For the U.S. energy grid, nuclear and hydropower are vital components and key pathways to replacing carbon-intensive generation while maintaining reliability. In the U.S., nuclear power has been an important source of clean (carbon-free) energy, with 52% of clean energy coming from nuclear generation. Nuclear power accounted for 19.7% of total U.S. generation, and 36% of Pennsylvania’s in-state generation. Nuclear generation has traditionally provided the “base load” while other sources are tasked with “filling” the remaining demand curve. Similarly, hydropower (including pumped storage) is a source of clean energy, making up 19% of total U.S. clean generation, and is expected to become even more important as the U.S. integrates more variable generation sources like solar and wind into the grid. Maximizing the benefits and efficiency of nuclear generation, in conjunction with hydropower, can additionally lead to ecological and other co-benefits, including resiliency to extreme weather. Our objective is to use machine learning, 3D hydrodynamic modeling, sensitivity studies, data assimilation, and ensemble weather prediction to improve the forecasts that often guide decisions to critical energy grid operational decisions.
A team is improving weather and water forecasts that guide decisions to critical energy grid operations, specifically carbon-free methods such as nuclear energy and hydropower.