Abstract

Microgrids (MGs) are emerging as pivotal solutions for addressing the growing global demand for reliable, decentralized energy systems, particularly given the variability of renewable energy sources (RESs) and integration challenges. This study critically reviews advancements in MG modeling, control dynamics, and optimization strategies, with a specific focus on dual-mode operations encompassing grid-connected and islanded systems. The primary objective is to identify key trends, challenges, and research gaps while proposing pathways to enhance MG performance and scalability. This research employs a systematic review methodology, analyzing 140 peer-reviewed studies to extract 81 insights on MG system stability, renewable integration, and emerging technologies such as artificial intelligence (AI) and blockchain. These studies were examined through comparative and contrastive evaluations, categorizing them based on MG control techniques, modeling tools, and technological innovations. The analysis focused on key performance metrics, stability solutions, and emerging trends, synthesizing findings to highlight advancements and research gaps. Key results indicate that 75% of reviewed studies emphasize control techniques for RES stability, while 90% utilize tools like MATLAB Simulink for MG modeling. Dual-mode systems demonstrate significant potential for enhanced energy reliability through advanced control strategies and hybrid RES configurations. Furthermore, the study underscores the need for standardization, scalability, and real-world validation in MG research while highlighting the implications of AI-based optimization, advanced energy storage, and smart grid integration. By addressing gaps in dual-mode operations and economic feasibility, this study lays a foundation for future research to develop robust, efficient, and adaptable MG systems, driving efforts toward resilient, decentralized energy infrastructure.

Key words: Microgrid Systems (MGs); Dual-Mode Operations; AI-Based Optimization; Blockchain in Energy; Scalability and Stability; Hybrid Renewable Systems