Project will use AI to prevent or minimize electric grid failures RSS Feed

Project will use AI to prevent or minimize electric grid failures

A project led by the Department of Energy’s SLAC National Accelerator Laboratory will combine artificial intelligence with massive amounts of data and industry experience from a dozen U.S. partners to identify places where the electric grid is vulnerable to disruption, reinforce those spots in advance and recover faster when failures do occur.

The eventual goal is an autonomous grid that seamlessly absorbs routine power fluctuations from clean energy sources like solar and wind and quickly responds to disruptive events—from major storms to eclipse-induced dips in solar power—with minimal intervention from humans.

“This project will be the first of its kind to use artificial intelligence and machine learning to improve the resilience of the grid,” said Sila Kiliccote, director of SLAC’s Grid Integration, Systems and Mobility lab, GISMo, and principal investigator for the project. “While the approach will be tested on a large scale in California, Vermont and the Midwest, we expect it to have national impact, and all the tools we develop will be made available either commercially or as open source code.”

Called GRIP, for Grid Resilience and Intelligence Project, the project builds on other efforts to collect massive amounts of data and use it to fine-tune grid operations, including SLAC’s VADER project. It’s one of seven Grid Modernization Laboratory Consortium projects aimed at boosting grid resilience that will receive up to $32 million in funding as part of the DOE’s Grid Modernization Initiative. GRIP was awarded up to $6 million over three years.

The project will use both machine learning, where computers ingest large amounts of data and teach themselves how a system behaves, and artificial intelligence, which uses the knowledge the machines have acquired to solve problems.

SLAC’s GISMo lab, which works with Stanford University, utilities and other industry partners on smart grid technology, will develop machine learning algorithms that digest data from satellite imagery, utility operations and other sources and build knowledge about how electrical distribution systems work.

“One of the first places we will test our data analytics platform is at a major California utility,” Kiliccote said. “The idea is to populate the platform with information about what your particular part of the grid looks like, in terms of things like solar and wind power sources, batteries where energy is stored, and how it’s laid out to distribute power to homes and businesses. Then you begin to look for anomalies – things that could be configured better.”
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For instance, she said, a grid can be divided into “islands,” or microgrids, that can be isolated to prevent a power disruption from spreading and taking the whole system down.