Grid Forming Converter Control for Stability Enhancement in Renewable Dominated Power Systems using Model Predictive Control

This paper presents a Model Predictive Control based grid forming converter strategy for enhancing frequency and voltage stability in renewable dominated power systems with low inertia. High penetration of converter interfaced renewable sources reduces natural system inertia and weakens grid damping, making conventional grid following and droop-based grid forming controls less effective under fast disturbances. To address this limitation, a predictive control framework is developed using a discrete time dynamic model of converter frequency and voltage behavior. The controller forecasts system response over a finite horizon and computes an optimal control sequence that minimizes frequency deviation, voltage error, and control effort under varying renewable generation and load conditions. The proposed method integrates virtual inertia response, damping support, and voltage regulation within a unified optimization structure and explicitly incorporates renewable load mismatch into the prediction model for anticipative disturbance rejection. Time domain simulations are carried out under step load changes and renewable variability scenarios. Performance is evaluated using frequency nadir, maximum deviation, rate of change of frequency, settling time, voltage deviation, control power peak, stability energy index, and load tracking mean square error. Results show tight frequency regulation, low RoCoF, minimal voltage variation, and fast settling with low control stress. The study demonstrates that predictive grid forming control provides robust and efficient stability support for next generation inverter dominated grids.