A Systematic Review and Evolutionary Analysis of Optimization Techniques and Software Tools in Hybrid Microgrid Systems

This study presents a systematic review and analysis of optimization techniques (OT) and software tools (ST) in Hybrid Microgrid Systems (HMGS). An advanced SCOPUS search was conducted using core keywords, including microgrids, renewable energy systems, and various OT and ST. The review analyzes 4,134 documents on OT, categorizing them into classical (16.87%), metaheuristic (47.12%), and artificial intelligence (AI)-based methods (36.01%). Metaheuristic techniques dominate the field, reflecting their adaptability and effectiveness, while AI-based methods are rapidly gaining prominence for addressing complex optimization challenges, including operational uncertainties, cost efficiency, and energy reliability. Additionally, 2,667 documents on ST reveal MATLAB/Simulink as the most widely used, accounting for 65.34% of the total, followed by HOMER at 22.08%. These tools are pivotal in enabling techno-economic analysis, system design, and optimization under diverse scenarios. By highlighting trends, leading contributors, and knowledge gaps, this study provides a comprehensive resource to guide innovation in HMGS, fostering sustainable energy integration and addressing global energy challenges.