As renewable energy penetration increases, power systems face new stability challenges stemming from the different control characteristics of grid-following (GFL) and grid-forming (GFM) inverters. While GFM inverters provide essential voltage support that traditional synchronous generators once supplied, quantifying overall system stability when both types operate together has proven difficult. Researchers have now proposed a solution using an amplitude mapping model (AMM) to evaluate stability capability in hybrid GFL-GFM systems.
The method introduces waveform-amplitude stability as a lens for assessing system health. By establishing mathematical models of renewable energy delivery systems and deriving a stability ability (SA) index, engineers can now calculate a simple, practical expression for stability quantification rather than relying on complex simulations. This analytical approach reveals how key system parameters influence stability and enables identification of the critical power ratio between GFL and GFM inverters needed to maintain safe operation.
The practical value of this work lies in its ability to guide real-world grid design. As utilities plan inverter deployments, they can use the SA index to determine the minimum proportion of GFM capacity required to stabilize a grid with predominantly GFL resources. Validation through case studies confirms the theoretical findings, demonstrating that the method accurately predicts system behavior across different operating conditions.
For grid operators and renewable energy developers, this represents a significant step toward confident, data-driven decision-making. Instead of conservative over-deployment of GFM inverters or risky under-deployment, utilities can now calculate optimal hybrid configurations that balance stability requirements with economic constraints. As grids continue transitioning away from conventional generation, having reliable quantitative tools for stability assessment becomes essential to maintaining reliability and preventing cascading failures during disturbances.
