The Need: Extreme weather events pose an enormous and increasing threat to the nation’s electric power systems and the associated socio-economic systems that depend on reliable delivery of electric power. While utilities have software tools available to help plan their daily and future operations, these tools do not include capabilities to help them plan for and recover from extreme events. Software for resilient design and recovery is not available commercially and research efforts in this area are preliminary.
Objective. This project will develop and deploy a suite of novel software tools to anticipate, absorb, and recover from grid events by demonstrating predictive analytics capabilities, combining state-of-the-art artificial intelligence and machine learning techniques, and controlling distributed energy resources (DERs). The tools will be integrated into an extensible and open platform.
Planned Outcome: The innovations in the project include using predictive analytics, image recognition, increased “learning” and “problem solving” capabilities for the anticipation of grid events. The project team will demonstrate distributed control theory with and without communications to absorb and recover from grid events.
The Need: Extreme weather events pose an enormous and increasing threat to the n