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

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.

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Publication Details

Journal
Sustainability
Published
2025-10-16
DOI
https://doi.org/10.3390/su17209189
Primary Topic
Power Systems and Renewable Energy
Type
article
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article

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

Jiaqiang Yang, Wenjing Liang, Yutong Wang, Zhi Gao Huang et al.
Sustainability
Power Systems and Renewable Energy
article

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

Jiaqiang Yang, Wenjing Liang, Yutong Wang, Zhi Gao Huang, Xinyu Yang, Yuan Wang, Xianglong Liu, Yu Guo, Sile Hu
article en

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.

SustainabilityVol. 17(20)
Inner Mongolia Electric Power (China) (CN), Inner Mongolia Electric Power Survey & Design Institute (China) (CN), Zhejiang University (CN)
Openalex Percentile: Top 67%
Power Systems and Renewable Energy
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