The talk will feature Dr. Jasmine Garland from CU Boulder. Dr. Garland develops predictive machine learning frameworks to help energy infrastructure withstand extreme weather, using advanced modeling techniques to improve long-term grid planning and resource adequacy. Her research integrates high-resolution climate data into capacity expansion models, ensuring the power grid can meet demand even during severe climate shocks. She has also conducted research residencies at the National Laboratory of the Rockies and ETH Zürich's Power Systems Laboratory, focused on climate impacts and energy equity.
Bio
Dr. Jasmine Garland is a postdoctoral researcher at the University of Colorado Boulder and a research intern at the Pacific Northwest National Laboratory, specializing in weather-driven power systems operations. Supported by the NSF GRFP and GAANN fellowships, she recently earned her Ph.D. from the University of Colorado Boulder, bridging climate data science and grid optimization.
Dr. Garland develops predictive machine learning frameworks to adapt energy infrastructure to extreme weather. Her research improves grid planning by integrating high-resolution climate data into capacity expansion models that determine long-term infrastructure investments and resource adequacy assessments, ensuring the power grid maintains sufficient supply to withstand severe climate shocks. To model these systems accurately, she moves beyond standard linear approximations by using AC Optimal Power Flow, advanced mathematical formulations that capture the complex physical limitations of grid operations. Her research trajectory also includes residencies at the National Laboratory of the Rockies and the Power Systems Laboratory at ETH Zürich, where she investigated climatic impacts on power systems and energy equity.