An Artificial Intelligence Frequency Regulation Strategy for Renewable Energy Grids Based on Hybrid Energy Storage

Qiang Zhang (), Qi Jia, Tingqi Zhang, Hui Zeng, Chao Wang, Wansong Liu, Hanlin Li and Yihui Song
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Qiang Zhang: State Grid Liaoning Electric Power Research Institute, Shenyang 110003, China
Qi Jia: State Grid Liaoning Electric Power Research Institute, Shenyang 110003, China
Tingqi Zhang: State Grid Liaoning Electric Power Research Institute, Shenyang 110003, China
Hui Zeng: State Grid Liaoning Electric Power Research Institute, Shenyang 110003, China
Chao Wang: State Grid Liaoning Electric Power Research Institute, Shenyang 110003, China
Wansong Liu: State Grid Liaoning Electric Power Research Institute, Shenyang 110003, China
Hanlin Li: Shenyang Institute of Engineering, Shenyang 110136, China
Yihui Song: Shenyang Institute of Engineering, Shenyang 110136, China

Energies, 2025, vol. 18, issue 10, 1-23

Abstract: To address the frequency regulation requirements of hybrid energy storage (HES) in renewable-dominated power grids, this paper proposes an asymmetric droop control strategy based on an improved backpropagation (BP) neural network. First, a simulation model of HES (comprising supercapacitors for the power support and batteries for the energy balance) participating in primary frequency regulation is established, with a stepwise frequency regulation dead zone designed to optimize multi-device coordination. Second, an enhanced Sigmoid activation function (with controllable parameters a , b , m , and n ) is introduced to dynamically adjust the power regulation coefficients of energy storage units, achieving co-optimization of frequency stability and State of Charge (SOC). Simulation results demonstrate that under a step load disturbance (0.05 p.u.), the proposed strategy reduces the maximum frequency deviation by 79.47% compared to scenarios without energy storage (from 1.7587 × 10 −3 to 0.0555 × 10 −3 ) and outperforms fixed-droop strategies by 44.33%. During 6-min continuous random disturbances, the root mean square (RMS) of system frequency deviations decreases by 4.91% compared to conventional methods, while SOC fluctuations of supercapacitors and batteries are reduced by 49.28% and 45.49%, respectively. The parameterized asymmetric regulation mechanism significantly extends the lifespan of energy storage devices, offering a novel solution for real-time frequency control in high-renewable penetration grids.

Keywords: asymmetric droop control; hybrid energy storage; improved backpropagation neural network; frequency stability; state of charge optimization (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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