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Genetic algorithm based optimized model for the selection of wind turbine for any site-specific wind conditions

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  • Petrović, A.
  • Đurišić, Ž.

Abstract

The paper presents a practical mathematical model for determining the optimal wind turbine (WT) design based on the known wind statistics at WT location and wind shear exponent. The basic WT optimization parameters are hub height (HH), WT rotor diameter (RD) and WT rated power (RP). The proposed model varies WT parameters and calculates the total actualized WT costs as well as the annual energy production (AEP). The optimal values of WT parameters are those at which the minimum levelized cost of energy (LCOE) is achieved. Firstly, the LCOE model is built up and WT optimization problem is defined. Then, genetic algorithm is presented and improved. A quantitative assessment of the drop in LCOE, had the unoptimized WT been used is done. Lastly, the practical applicability of the proposed model is demonstrated on real examples of WTs installed at locations with different wind conditions. It is indicated that by optimization of WT parameters, LCOE can be reduced effectively. The developed algorithm and mathematical model have a general character and can be used to optimize utilization of wind potential at a particular location and also provide maximization of the profit generated by the wind power plant within its life-span.

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  • Petrović, A. & Đurišić, Ž., 2021. "Genetic algorithm based optimized model for the selection of wind turbine for any site-specific wind conditions," Energy, Elsevier, vol. 236(C).
    • Handle: RePEc:eee:energy:v:236:y:2021:i:c:s0360544221017242
    DOI: 10.1016/j.energy.2021.121476
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    References listed on IDEAS

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    Cited by:

    1. Asmita Ajay Rathod & Balaji Subramanian, 2022. "Scrutiny of Hybrid Renewable Energy Systems for Control, Power Management, Optimization and Sizing: Challenges and Future Possibilities," Sustainability, MDPI, vol. 14(24), pages 1-35, December.
    2. Xu, Li & Wang, Jin & Ou, Yanxia & Fu, Yang & Bian, Xiaoyan, 2022. "A novel decision-making system for selecting offshore wind turbines with PCA and D numbers," Energy, Elsevier, vol. 258(C).

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