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Multivariate optimization of off-grid wind turbines with variable demand - Case study of a remote commercial building

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  • Baniassadi, Amir
  • Shirinbakhsh, Mehrdad
  • Torabi, Farschad

Abstract

Generally, in optimization of wind turbines, the annual energy output is set as the objective function. However, as verified in this study, maximizing the annual energy does not necessarily guarantee the optimum design for off-grid wind turbines. In such cases, the demand profile can have a significant effect on design and performance of the rotor. In this study, an off-grid wind turbine is optimized while considering the demand profile. Blade element method, genetic algorithm, and EnergyPlus® are applied in a coupled scheme to obtain the optimum design. Results suggest that if minimizing the annual energy deficit or fuel consumption is set as the objective function, the obtained optimum design will be different to the case in which maximizing the annual wind generated energy is set as the target. Accordingly, in the investigated case of this study, the annual wind fraction increased up to 5% by applying the proposed objective function.

Suggested Citation

  • Baniassadi, Amir & Shirinbakhsh, Mehrdad & Torabi, Farschad, 2017. "Multivariate optimization of off-grid wind turbines with variable demand - Case study of a remote commercial building," Renewable Energy, Elsevier, vol. 101(C), pages 1021-1029.
  • Handle: RePEc:eee:renene:v:101:y:2017:i:c:p:1021-1029
    DOI: 10.1016/j.renene.2016.09.067
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    1. Del Valle Carrasco, Arturo & Valles-Rosales, Delia J. & Mendez, Luis C. & Rodriguez, Manuel I., 2016. "A site-specific design of a fixed-pitch fixed-speed wind turbine blade for energy optimization using surrogate models," Renewable Energy, Elsevier, vol. 88(C), pages 112-119.
    2. Zhu, Wei Jun & Shen, Wen Zhong & Sørensen, Jens Nørkær, 2014. "Integrated airfoil and blade design method for large wind turbines," Renewable Energy, Elsevier, vol. 70(C), pages 172-183.
    3. Dai, J.C. & Hu, Y.P. & Liu, D.S. & Long, X., 2011. "Aerodynamic loads calculation and analysis for large scale wind turbine based on combining BEM modified theory with dynamic stall model," Renewable Energy, Elsevier, vol. 36(3), pages 1095-1104.
    4. Vučina, Damir & Marinić-Kragić, Ivo & Milas, Zoran, 2016. "Numerical models for robust shape optimization of wind turbine blades," Renewable Energy, Elsevier, vol. 87(P2), pages 849-862.
    5. Mittal, Prateek & Kulkarni, Kedar & Mitra, Kishalay, 2016. "A novel hybrid optimization methodology to optimize the total number and placement of wind turbines," Renewable Energy, Elsevier, vol. 86(C), pages 133-147.
    6. Hodzic, Migdat & Tai, Li-Chou, 2016. "Grey Predictor reference model for assisting particle swarm optimization for wind turbine control," Renewable Energy, Elsevier, vol. 86(C), pages 251-256.
    7. Shen, Xin & Chen, Jin-Ge & Zhu, Xiao-Cheng & Liu, Peng-Yin & Du, Zhao-Hui, 2015. "Multi-objective optimization of wind turbine blades using lifting surface method," Energy, Elsevier, vol. 90(P1), pages 1111-1121.
    8. Bavanish, B. & Thyagarajan, K., 2013. "Optimization of power coefficient on a horizontal axis wind turbine using bem theory," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 169-182.
    9. Jabbari Asl, Hamed & Yoon, Jungwon, 2016. "Power capture optimization of variable-speed wind turbines using an output feedback controller," Renewable Energy, Elsevier, vol. 86(C), pages 517-525.
    10. Guo, Qiang & Zhou, Lingjiu & Wang, Zhengwei, 2015. "Comparison of BEM-CFD and full rotor geometry simulations for the performance and flow field of a marine current turbine," Renewable Energy, Elsevier, vol. 75(C), pages 640-648.
    11. Song, Mengxuan & Chen, Kai & Zhang, Xing & Wang, Jun, 2016. "Optimization of wind turbine micro-siting for reducing the sensitivity of power generation to wind direction," Renewable Energy, Elsevier, vol. 85(C), pages 57-65.
    12. Pourrajabian, Abolfazl & Nazmi Afshar, Peyman Amir & Ahmadizadeh, Mehdi & Wood, David, 2016. "Aero-structural design and optimization of a small wind turbine blade," Renewable Energy, Elsevier, vol. 87(P2), pages 837-848.
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    Cited by:

    1. Douak, M. & Aouachria, Z. & Rabehi, R. & Allam, N., 2018. "Wind energy systems: Analysis of the self-starting physics of vertical axis wind turbine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1602-1610.
    2. Si, Binghui & Tian, Zhichao & Jin, Xing & Zhou, Xin & Shi, Xing, 2019. "Ineffectiveness of optimization algorithms in building energy optimization and possible causes," Renewable Energy, Elsevier, vol. 134(C), pages 1295-1306.

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