AI-driven control and optimization for renewable energy integration in smart grids: Challenges, applications, and future research directions

The transition towards worldwide RES suffers from intrinsic intermittency and variability, which further raises concerns about grid stability, efficiency, and reliability. In turn, AI, ML, and DL have become essential tools. The review comprehensively outlines the key applications of these techniques, including high-accuracy forecasting, adaptive control, smart demand response, and predictive fault detection. The review also goes into their roles in optimizing energy storage, developing digital twins, and enhancing cybersecurity. The discussion extends to multi-objective optimization frameworks that balance cost, resilience, and sustainability, and includes a life cycle assessment of these AI-driven solutions. Quantum computing, reinforcement learning, neuromorphic systems, and hydrogen economies enabled by AI are pointed out as promising frontiers. The main technical, data, and regulatory challenges, along with potential solutions, are identified. It is deduced that AI may play a crucial role in creating intelligent, resilient, and sustainable power systems.