The goal of this project is to respond to grid events by:
Demonstrating machine learning and artificial intelligence from different data sources to anticipate grid events;
Validating controls for distributed energy resources for absorbing grid events; and
Reducing recovery time by managing distributed energy resources in the case of limited communications.
The project builds on previous efforts to collect massive amounts of data and use it to fine-tune grid operations, including SLAC’s VADER project as well as other Grid Modernization Lab Consortium projects on distributed controls and cyber security.
The innovations in the project include application of artificial intelligence and machine learning for distribution grid resilience. Particularly using predictive analytics, image recognition, increased “learning” and “problem solving” capabilities for anticipation of grid events. The project team will demonstrate distributed control theory with and without communications to absorb and recover from grid events.
This three-year project will deploy, test and validate developing capabilities around the country. Anticipation analytics will be tested and validated with Southern California Edison; absorption algorithms will be tested in Vermont; and extremum seeking controls developed by LBNL will be tested with one of NRECA members.