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arxiveess.SY2026-07-10

Jacobian Voltage Stiffness Metric -- A Measure of Grid-Forming Capability and System Strength in IBR-Dominated Grids

Ambuj Gupta, Balarko Chaudhuri, Mark O'Malley

As power systems transition toward inverter-based resource (IBR)-dominated grids, traditional system strength definitions and metrics are becoming increasingly inadequate to characterize upcoming stability challenges. Emerging definitions characterize system strength in terms of "voltage source behind impedance (VSBI)" characteristics. Similarly, Grid-ForMing (GFM) IBRs are expected to contribute voltage stiffness by exhibiting near-constant VSBI characteristics in the (sub-)transient time frame. To quantify VSBI characteristics as a measure of system strength or grid-forming capability, this paper proposes the Jacobian Voltage Stiffness Metric (JVSM), derived from the frequency-domain Jacobian. JVSM provides a measure of both small-signal voltage magnitude and phase-angle stiffness. JVSM is demonstrated to serve as a compliance criterion for evaluating the VSBI characteristics of GFM IBRs. When applied for grid strength assessment, it more effectively identifies small-signal stability problems than state-of-the-art strength metrics. The proposed JVSM is validated through electromagnetic transient simulation case studies using the National Laboratory of the Rockies (NLR, formerly NREL) and WECC-approved industry-standard GFM IBR models and on a modified IEEE 39-bus system.

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arxivcs.ROeess.SY2026-07-24

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arxivcs.NIcs.MAeess.SY2026-07-24

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arxiveess.SY2026-07-24

Physics-Informed Neural Network for Modeling the Dynamic Behavior of Grid-Forming Converters

Hussein Jaffal, Arianna Fois, Sarra Bouchkati, Amirali Mahjoob, Andreas Ulbig

This paper investigates physics-informed neural networks for modeling the full dynamic behavior of droop-controlled grid-forming converters. The approach is trained on synthetic data generated via numerical solvers and benchmarked against both traditional integration methods and…

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arxiveess.SY2026-07-24

StateFormer: A Multivariate Transformer for Learning History-Dependent Battery State Dynamics and Long-Horizon Health Forecasting

Zhe Bai, Stephen Harris

This paper introduces a novel multivariate Transformer \emph{StateFormer} that forecasts degradation dynamics of large-scale battery systems. The model learns across time scales, from short-term thermal fluctuations to long-term aging trajectories, enabling accurate prediction of…

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