Comparative Analysis of Microgrid Control Techniques using Variance-Based Methods

Abstract

This paper presents a rigorous comparative evaluation of three advanced microgrid control strategies, "droop control", "model predictive control", and "fuzzy logic control", under varying renewable penetration levels and grid conditions. A detailed microgrid model incorporating energy storage and realistic load profiles is developed, and key performance metrics, including frequency deviation, voltage stability, energy cost, and loss of load probability, are analyzed. A two-way analysis of variance framework is employed to ensure statistical significance of the results. The findings indicate that Model Predictive Control achieves the best overall performance, particularly in terms of stability and cost efficiency, while Fuzzy Logic Control demonstrates strong robustness under uncertain conditions. Droop control, although simple and reliable, shows comparatively lower performance. Additionally, a regression model is proposed to predict energy cost based on control strategy, renewable integration, and storage capacity, providing practical insights for optimal microgrid operation.