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Optimal power dispatch in wind farm based on reduced blade damage and generator losses

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  • Zhang, Jin-hua
  • Liu, Yong-qian
  • Tian, De
  • Yan, Jie

Abstract

For large wind farms, the unit combinatorial optimization research can improve the level of wind farm operation and increase the economic efficiency of wind farms. The blades, which not only account for 20% of the total wind turbine cost, but also have much influence on the wind turbine life, are important components of wind turbines. With the stress analysis of blade roots in four different load conditions, this paper establishes a mathematic model quantifying damage of the blades and its relationship with blade life. The relationship between generator losses and reactive power is also established according to the generator losses of wind turbines. Moreover, based on the theory of binary particle swarm algorithm, genetic algorithm and GA-IBPSO algorithm, three optimal power dispatch models are proposed. The models take wind power prediction and load demand of electrical system into consideration and the objective of the optimization are to minimize the blade damage and generator losses. Application of the proposed optimal dispatching model in a 49.5MW wind farm has proved itself feasible and reliable in reducing the number of start-stop times, extending the unit life while meeting the load requirements.

Suggested Citation

  • Zhang, Jin-hua & Liu, Yong-qian & Tian, De & Yan, Jie, 2015. "Optimal power dispatch in wind farm based on reduced blade damage and generator losses," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 64-77.
    • Handle: RePEc:eee:rensus:v:44:y:2015:i:c:p:64-77
    DOI: 10.1016/j.rser.2014.12.008
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    References listed on IDEAS

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    1. Shamshad, A. & Bawadi, M.A. & Wan Hussin, W.M.A. & Majid, T.A. & Sanusi, S.A.M., 2005. "First and second order Markov chain models for synthetic generation of wind speed time series," Energy, Elsevier, vol. 30(5), pages 693-708.
    2. Niknam, Taher & Taheri, Seyed Iman & Aghaei, Jamshid & Tabatabaei, Sajad & Nayeripour, Majid, 2011. "A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources," Applied Energy, Elsevier, vol. 88(12), pages 4817-4830.
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    1. Yao, Qi & Hu, Yang & Deng, Hui & Luo, Zhiling & Liu, Jizhen, 2020. "Two-degree-of-freedom active power control of megawatt wind turbine considering fatigue load optimization," Renewable Energy, Elsevier, vol. 162(C), pages 2096-2112.
    2. Baohua Zhang & Weihao Hu & Peng Hou & Jin Tan & Mohsen Soltani & Zhe Chen, 2017. "Review of Reactive Power Dispatch Strategies for Loss Minimization in a DFIG-based Wind Farm," Energies, MDPI, vol. 10(7), pages 1-17, June.
    3. Hyungyu Kim & Kwansu Kim & Insu Paek, 2019. "A Study on the Effect of Closed-Loop Wind Farm Control on Power and Tower Load in Derating the TSO Command Condition," Energies, MDPI, vol. 12(10), pages 1-19, May.
    4. Yao, Qi & Hu, Yang & Zhao, Tianyang & Guan, Yuanpeng & Luo, Zhiling & Liu, Jizhen, 2022. "Fatigue load suppression during active power control process in wind farm using dynamic-local-reference DMPC," Renewable Energy, Elsevier, vol. 183(C), pages 423-434.
    5. Xingkang Jin & Wen Tan & Yarong Zou & Zijian Wang, 2022. "Active Disturbance Rejection Control for Wind Turbine Fatigue Load," Energies, MDPI, vol. 15(17), pages 1-15, August.

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