With a focus on prevention, PNNL researchers are working to predict droughts, enhance grid resilience, and manage forests to reduce wildfire.

The U.S. Department of Energy has selected the Scalable Predictive Methods for Excitations and Correlated Phenomena project to receive funding to develop software for chemical research.

Research into practical systems for chemical energy storage will be a focus at the Energy Sciences Center.

Bojana Ginovska leads a physical biosciences research team headed for PNNL's new Energy Sciences Center. She uses the transformative power of molecular catalysis and enzymes to explore scientific principles.

New research aids the design of more efficient water purification technologies.

Alison Colotelo, senior research scientist and project manager, has been selected for the Hydropower Foundation advisory board.

PNNL is developing the Digital Twins for Hydropower framework to help the industry to affordably modernize the aging hydropower fleet.

Marcel Baer is a computational scientist working in PNNL’s Physical Sciences Division with a prominent effort in materials science and physical bioscience.

PNNL and four other national laboratories executed the Hydropower Value Study to examine hydropower operations in different regions of the United States.

With quantum chemistry, researchers led by PNNL computational scientist Simone Raugei are discovering how enzymes such as nitrogenase serve as natural catalysts that efficiently break apart molecular bonds to control energy and matter.

Study says planners need to account for climate impacts on renewable energy during capacity development planning to fully understand investment implications to the power sector.

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

PNNL has published a report that sets the foundation for modeling gaps and technical challenges in optimizing hydropower operations for both energy production and water management.

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.

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.

PNNL has earned “Best Paper” at an international resilience conference for research on hydropower’s capabilities and constraints in the event of extreme events, like hurricanes and rolling blackouts.

A PNNL research team compared two types of pumped storage hydropower (open and closed loop) to identify potential environmental impacts.

Pumped-storage hydropower offers the most cost-effective storage option for shifting large volumes of energy. A PNNL-led team wrote a report comparing cost and performance factors for 10 storage technologies.

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.