Diverse Control Strategies for Frequency Regulation in Islanded Microgrids: A Systematic Comparison

Islanded microgrids are emerging as a cornerstone of modern decentralized power systems, particularly in remote and renewable-rich areas. However, maintaining frequency stability remains a critical challenge due to fluctuating generation, variable load demand, and reduced system inertia. This paper presents a mathematical modelling for analyzing the controllability of system and a systematic comparison of diverse control methods for regulating the frequency in islanded microgrids, encompassing conventional approaches like droop and PID control, advance methods like Model Predictive Control (MPC) and robust control, as well as intelligent techniques including fuzzy logic, neural networks, and deep reinforcement learning. Special emphasis is placed on the inclusion of Hybrid Energy Storage Systems (HESS), combining battery energy storage systems and supercapacitors, to enhance system flexibility and dynamic performance. The study evaluates each control method based on response time, disturbance rejection, implementation complexity, suitability and its mathematical model for HESS-enabled microgrids. The paper concludes by highlighting the superior potential of AI-based control strategies in effectively managing frequency deviations when integrated with HESS, paving the way for resilient and intelligent microgrid operations.