Filtered by Advanced Hydrocarbon Conversion, Bioenergy Technologies, Chemical Physics, Data Analytics & Machine Learning, Electric Grid Modernization, and Stakeholder Engagement
Seventeen teams from regional colleges and universities gathered at PNNL Nov. 16 to put their cyber skills to the test by protecting critical energy infrastructure against simulated cyberattacks as part of DOE's CyberForce Competition.
In the third year of the DISCOVR Consortium project, the consortium team has identified an algal strain that progressed successfully through multiple evaluation phases.
Scientists at PNNL are bringing artificial intelligence into the quest to see whether computers can help humans sift through a sea of experimental data.
In today’s digital age, the rabbit hole of connected information can be not only a time sink, but downright overwhelming. Even for high-performance computers.
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.
A new Co-Optima report describes an assessment of 400 biofuel-derived samples and identifies the top ten candidates for blending with petroleum fuel to improve boosted spark ignition engine efficiency.
Twenty-four analysts from U.S. intelligence organizations met in August for a machine learning activity with PNNL researchers Nicole Nichols, Jeremiah Rounds, Lawrence Phillips, and Brian Kritzstein.
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.
Trouble on the electric grid might start with something relatively small: a downed power line, or a lightning strike at a substation. What happens next?
Pacific Northwest National Laboratory is leading efforts to address next-generation computing’s critical role in protecting the nation from cybersecurity threats.
Researchers at PNNL have developed a model that predicts outcomes from the algae hydrothermal liquefaction process in a way that mirrors commercial reality much more closely than previous analyses.
A new PNNL tool makes it easy to see the differences across the country when it comes to the cost and affordability of electricity. Users can sort and compare nearly 100 metrics or variables and get individual county information.
Researchers at PNNL are applying deep learning techniques to learn more about neutrinos, part of a worldwide network of researchers trying to understand one of the universe’s most elusive particles.