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Multidisciplinary design optimization of offshore wind turbines for minimum levelized cost of energy

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  • Ashuri, T.
  • Zaaijer, M.B.
  • Martins, J.R.R.A.
  • van Bussel, G.J.W.
  • van Kuik, G.A.M.

Abstract

This paper presents a method for multidisciplinary design optimization of offshore wind turbines at system level. The formulation and implementation that enable the integrated aerodynamic and structural design of the rotor and tower simultaneously are detailed. The objective function to be minimized is the levelized cost of energy. The model includes various design constraints: stresses, deflections, modal frequencies and fatigue limits along different stations of the blade and tower. The rotor design variables are: chord and twist distribution, blade length, rated rotational speed and structural thicknesses along the span. The tower design variables are: tower thickness and diameter distribution, as well as the tower height. For the other wind turbine components, a representative mass model is used to include their dynamic interactions in the system. To calculate the system costs, representative cost models of a wind turbine located in an offshore wind farm are used. To show the potential of the method and to verify its usefulness, the 5 MW NREL wind turbine is used as a case study. The result of the design optimization process shows 2.3% decrease in the levelized cost of energy for a representative Dutch site, while satisfying all the design constraints.

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  • Ashuri, T. & Zaaijer, M.B. & Martins, J.R.R.A. & van Bussel, G.J.W. & van Kuik, G.A.M., 2014. "Multidisciplinary design optimization of offshore wind turbines for minimum levelized cost of energy," Renewable Energy, Elsevier, vol. 68(C), pages 893-905.
    • Handle: RePEc:eee:renene:v:68:y:2014:i:c:p:893-905
    DOI: 10.1016/j.renene.2014.02.045
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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. 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.
    3. 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.
    4. Kusiak, Andrew & Song, Zhe, 2010. "Design of wind farm layout for maximum wind energy capture," Renewable Energy, Elsevier, vol. 35(3), pages 685-694.
    5. Maalawi, K.Y. & Badr, M.A, 2003. "A practical approach for selecting optimum wind rotors," Renewable Energy, Elsevier, vol. 28(5), pages 803-822.
    6. Karadeniz, Halil & ToÄŸan, Vedat & Vrouwenvelder, Ton, 2009. "An integrated reliability-based design optimization of offshore towers," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1510-1516.
    7. Maki, Kevin & Sbragio, Ricardo & Vlahopoulos, Nickolas, 2012. "System design of a wind turbine using a multi-level optimization approach," Renewable Energy, Elsevier, vol. 43(C), pages 101-110.
    8. Fatih Karpat, 2013. "A Virtual Tool for Minimum Cost Design of a Wind Turbine Tower with Ring Stiffeners," Energies, MDPI, vol. 6(8), pages 1-19, July.
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    3. Reddy, Sohail R., 2021. "A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design," Energy, Elsevier, vol. 220(C).
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    6. Mohammad Barooni & Turaj Ashuri & Deniz Velioglu Sogut & Stephen Wood & Shiva Ghaderpour Taleghani, 2022. "Floating Offshore Wind Turbines: Current Status and Future Prospects," Energies, MDPI, vol. 16(1), pages 1-28, December.
    7. Ozan Gözcü & Taeseong Kim & David Robert Verelst & Michael K. McWilliam, 2022. "Swept Blade Dynamic Investigations for a 100 kW Small Wind Turbine," Energies, MDPI, vol. 15(9), pages 1-22, April.
    8. Chen, Jincheng & Wang, Feng & Stelson, Kim A., 2018. "A mathematical approach to minimizing the cost of energy for large utility wind turbines," Applied Energy, Elsevier, vol. 228(C), pages 1413-1422.
    9. Sessarego, Matias & Ramos-García, Néstor & Yang, Hua & Shen, Wen Zhong, 2016. "Aerodynamic wind-turbine rotor design using surrogate modeling and three-dimensional viscous–inviscid interaction technique," Renewable Energy, Elsevier, vol. 93(C), pages 620-635.
    10. van Dijk, Mike T. & van Wingerden, Jan-Willem & Ashuri, Turaj & Li, Yaoyu, 2017. "Wind farm multi-objective wake redirection for optimizing power production and loads," Energy, Elsevier, vol. 121(C), pages 561-569.
    11. Kyoungboo Yang, 2020. "Geometry Design Optimization of a Wind Turbine Blade Considering Effects on Aerodynamic Performance by Linearization," Energies, MDPI, vol. 13(9), pages 1-18, May.
    12. Barooni, M. & Ale Ali, N. & Ashuri, T., 2018. "An open-source comprehensive numerical model for dynamic response and loads analysis of floating offshore wind turbines," Energy, Elsevier, vol. 154(C), pages 442-454.
    13. Zhang, Bin & E, Jiaqiang & Gong, Jinke & Yuan, Wenhua & Zuo, Wei & Li, Yu & Fu, Jun, 2016. "Multidisciplinary design optimization of the diesel particulate filter in the composite regeneration process," Applied Energy, Elsevier, vol. 181(C), pages 14-28.
    14. Koh, J.H. & Ng, E.Y.K., 2016. "Downwind offshore wind turbines: Opportunities, trends and technical challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 797-808.
    15. Ahmed, Ahsan & Nadeem, Talha Bin & Naqvi, Asad A. & Siddiqui, Mubashir Ali & Khan, Muhammad Hamza & Bin Zahid, Muhammad Saad & Ammar, Syed Muhammad, 2022. "Investigation of PV utilizability on university buildings: A case study of Karachi, Pakistan," Renewable Energy, Elsevier, vol. 195(C), pages 238-251.
    16. Jie Zhu & Xin Cai & Rongrong Gu, 2017. "Multi-Objective Aerodynamic and Structural Optimization of Horizontal-Axis Wind Turbine Blades," Energies, MDPI, vol. 10(1), pages 1-18, January.
    17. Khazaee, Meghdad & Derian, Pierre & Mouraud, Anthony, 2022. "A comprehensive study on Structural Health Monitoring (SHM) of wind turbine blades by instrumenting tower using machine learning methods," Renewable Energy, Elsevier, vol. 199(C), pages 1568-1579.
    18. Zhu, Jie & Zhou, Zhong & Cai, Xin, 2020. "Multi-objective aerodynamic and structural integrated optimization design of wind turbines at the system level through a coupled blade-tower model," Renewable Energy, Elsevier, vol. 150(C), pages 523-537.
    19. Chen, Z.J. & Stol, K.A. & Mace, B.R., 2017. "Wind turbine blade optimisation with individual pitch and trailing edge flap control," Renewable Energy, Elsevier, vol. 103(C), pages 750-765.
    20. Al-Nassar, W.K. & Neelamani, S. & Al-Salem, K.A. & Al-Dashti, H.A., 2019. "Feasibility of offshore wind energy as an alternative source for the state of Kuwait," Energy, Elsevier, vol. 169(C), pages 783-796.
    21. Michael K. McWilliam & Antariksh C. Dicholkar & Frederik Zahle & Taeseong Kim, 2022. "Post-Optimum Sensitivity Analysis with Automatically Tuned Numerical Gradients Applied to Swept Wind Turbine Blades," Energies, MDPI, vol. 15(9), pages 1-19, April.
    22. Hailun Xie & Lars Johanning, 2023. "A Hierarchical Met-Ocean Data Selection Model for Fast O&M Simulation in Offshore Renewable Energy Systems," Energies, MDPI, vol. 16(3), pages 1-20, February.
    23. Manchado, Cristina & Gomez-Jauregui, Valentin & Lizcano, Piedad E. & Iglesias, Andres & Galvez, Akemi & Otero, Cesar, 2019. "Wind farm repowering guided by visual impact criteria," Renewable Energy, Elsevier, vol. 135(C), pages 197-207.
    24. 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.

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