind turbine energy and power factor with an evolutionary computation algorithm," Energy, Elsevier, vol. 35(3), pages 1324-1332.
    15. Zhixin, Wang & Chuanwen, Jiang & Qian, Ai & Chengmin, Wang, 2009. "The key technology of offshore wind farm and its new development in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 216-222, January.
    16. Blanco, María Isabel, 2009. "The economics of wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1372-1382, August.
    17. Sun, Xiaojing & Huang, Diangui & Wu, Guoqing, 2012. "The current state of offshore wind energy technology development," Energy, Elsevier, vol. 41(1), pages 298-312.
    18. Esteban, Miguel & Leary, David, 2012. "Current developments and future prospects of offshore wind and ocean energy," Applied Energy, Elsevier, vol. 90(1), pages 128-136.
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