Connected moments math shortcut shaves time and cost of quantum calculations while maintaining accuracy.

The project received an Innovative and Novel Computational Impact on Theory and Experiment (INCITE) award, a highly competitive U.S. Department of Energy Office of Science program.

PNNL is one of the collaborating partners on a new grid-scale solar and energy storage installation near the PNNL campus in a project led by Energy Northwest.

Pacific Northwest National Laboratory researchers used machine learning to explore the largest water clusters database, identifying—with the most accurate neural network—important information about this life-essential molecule.

Making sure there’s enough electricity at the lowest price is a critical endeavor undertaken daily by electricity market operators. Now, there’s an approach that provides more timely and accurate information to make day-ahead decisions.

With support from DOE’s Office of Electricity and National Grid, PNNL led a groundbreaking study to accurately assess the full value of grid energy storage investments across a wide variety of use cases.

Energy storage is slowly shifting utility planning practices from the current paradigm, which ensures grid reliability by building reserve generation resources, to ensuring grid reliability by optimizing grid services.

Researchers at PNNL used key metrics to develop visualizations that show how the combined effects of climate change on hydropower and load influence the frequency, duration, and severity of power shortfalls.