Aggregate model of large-scale parallel grid-forming inverters for converter-driven stability analysis

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Authors: Naiara Goñi, Javier Marcos, Miguel García, Alberto Garcı́a, Luis Marroyo, Guillermo Bautista
Cited by: 1
References: 18

Abstract

• Accurate 2-aggregate inverter model for large-scale inverter-based plants. • A single-inverter model cannot represent inverter-to-inverter interactions. • Inverter-to-grid and inverter-to-inverter resonances arise in large-scale plants. • The proposed model considers parameter dispersion focusing on stability analysis. • Validation against accurate EMT simulations considering parameter dispersion. The increasing share of renewable energy plants is transforming power systems into inverter-dominated grids with low inertia and weak-grid conditions. Grid-Forming (GFM) inverters can address these challenges by emulating the dynamics of synchronous generators; however, large-scale GFM plants introduce complex multi-inverter and grid interactions that are not yet fully understood. This paper proposes a reduced order but high-fidelity aggregate model for large-scale GFM plants, capable of representing both plant-to-grid and internal inverter-to-inverter resonances while considering parameter dispersion among inverters for converter-driven stability analysis. The model is specifically designed for the planning and diagnosis of resonances occurring at the normal operating point in grids with a high penetration of GFM resources. Cross-validation against detailed averaged electromagnetic transient simulations confirm that the proposed 2-AggGFM model accurately reproduces the critical dynamic behavior of multi-inverter plants, including resonance excitability from grid and inverter perturbations. These results demonstrate its effectiveness as a practical and efficient model for converter-driven stability assessment and control-oriented design in large-scale inverter-based grids.

Fields of Study & Subjects

Microgrid Control and Optimization HVDC Systems and Fault Protection Islanding Detection in Power Systems

Publication details

Journal / source
International Journal of Electrical Power & Energy Systems
Publisher
Elsevier BV
Volume
178
Pages
111899–111899
Publication date
1 May 2026
Language
EN