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

PNNL’s ESMI is a Laboratory-funded research and development (R&D) program focused on transforming and accelerating materials development processes for next-generation energy storage technologies.

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.

UTEP and PNNL are advancing the collective scientific impact of both institutions through collaborations between PNNL researchers and UTEP faculty, as well as by building on the complementary strengths to grow a diverse STEM workforce.