Energy experts at PNNL are helping 56 states and territories deliver their state energy security plans (SESPs) through a transparent and collaborative review process.

PNNL’s pioneering CETC project with regional universities demonstrates transactive controls among multiple commercial buildings and devices for energy efficiency and grid reliability.

In January 2024, CESER—in partnership with GDO, NASEO, and PNNL—created a set of state energy security cohorts to support the coordination and technical development of state energy security planning, assessment, and mitigation.

PNNL is a leader in the integration of aberration-corrected electron microscopy, in-situ techniques, and atom probe tomography to address challenges in nuclear materials, environmental remediation, energy storage, and national security.

To help regulators identify a clear pathway to implement policy targets or mandates, PNNL researchers have provided a self-assessment tool.

PNNL and collaborators have established a national heat pump and heat pump water heater partnership to help drive adoption of these energy-saving technologies in both residential and commercial buildings.

Pacific Northwest National Laboratory’s (PNNL) Generative AI (GenAI) investment aims to harness this transformative technology to drive innovation across the science, energy, and security research domains.

The Grid Storage Launchpad (GSL) is a national capability for energy storage research funded by the Department of Energy Office of Electricity and located on the Pacific Northwest National Laboratory (PNNL) campus in Richland, Washington

By improving the Weather Research and Forecasting (WRF)-Solar model, this project aims to reduce forecast errors, improve sub-grid scale variability estimates, and more accurately estimate forecast uncertainty.

FEMP's operations and maintenance (O&M) resources offer federal agencies technology- and management-focused guidance to improve energy and water efficiency and ensure safer and more reliable operations.

Pacific Northwest National Laboratory has pioneered the use of observational research for evaluating energy efficient technologies in the built environment.

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.

Poorly insulated walls in residential buildings waste an estimated quadrillion+ Btus of energy per year. Upgrading windows and insulation during re-siding projects is a unique, cost-effective opportunity to improve efficiency and comfort.

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.

PNNL researchers developed and manage the online database Tethys to actively collects and curates information on the environmental effects of wind and marine energy.

The Triton Initiative was created to help the marine energy industry better understand the environmental impacts of marine energy devices.