Energy Storage Multi-Stage Dynamic Frequency Modulation Control Strategy Based on Inertia–Damping Coordination Tuning

High renewable penetration reduces system inertia and limits frequency support capability. Conventional energy storage systems using fixed inertia–damping virtual synchronous generator control cannot adapt to the multi-stage characteristics of frequency dynamics. To address this issue, a multi-stage dynamic frequency regulation strategy for energy storage is proposed based on coordinated inertia–damping tuning. First, a four-stage dynamic frequency response model is established according to the gradient characteristics of the frequency trajectory. The model covers inertia support, primary frequency regulation, and steady-state restoration. It reveals the differentiated requirements for inertia and damping parameters across frequency regulation stages. Second, a smooth inertia–damping transition mechanism is designed using hyperbolic tangent and sigmoid buffer functions. The buffer functions enable coordinated parameter switching and suppress secondary disturbances caused by abrupt changes. Finally, simulation results show that compared with conventional fixed-parameter strategies, the proposed method reduces the rate of change of frequency by 34.42%. The steady-state frequency deviation is decreased by 10.69%. The frequency recovery time is shortened by 19.55%, and the overshoot is reduced by 11.37%. These results demonstrate that the proposed strategy enhances transient frequency stability in power systems with high renewable penetration. The proposed method provides theoretical support and technical guidance for large-scale energy storage participation in frequency regulation.

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Publication Details

Journal
Energies
Published
2026-03-12
DOI
https://doi.org/10.3390/en19061430
Primary Topic
Microgrid Control and Optimization
Type
article
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Energy Storage Multi-Stage Dynamic Frequency Modulation Control Strategy Based on Inertia–Damping Coordination Tuning

Xiang Ren, Dong Han, L.H. Liu, Jia Dong et al.
Energies
Microgrid Control and Optimization
article

Energy Storage Multi-Stage Dynamic Frequency Modulation Control Strategy Based on Inertia–Damping Coordination Tuning

Xiang Ren, Dong Han, L.H. Liu, Jia Dong, Li Zhang, Ning Zhang
article en

Abstract

High renewable penetration reduces system inertia and limits frequency support capability. Conventional energy storage systems using fixed inertia–damping virtual synchronous generator control cannot adapt to the multi-stage characteristics of frequency dynamics. To address this issue, a multi-stage dynamic frequency regulation strategy for energy storage is proposed based on coordinated inertia–damping tuning. First, a four-stage dynamic frequency response model is established according to the gradient characteristics of the frequency trajectory. The model covers inertia support, primary frequency regulation, and steady-state restoration. It reveals the differentiated requirements for inertia and damping parameters across frequency regulation stages. Second, a smooth inertia–damping transition mechanism is designed using hyperbolic tangent and sigmoid buffer functions. The buffer functions enable coordinated parameter switching and suppress secondary disturbances caused by abrupt changes. Finally, simulation results show that compared with conventional fixed-parameter strategies, the proposed method reduces the rate of change of frequency by 34.42%. The steady-state frequency deviation is decreased by 10.69%. The frequency recovery time is shortened by 19.55%, and the overshoot is reduced by 11.37%. These results demonstrate that the proposed strategy enhances transient frequency stability in power systems with high renewable penetration. The proposed method provides theoretical support and technical guidance for large-scale energy storage participation in frequency regulation.

EnergiesVol. 19(6)
Anhui University (CN), University of Shanghai for Science and Technology (CN), State Grid Corporation of China (China) (CN)
Affordable and clean energy
Openalex Percentile: Top 26%
Microgrid Control and Optimization
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