Enhancing Smart Grid Stability Using AI Techniques: A Systematic Literature Review

The modern smart grid leverages advanced technologies to enhance electricity distribution's efficiency, reliability, and sustainability, enabling two-way energy and data exchange. This systematic literature review (SLR) explores how artificial intelligence (AI) addresses the challenge of smart grid stability. From 1,181 records identified via Scopus and Web of Science, 41 studies were analyzed in-depth. The SLR evaluates AI techniques such as machine learning, deep learning, neural networks, hierarchical clustering, and explainable AI across functions like fault detection, stability analysis, energy forecasting, and demand response. Findings reveal AI's role in enhancing grid stability through predictive capabilities and adaptive controls. This study contributes a taxonomy of AI applications, identifies research gaps, and outlines a future research direction. Key challenges include scalability, data privacy, and model interpretability, while opportunities lie in hybrid models, and integration with blockchain and IoT. The study provides actionable insights for researchers, policymakers, and stakeholders.