<p>As decentralized energy systems become pivotal for global decarbonization, Microgrid Energy Management Systems (EMS) are increasingly adopting Artificial Intelligence (AI) to cope with renewable intermittency, load uncertainty, and multi-objective operational constraints. While deep learning has substantially improved predictive capabilities and control performance, it has introduced two critical externalities: the limited transparency of “black-box” models and escalating computational energy costs. This study conducts a systematic bibliometric analysis of 2,150 records from the Scopus database (2011–2025), using VOSviewer and the Bibliometrix R package to map the convergence and divergence of Explainable AI (XAI) and Frugal AI within the microgrid domain. Our findings reveal a significant dichotomy: while research into interpretability is experiencing exponential growth due to regulatory and operational trust requirements, “frugality” (computational efficiency) remains a peripheral concern, rarely prioritized in model design despite the resource constraints of edge deployment. We argue that the current literature overlooks the environmental footprint of the algorithms themselves. To the best of our knowledge, this is the first bibliometric study that jointly examines frugality and explainability in AI for microgrid energy management, rather than treating them as separate lines of inquiry. Consequently, we propose a consolidated research agenda focusing on hybrid architectures that reconcile high-performance forecasting with lightweight, transparent design, essential for the sustainable deployment of intelligent microgrids. Beyond mapping the field, the proposed agenda is intended to guide researchers and practitioners toward deployable EMS solutions that respect both hardware constraints and emerging requirements for algorithmic transparency.</p>