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Investigating the Power-COE trade-off for wind farm layout optimization considering commercial turbine selection and hub height variation

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  • Abdulrahman, Mamdouh
  • Wood, David

Abstract

New aspects were added to the wind farm layout optimization problem by including commercial turbine selection and a realistic representation for the thrust coefficient. The available manufacturers' data were used to develop generic representation for the thrust coefficient, on which the wake and power calculations mainly depend. A simple field-based cost model was implemented. The optimization was performed for onshore and offshore conditions using MATLAB genetic algorithm, ga, solver for two simple test cases. Three objective functions were considered, individually: (1) the output power, (2) the capacity factor, and (3) the cost per output power.

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  • Abdulrahman, Mamdouh & Wood, David, 2017. "Investigating the Power-COE trade-off for wind farm layout optimization considering commercial turbine selection and hub height variation," Renewable Energy, Elsevier, vol. 102(PB), pages 267-278.
    • Handle: RePEc:eee:renene:v:102:y:2017:i:pb:p:267-278
    DOI: 10.1016/j.renene.2016.10.038
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    Cited by:

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    6. Wang, Longyan & Cholette, Michael E. & Zhou, Yunkai & Yuan, Jianping & Tan, Andy C.C. & Gu, Yuantong, 2018. "Effectiveness of optimized control strategy and different hub height turbines on a real wind farm optimization," Renewable Energy, Elsevier, vol. 126(C), pages 819-829.
    7. Froese, Gabrielle & Ku, Shan Yu & Kheirabadi, Ali C. & Nagamune, Ryozo, 2022. "Optimal layout design of floating offshore wind farms," Renewable Energy, Elsevier, vol. 190(C), pages 94-102.
    8. Feng, Ju & Shen, Wen Zhong, 2017. "Design optimization of offshore wind farms with multiple types of wind turbines," Applied Energy, Elsevier, vol. 205(C), pages 1283-1297.
    9. Tao, Siyu & Xu, Qingshan & Feijóo, Andrés & Zheng, Gang & Zhou, Jiemin, 2020. "Wind farm layout optimization with a three-dimensional Gaussian wake model," Renewable Energy, Elsevier, vol. 159(C), pages 553-569.
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    11. Wu, Yan & Xia, Tianqi & Wang, Yufei & Zhang, Haoran & Feng, Xiao & Song, Xuan & Shibasaki, Ryosuke, 2022. "A synchronization methodology for 3D offshore wind farm layout optimization with multi-type wind turbines and obstacle-avoiding cable network," Renewable Energy, Elsevier, vol. 185(C), pages 302-320.
    12. Govindan, Kannan, 2023. "Pathways to low carbon energy transition through multi criteria assessment of offshore wind energy barriers," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    13. Yin, Peng-Yeng & Cheng, Chun-Ying & Chen, Hsin-Min & Wu, Tsai-Hung, 2020. "Risk-aware optimal planning for a hybrid wind-solar farm," Renewable Energy, Elsevier, vol. 157(C), pages 290-302.
    14. Sedaghat, Ahmad & Hassanzadeh, Arash & Jamali, Jamaloddin & Mostafaeipour, Ali & Chen, Wei-Hsin, 2017. "Determination of rated wind speed for maximum annual energy production of variable speed wind turbines," Applied Energy, Elsevier, vol. 205(C), pages 781-789.
    15. Nansheng Pang & Mengfan Nan & Qichen Meng & Siyang Zhao, 2021. "Selection of Wind Turbine Based on Fuzzy Analytic Network Process: A Case Study in China," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
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    17. Yin, Peng-Yeng & Wu, Tsai-Hung & Hsu, Ping-Yi, 2017. "Simulation based risk management for multi-objective optimal wind turbine placement using MOEA/D," Energy, Elsevier, vol. 141(C), pages 579-597.

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