The increasing integration of renewable energy sources and inverter-based resources (IBRs) into modern power systems has triggered a paradigm shift in their operation and control. Given that most of these systems use grid-forming (GFM) converters as their interface, this technology has become essential for ensuring microgrid stability, particularly under low-inertia conditions. Among the key advantages of GFM technology is its ability to emulate the dynamic behavior of synchronous machines by regulating voltage and frequency, thereby providing a stable reference for other grid-following units.

This study conducts a sensitivity analysis of the tuning of control parameters within the control loops of GFM converters in a microgrid. The analysis focuses on the system’s response to dynamic events and disturbances (e.g., fault conditions), employing Monte Carlo simulation to assess the impact on system stability through continuous monitoring of voltage and frequency. The Monte Carlo approach generates a broad set of dynamic scenarios while systematically varying the controller parameters.

A time-domain simulation environment is developed based on a detailed microgrid model using DIgSILENT PowerFactory, with Python employed as an interface for automating the dynamic simulations and data processing. This framework allows for the introduction of uncertainty in controller parameter selection, particularly the proportional and integral gains (Kp, Ki), and their effects on system stability margins. Additionally, different scenarios are considered involving varying levels of renewable penetration, load profiles, and network topologies to identify critical operating conditions that amplify parameter sensitivity.

The results demonstrate that inadequate tuning can lead to inefficient system operation, particularly in the presence of disturbances, as well as in islanded microgrids or during transitions between grid-connected and islanded modes. On the other hand, proper or optimized tuning improves both transient and steady-state performance, reinforcing voltage and frequency stability even under significant renewable intermittency.

This study contributes to a deeper understanding of how GFM controller design parameters influence the dynamic behavior of power systems, and it offers practical guidelines for robust controller tuning. Such analyses are essential for the planning and reliable operation of future power systems characterized by high IBR penetration and minimal reliance on conventional synchronous generation.