AI-Enhanced Smart Grid Integration for Solar Energy: A Comprehensive Bibliometric Analysis

Solar energy is now a large part of the smart grid. Its variability makes forecasting and stability harder. Artificial intelligence (AI) and machine learning (ML) are widely used to manage these problems. This paper maps that literature. We analyzed 273 Scopus-indexed documents published between 2010 and 2024. VOSviewer and Biblioshiny were used for keyword co-occurrence, citation, bibliographic coupling, cocitation, co-authorship and thematic analysis. The results reveal an exponential growth in publications (28.64% annual growth rate), with China, India, and the United States as the leading contributors. Three clusters were found: smart grid and solar fundamentals, AI-based energy management, and deep learning for solar forecasting. Smart power grids and solar energy appear as basic themes. AI and energy management remain niche but well developed. Keyword trends show a shift from shallow neural networks to deep and reinforcement learning. The review outlines influential works, collaboration patterns and open gaps for future research in AI-enabled solar smart grid integration.