Power Grid Primary Frequency Control Strategy Based on Fuzzy Adaptive and State-of-Charge Self-Recovery of Flywheel–Battery Hybrid Energy Storage System

Shaobo Wen (), Yipeng Gong, Zhendong Zhao, Xiufeng Mu and Sufang Zhao
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Shaobo Wen: School of Traffic Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Yipeng Gong: School of Traffic Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Zhendong Zhao: School of Traffic Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Xiufeng Mu: School of Communication and Artificial Intelligence, School of Integrated Circuits, Nanjing Institute of Technology, Nanjing 211167, China
Sufang Zhao: School of Traffic Engineering, Nanjing Institute of Technology, Nanjing 211167, China

Energies, 2025, vol. 18, issue 6, 1-23

Abstract: The integration of new renewable energy sources, such as wind and solar power, is characterized by strong randomness and volatility, which increases the risk of power grid system frequency fluctuations exceeding limits. Traditional thermal power units are unable to frequently respond to frequency regulation signals, necessitating the incorporation of energy storage technologies for primary frequency control. This paper presents a primary frequency control strategy for a flywheel–battery hybrid energy storage system (HESS) based on fuzzy adaptation and state-of-charge (SOC) self-recovery. First, a frequency response system model for primary frequency regulation in flywheel–battery hybrid energy storage was formulated. The frequency regulation command is divided into high-frequency and low-frequency components, which are allocated to the flywheel and the battery, respectively. Fuzzy control and regression functions were employed to adjust and constrain the frequency deviation, frequency deviation rate, and SOC. Subsequently, considering the SOC and frequency deviation of each energy storage component, a SOC self-recovery strategy was proposed. Finally, a simulation analysis was performed using a system benchmark capacity of 600 MW. Under step load disturbance conditions, the proposed strategy reduces the maximum frequency deviation by 10.52% and the steady-state frequency deviation by 8.35% compared with traditional methods. Under random load disturbance conditions, the root mean square (RMS) value of frequency deviation is reduced by 7.34%, and the peak-to-valley difference of frequency decreases by 6.74%. Compared to energy storage without SOC self-recovery, the RMS values of SOC for flywheel storage and battery storage are reduced by 8.79% and 16.68%, respectively. The results demonstrate that the proposed control strategy effectively improves the system’s frequency regulation performance, reduces energy storage output fluctuations, and enhances the SOC self-recovery effect of the HESS.

Keywords: flywheel–battery hybrid energy storage; primary frequency control; fuzzy control; SOC self-recovery (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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