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

Bin Cheng, Jiahui Wu (), Guancheng Lv and Zhongbo Li
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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

Energies, 2025, vol. 18, issue 7, 1-21

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.

Keywords: wind power grid integration; energy storage health; genetic algorithm; support vector regression; model predictive control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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