Artificial Intelligence for Smart Renewable Energy Grids: A Structured Literature Review and Integrated Framework

Abstract

Renewable energy is being added to the grid faster than the grid itself was ever built to handle. Solar and wind depend on the weather, so their output goes up and down in ways that traditional, mostly one-directional grid infrastructure was not designed to absorb. That mismatch can create voltage instability, energy imbalance, and inefficient dispatch [1], [2]. This paper is a structured literature review of how Artificial Intelligence (AI), including classical machine learning, deep learning, graph neural networks, physics-informed learning, explainable AI, and reinforcement learning, can be built into smart grid systems to deal with these problems. Using twenty-three peer-reviewed and technical-report sources found through a systematic search process, this review pulls together forecasting models for renewable generation and demand, topology-aware and physically-constrained state estimation, explainability methods that help build control-room trust, and reinforcement-learning controllers used for battery scheduling and microgrid dispatch. From there, the paper proposes an integrated AI-powered smart grid framework, walks through a real-world case study of duck-curve management at the California Independent System Operator (CAISO), and discusses the trade-offs, deployment challenges, and open research questions tied to each method. The evidence shows that AI-based forecasting and dispatch improve renewable utilization and supply-demand balance compared to conventional rule-based control, though results are context-specific. Cybersecurity, model transferability, data privacy, and computational scalability are still the main things standing in the way of wider deployment.