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

Abstract

This study systematically reviews the optimization techniques (OTs) and software tools (STs) in hybrid microgrid systems (HMGSs) to enhance the efficiency, costeffectiveness, and energy reliability. An advanced Scopus search was conducted using core keywords related to microgrids, renewable energy systems, and various OTs and STs, which identified 4134 relevant documents on OTs. These were classified into classical (16.87%), metaheuristic (47.12%), and artificial intelligence (AI)-based methods (36.01%), highlighting the dominance of metaheuristics and the growing role of AI-driven approaches in handling uncertainties and real-time decision-making. Additionally, 2667 documents on STs were analyzed, identifying MATLAB/Simulink (65.34%) and HOMER (22.08%) as the most widely used tools for simulation, modeling, and techno-economic analysis. This study identifies key research trends, highlights gaps in the optimization strategies, and emphasizes the need for AI integration, broader adoption of open-source tools, and scalable optimization frameworks. By mapping the evolution and effectiveness of OTs and STs, it provides valuable insights for researchers, policymakers, and industry professionals, supporting the development of sustainable and intelligent HMGS solutions.