An Optimal Active Power Allocation Method for Wind Farms Considering Unit Fatigue Load

Zhi Huang, Xinyu Yang, Sile Hu, Yu Guo, Yutong Wang, Xianglong Liu, Yuan Wang, Wenjing Liang and Jiaqiang Yang ()
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Zhi Huang: Inner Mongolia Daqingshan Laboratory Co., Ltd., Hohhot 010020, China
Xinyu Yang: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Sile Hu: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Yu Guo: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Yutong Wang: Inner Mongolia Daqingshan Laboratory Co., Ltd., Hohhot 010020, China
Xianglong Liu: Inner Mongolia Power (Group) Co., Ltd., Hohhot 010020, China
Yuan Wang: Inner Mongolia Electric Power Group Mengdian Economic and Technical Research Institute Co., Ltd., Hohhot 010020, China
Wenjing Liang: Inner Mongolia Daqingshan Laboratory Co., Ltd., Hohhot 010020, China
Jiaqiang Yang: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Sustainability, 2025, vol. 17, issue 20, 1-20

Abstract: To address the issue of premature wear and tear in wind turbines due to uneven fatigue load distribution within wind farms, this study proposes an optimal active power allocation method that considers unit fatigue loads. First, the fatigue load expressions for wind turbine shafts and tower systems with two degrees of freedom are derived, and a quantitative relationship between turbine fatigue load and active power output variations is established. Subsequently, the optimization objective is set as minimizing the total fatigue load in the wind farm during frequency regulation. This model incorporates the fatigue load differences among different turbines and ensures that the sum of the power adjustments across all turbines meets the frequency regulation power demand, resulting in an active power allocation model. To solve this optimization model, an improved Firefly Algorithm (IFA), integrating Logistic mapping and an adaptive weight strategy, is employed. Aligned with the recommended goals of sustainable development, this approach not only reduces fatigue loads, enhancing the lifespan and efficiency of wind turbines, but also ensures that the wind farm retains strong frequency regulation performance. By optimizing turbine performance and promoting a more balanced load distribution, the proposed method significantly contributes to the overall reliability and economic sustainability of renewable energy systems. Finally, a case study system consisting of nine 5 MW turbines is established to validate the proposed method, demonstrating its ability to evenly distribute the fatigue load across turbines while effectively tracking higher-level dispatch commands and reducing the same fatigue loads.

Keywords: wind power; fatigue load; active power allocation; optimization strategy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:20:p:9189-:d:1773023

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