Neural Network-Based Droop Optimisation

The modern multi-microgrid (MMG) systems with high renewable penetration are subject to voltage instability, inefficiency in power sharing, and long transient recovery, which are drawbacks that the conventional fixed droop controllers cannot tackle. In this context, research presents the AI-Enhanced Adaptive Droop Control (AI-ADC) framework that combines rule-based tuning with a neural-network model that can predict the optimal droop coefficients in real time. The controller is validated through a high-fidelity MATLAB/Simulink model incorporating the dynamics of PV, wind, BESS, and a converter, and subjected to load steps, irradiance fall-off, DER failure, and communication delay situations. The controller's performance with respect to voltage undershoot has been better by over 60%, the voltage steady-state deviation has been under 0.25 V, the power-sharing error has been reduced to below 3%, and the settling time has been almost four times faster than that of the conventional methods. The AI-ADC, by allowing predictive and adaptive droop adjustments, presents a scalable, efficient, and highly resilient control solution for the next-generation MMG networks dominated by renewable energy sources.