Physics-informed machine learning (PIML) is a modeling approach that harnesses the power of machine learning and big data to improve the understanding of coupled, dynamic systems.

PNNL is working on behalf of the U.S. Department of Energy to create a prototype system that enables homes to help provide services to the power grid while delivering economic benefits to residents.

Navigating the challenges of connecting hydropower to the electrical grid

This 18-month study will analyze how the region can meet its needs for reliable, resilient, and affordable energy along with decarbonization goals and other energy policies and priorities.

Cataloging and quantifying the economic value of non-traditional value streams in conversions of non-powered dams to hydroelectric facilities

PNNL water quality research and tools help inform hydropower decisions and operations.