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US DOE planning September battery technology summit

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US DOE planning September battery technology summit

The U.S. Department of Energy has invited people across the electric utility, academic, venture capitalist and other sectors to a summit to develop new options for moving the agency's research on battery technologies to market, DOE Undersecretary for Science Paul Dabbar said June 15.

The goal of the summit in September will be "to connect people with what we're doing at the [national] labs ... to try to commercialize it, to try to push it out the door," Dabbar said at an event held in Washington, D.C., by the Center for Strategic and International Studies.

Electricity storage technologies such as batteries are often viewed as key to adding more renewable generation to the grid both at the utility scale and residential level without hurting system reliability. Under the Trump administration, the DOE has made grid resiliency and reliability a key focus. In addition to exploring options to rescue financially ailing coal and nuclear generation plants in the name of resiliency, the agency has made moves to advance cybersecurity and battery storage.

The DOE's national laboratories are working on advanced batteries that would have from three to five times the performance levels of lithium-ion technology, Dabbar said. "Batteries and storage have the ability to make major changes" in ways that could impact sustainable energy and the use of personal electronics, he said.

DOE has sent out about 400 invitations for the September gathering, dubbed an X-Lab Summit, to be held at Stanford University in California, Dabbar said.

Dabbar plans to hold at least one more such summit on other topics this year, including machine learning and artificial intelligence. DOE labs are already developing machine learning algorithms for such things as managing energy usage in buildings, oil pumps and grid dispatch, Dabbar said.

The DOE's Bonneville Power Administration, a federal power marketing administration in the Pacific Northwest, is already employing machine learning algorithms to optimize dispatch and grid management, Dabbar said.