Now, anyone can easily explore and access data from a nationwide map of data centers, the infrastructure that powers them, and projections of future data center locations.
PNNL's E-COMP initiative is helping unleash American energy innovation with advanced theories, models, and software tools to better operate power systems that rely heavily on high-speed power electronic control.
The Generator Scorecard, developed by PNNL in partnership with BPA, automates generator evaluations, reducing engineering workloads and improving grid reliability.
New datasets delineating global urban land support scientific research, application, and policy, but they can produce different results when applied to the same problem making it difficult for researchers to decide which to use.
The demand for energy is growing—and so is the technology supporting it. However, future development of power generation technologies could be affected by a key factor: material supply.
PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.
PNNL helps deliver efficiency-related rules and requirements that steadily improve performance of America’s buildings, saving energy and costs and reducing carbon emissions.
Mandy Mahoney, director of the DOE Building Technologies Office, visited PNNL in late November. One key agenda item involved meeting with staff for a discussion of effective equity and justice integration in buildings-related research.
Leaders from the DOE Office of Energy Efficiency and Renewable Energy visited PNNL October 19–20 for a firsthand look at capabilities and research progress.
Variations in the level of market globalization can greatly affect the amount of water required to meet future global demand for agricultural commodities.
Climate change and socioeconomic pressures are transforming passenger and freight transportation in the Arctic, producing effects that have yet to be fully understood.
Testing the assumption that different future socio-economic development patterns, which result in different land-use changes, can be paired with different future climate outcomes for risk assessments in a multi-model framework.
Incorporating spatially explicit land characteristics in a global model illustrates the complex effects of applying uniform regional protection assumptions in a global analysis.
