This study systematically reviews the optimization techniques (OTs) and software tools (STs) in hybrid microgrid systems (HMGSs) to enhance the efficiency, cost-effectiveness, 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.

Hybrid energy systems (HESs) integrate renewable sources, storage, and optionally conventional energies, offering a sustainable alternative to fossil fuels. Microgrids (MGs) bolster this integration, enhancing energy management, resilience, and reliability across different levels. This study, emphasizing the need for refined optimization methods, investigates three themes: renewable energy, microgrid, and multiobjective optimization (MOO), through a bibliometric analysis of 470 Scopus documents from 2010 to 2023, analyzed using SciMAT v1.1.04 software. It segments the research into two periods, 2010–2019 and 2020–2023, revealing a surge in MOO focus, particularly in the latter period, with a 35% increase in MOO-related research. This indicates a shift toward comprehensive energy ecosystem management that balances environmental, technical, and economic elements. The initial focus on MOO, genetic algorithms, and energy management systems has expanded to include smart grids and electric power systems, with MOO remaining a primary theme in the second period. The increased application of artificial intelligence (AI) in optimizing HMGS within the MOO framework signals a move toward more sustainable, intelligent energy solutions. Despite progress, challenges remain, including high battery costs, the need for reliable MOO data, the intermittency of renewable energy sources, and HMGS network scalability issues, highlighting directions for future research.

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.

Hybrid Renewable Energy Systems (HRES) integrate renewable sources, storage, and optionally conventional energies, offering an eco-friendly solution to fossil fuels. Microgrids (MGs) bolster HRES integration, enhancing energy management, resilience, and reliability at various levels. This study, emphasizing the need for refined optimization methods, investigates three themes: renewable energy, microgrid, and multiobjective optimization (MOO), through a bibliometric analysis of 470 Scopus documents from 2010-2023, analyzed with SciMAT software. It segments the research into two periods, 2010-2019 and 2020-2023, revealing a surge in MOO focus, especially in the latter period, with a 35% increase in MOO-related research. This indicates a shift towards com-prehensive energy ecosystem management that balances environmental, technical, and economic elements. The initial focus on MOO, genetic algorithms, and energy management systems has expanded to include smart grids and electric power systems, with MOO remaining a primary theme in the second period. The increased application of Artificial Intelligence (AI) in optimizing HMGS within the MOO framework signals a move towards more sustainable, intelligent energy solutions. Despite progress, challenges remain, including high battery costs, the need for reliable MOO data, the intermittency of renewable energy sources, and HMGS network scalability issues, highlighting directions for future research.