Modeling and Mitigation of Spatial–Temporal Frequency Patterns in IBR-Dominated Power Systems

With power systems becoming increasingly dominated by inverter-based resources (IBRs), spatial–temporal frequency dynamics have emerged as a significant challenge due to the loss of mechanical inertia and increasing heterogeneity in inverter controls. Conventional models grounded in center-of-inertia (COI) frequency and system frequency response (SFR) fail to capture localized frequency behavior under disturbances, particularly in systems with non-uniform synthetic inertia and weak electrical coupling. This paper develops an analytical modeling framework for the characterization and mitigation of spatial–temporal frequency patterns in fully inverter-based systems. Local frequency dynamics are explicitly derived from the control characteristics of grid-forming (GFM) and grid-following (GFL) inverters and incorporated into a network-aware formulation using topology-dependent state-space equations. The proposed model elucidates the interplay between local control parameters and network structure in shaping the propagation of frequency disturbances. A coordinated mitigation approach is further introduced, leveraging the tuning of local inverter settings and system topology parameters to suppress spatial frequency deviations. The proposed mitigation method is developed under an analytically assumed disturbance scenario in which the disturbance location is considered known for modeling and analysis purposes. The framework establishes a principled foundation for the analysis, prediction, and mitigation of frequency dynamics in low-inertia power systems.

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Publication Details

Journal
Electronics
Published
2026-02-13
DOI
https://doi.org/10.3390/electronics15040813
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
0.00
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article

Modeling and Mitigation of Spatial–Temporal Frequency Patterns in IBR-Dominated Power Systems

Junqiang Gong, Fuquan Huang, Xinjie Zeng, Xiaohua Li et al.
Electronics
Microgrid Control and Optimization
article

Modeling and Mitigation of Spatial–Temporal Frequency Patterns in IBR-Dominated Power Systems

Junqiang Gong, Fuquan Huang, Xinjie Zeng, Xiaohua Li, Nodira Bakhadirovna Turgunova, Anarkhon Mamasadikovna Kosimakhunova, Ying Xue
article en

Abstract

With power systems becoming increasingly dominated by inverter-based resources (IBRs), spatial–temporal frequency dynamics have emerged as a significant challenge due to the loss of mechanical inertia and increasing heterogeneity in inverter controls. Conventional models grounded in center-of-inertia (COI) frequency and system frequency response (SFR) fail to capture localized frequency behavior under disturbances, particularly in systems with non-uniform synthetic inertia and weak electrical coupling. This paper develops an analytical modeling framework for the characterization and mitigation of spatial–temporal frequency patterns in fully inverter-based systems. Local frequency dynamics are explicitly derived from the control characteristics of grid-forming (GFM) and grid-following (GFL) inverters and incorporated into a network-aware formulation using topology-dependent state-space equations. The proposed model elucidates the interplay between local control parameters and network structure in shaping the propagation of frequency disturbances. A coordinated mitigation approach is further introduced, leveraging the tuning of local inverter settings and system topology parameters to suppress spatial frequency deviations. The proposed mitigation method is developed under an analytically assumed disturbance scenario in which the disturbance location is considered known for modeling and analysis purposes. The framework establishes a principled foundation for the analysis, prediction, and mitigation of frequency dynamics in low-inertia power systems.

ElectronicsVol. 15(4)
North China Electric Power University (CN), Shenzhen Stock Exchange (CN), Kurgan State University (RU), Ferghana Polytechnical Institute (UZ), Ferghana State University (UZ), South China University of Technology (CN)
Openalex Percentile: Top 57%
Microgrid Control and Optimization
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