Zonal Power Grid Control for Low-Latency Autonomous Coordination

The patent segments the power grid into multiple autonomous zones, each with its own controller that can make local decisions independently. This segmentation reduces control latency by eliminating the need for all decisions to travel through a centralized controller, while maintaining coordination reliability through peer-to-peer communication and federated learning mechanisms that enable zones to cooperate on grid-wide objectives.

Each zonal controller in the patent is equipped with autonomous capabilities to monitor, analyze, and control its own zone without requiring centralized management of every node. The controllers use federated learning to collectively optimize grid performance while maintaining individual autonomy, and can independently handle renewable energy integration and load management, thereby reducing overall system complexity despite increased grid flexibility.

The patent implements federated learning where each zonal controller performs partial training iterations locally using its own data, and only exchanges model updates rather than raw data with other zones. This approach achieves high modeling accuracy through collective learning while significantly reducing computational energy consumption and communication overhead compared to centralized training of the entire grid model.