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.…

• EC
  European Commission

  Award: CZ.10.03.01/00/22_003/0000048
• NN
  National Natural Science Foundation of China

  Award: 52207117
• EU
  European Union Agency for Network and Information Security