Abstract
The rapid rise of wind energy introduces persistent challenges related to variability, control, and operational reliability. While traditional machine learning supports forecasting and optimization, its scope is narrow and typically operator-dependent. We propose an agentic AI paradigm in which autonomous, goal-directed agents interact with turbines, the wind farm, and the grid to manage complexity in real time. A multi-agent architecture coordinates turbine-, farm-, and grid-level decisions to enhance efficiency and resilience. We present three concise case studies illustrating optimization at complementary scales: Case 1 (turbine-level) an AI agent adjusts upstream derating setpoints to mitigate wake effects and increase overall farm power production, at the cost of reduced power from the derated turbines; Case 2 (farm-level) a coordinating agent aligns multiple turbines to meet plant-wide energy and reliability objectives under operational and environmental constraints; Case 3 (grid-level) a system agent balances wind with other generators and consumer demand while honoring transmission limits and minimizing cost. Together, these cases show how local actions and global coordination increase energy yield, reduce structural loading, and improve reliability. Applications span adaptive forecasting, wake management, predictive maintenance, market participation, and cyber-physical security. We outline research needs in data quality, interoperability, safety, and regulation, and advocate hybrid designs that fuse reinforcement learning with digital twins to advance intelligent wind infrastructures.