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Research on Wind Power Grid Integration Power Fluctuation Smoothing Control Strategy Based on Energy Storage Battery Health Prediction

Author

Listed:
  • Bin Cheng

    (School of Electrical Engineering, Xinjiang University, Urumqi 830047, China)

  • Jiahui Wu

    (School of Electrical Engineering, Xinjiang University, Urumqi 830047, China)

  • Guancheng Lv

    (CGN New Energy Investment (Shenzhen) Co., Ltd., Xinjiang Branch, Urumqi 830011, China)

  • Zhongbo Li

    (CGN New Energy Investment (Shenzhen) Co., Ltd., Xinjiang Branch, Urumqi 830011, China)

Abstract

Due to the volatility and uncertainty of wind power generation, energy storage can help mitigate the fluctuations in wind power grid integration. During its use, the health of the energy storage system, defined as the ratio of the current available capacity to the initial capacity, deteriorates, leading to a reduction in the available margin for power fluctuation smoothing. Therefore, it is necessary to predict the state of health (SOH) and adjust its charge/discharge control strategy based on the predicted SOH results. This study first adopts a Genetic Algorithm-Optimized Support Vector Regression (GA-SVR) model to predict the SOH of the energy storage system. Secondly, based on the health prediction results, a control strategy based on the model predictive control (MPC) algorithm is proposed to manage the energy storage system’s charge/discharge process, ensuring that the power meets grid integration requirements while minimizing energy storage lifespan loss. Further, since the lifespan loss caused by smoothing the same fluctuation differs at different health levels, a fuzzy adaptive control strategy is used to adjust the parameters of the MPC algorithm’s objective function under varying health conditions, thereby optimizing energy storage power and achieving the smooth control of the wind farm grid integration power at different energy storage health levels. Finally, a simulation is conducted in MATLAB for a 50 MW wind farm grid integration system, with experimental parameters adjusted accordingly. The experimental results show that the GA-SVR algorithm can accurately predict the health of the energy storage system, and the MPC-based control strategy derived from health predictions can improve grid power stability while adaptively adjusting energy storage output according to different health levels.

Suggested Citation

  • Bin Cheng & Jiahui Wu & Guancheng Lv & Zhongbo Li, 2025. "Research on Wind Power Grid Integration Power Fluctuation Smoothing Control Strategy Based on Energy Storage Battery Health Prediction," Energies, MDPI, vol. 18(7), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1795-:d:1627021
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    References listed on IDEAS

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    1. Xiaoqiong Pang & Rui Huang & Jie Wen & Yuanhao Shi & Jianfang Jia & Jianchao Zeng, 2019. "A Lithium-ion Battery RUL Prediction Method Considering the Capacity Regeneration Phenomenon," Energies, MDPI, vol. 12(12), pages 1-14, June.
    2. Lin, Cheng & Mu, Hao & Xiong, Rui & Shen, Weixiang, 2016. "A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm," Applied Energy, Elsevier, vol. 166(C), pages 76-83.
    3. Wei, Meng & Balaya, Palani & Ye, Min & Song, Ziyou, 2022. "Remaining useful life prediction for 18650 sodium-ion batteries based on incremental capacity analysis," Energy, Elsevier, vol. 261(PA).
    4. Petit, Martin & Prada, Eric & Sauvant-Moynot, Valérie, 2016. "Development of an empirical aging model for Li-ion batteries and application to assess the impact of Vehicle-to-Grid strategies on battery lifetime," Applied Energy, Elsevier, vol. 172(C), pages 398-407.
    5. Junda Huo & Jianwen Huo, 2024. "Hybrid Energy Storage Power Adaptive Optimization Strategy Based on Improved Model Predictive Control and Improved DBO-VMD," Energies, MDPI, vol. 17(13), pages 1-21, July.
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