Research that modeled increased heat pump adoption alongside climate change impacts in Texas showed that high-efficiency heat pumps buffer the strain that electric heating might put on the power grid.
PNNL researchers are exploring the kinds of flicker waveforms that the eye and brain can detect, seeking to understand the different visual and non-visual effects that result.
PNNL has developed a decision tool that provides contractors and installers with the information they need to properly select and install cold climate heat pumps, which are a key technology for achieving decarbonization.
Erich Hsieh, Deputy Assistant Secretary for OE’s Energy Storage Division, shared insights about the Grid Storage Launchpad and energy storage innovations .
The study found that the way a fire burns (in open air versus in an oven in a controlled lab setting) can greatly change the leftover materials (char or charcoal) and how they interact in the environment.
PNNL and collaborators developed new models—recently approved by the U.S. Western Electricity Coordinating Council (WECC)—to help utilities understand how new grid-forming inverter technology will enhance grid stability.
At the second Grid Resilience to Extreme Events Summit, a diverse range of experts gathered to tackle the biggest challenges in building a resilient grid.
Tennessee State University received Department of Energy funding to establish an academy focused on preparing students and professionals to work in an emerging field: clean energy systems. PNNL is helping with that effort and others.
Researchers at PNNL devised a new workflow for integrating life cycle analysis into building design, aided by a computational method that adapts existing software to net-zero energy, carbon-negative homes.
GUV can reduce transmission of airborne disease while reducing energy use and carbon emissions. But fulfilling that promise depends on having accurate and verifiable performance data.
A new study uses direct numerical simulations to develop a near-surface turbulence model for thermal convection using interpretable and physics-aware neural networks, broadening the applications of numerical simulations.