Artificial Intelligence and Metaheuristic Optimization Strategies for Renewable Microgrid Sizing and Design: A Scoping Review

Optimal sizing and design of renewable microgrids and hybrid renewable energy systems require balancing renewable resource variability, demand uncertainty, storage operation, reliability, and techno-economic constraints. Artificial intelligence and metaheuristic optimization strategies have been increasingly used to address these challenges; however, the evidence remains methodologically heterogeneous. This scoping review maps the literature on artificial intelligence, learning-based, metaheuristic, heuristic, and optimization-based strategies for renewable microgrid sizing and design. The review followed PRISMA-ScR guidelines. Searches were conducted in Scopus and the Web of Science Core Collection for research articles published between 2009 and March 2026. A total of 69 studies were included. Metaheuristics dominated the field, appearing in 63 studies, with particle swarm optimization and genetic algorithm-based strategies as the most frequent methodological families. Artificial intelligence and learning-based strategies were mainly used to support forecasting, surrogate modeling, uncertainty handling, and energy management. The most recurrent configurations involved photovoltaic, wind, and battery storage systems, often with diesel backup in stand-alone or off-grid contexts. The literature is strongly oriented toward metaheuristic sizing of PV–wind–battery microgrids, with emerging integration of AI-assisted prediction and decision-support strategies. Future studies should address reproducibility, uncertainty modeling, real-world validation, degradation assessment, explainability, and scalability.