AI Methods and Deployment Challenges from Prediction to Trusted Operation in Renewable-Rich Smart Grids: A Review
Keywords:
Artificial Intelligence, Explainable AI, Reinforcement Learning, Renewable Energy Integration, Smart GridAbstract
The inclusion of renewable energy sources, distributed energy resources, and electric vehicles in the power grid has transformed conventional power grids into complex, multi-directional cyber-physical systems, for which traditional model-based tools are inadequate and/or linear. Artificial Intelligence (AI) and Machine Learning (ML) have therefore become key enablers of next-generation smart grids, with AI-driven data-based solutions helping forecast energy loads and renewable generation, diagnose faults and protect against them, optimise microgrids and electric-vehicle charging, and support cybersecurity and explainable decision-making. This work summarizes the current AI/ML applications for smart grid optimization and renewable-energy integration, based mainly on literature published from 2024 to 2026, including peer-reviewed studies and recent preprints. It presents recent breakthroughs in deep learning-based forecasting, reinforcement learning-based microgrid energy management, and digital-twin-based grid monitoring and AI-based cyber defense, and places these advances in the context of the fast-digitalizing power system in India as an illustrative example from emerging economies. There are persistent data quality issues, model interpretability concerns, real-time scalability issues, and AI-specific security concerns, which are identified, and directions for future research are proposed. The review aims to serve as a single reference to help researchers, utility engineers, and policymakers design "smart" power systems that are able to be powered by renewables.
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