Renewable microgrid optimization using AI: A B-SLR approach and future research directions

This study advances the discourse on artificial intelligence (AI) in renewable microgrid optimization by providing a business-centric and critically integrative review rather than a purely technical summary. Existing literature remains fragmented across engineering and policy domains, often overlooking the economic and governance dimensions shaping AI adoption. Employing a Bibliometric–Systematic Literature Review (B-SLR) of 59 peer-reviewed studies published between 2014 and 2025; this paper combines bibliometric mapping with thematic synthesis to assess not only how AI optimizes microgrids but also why institutional and contextual factors influence its real-world effectiveness. reveal a clear shift from deterministic optimization models to hybrid, multi-objective frameworks integrating machine learning, evolutionary algorithms, and game-theoretic approaches. Yet, these advances remain constrained by limited field validation, weak scalability, and insufficient attention to explainability, ethics, and regulatory adaptation, particularly in low- and middle-income contexts. The review contributes an integrated conceptual framework linking AI technique, operational performance, business value, and sustainability outcomes, and proposes a forward-looking agenda emphasizing explainable AI, federated learning, edge computing, and participatory governance. By reframing AI-enabled microgrids as strategic instruments of sustainable modernization, the study highlights how technological innovation, economic feasibility, and ethical governance must converge to achieve inclusive and resilient energy transitions.