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Michael Howland
Jeffrey Cheah Career Development Assistant Professor, Civil and Environmental Engineering, Massachusetts Institute of Technology
Speaker Biography:
Michael F. Howland is the Jeffrey Cheah Career Development Assistant Professor of Civil and Environmental Engineering at MIT. He was a Postdoctoral Scholar at Caltech in the Department of Aerospace Engineering. He received his B.S. from Johns Hopkins University and his M.S. from Stanford University. He received his Ph.D. from Stanford University in the Department of Mechanical Engineering. His work is focused at the intersection of fluid mechanics, weather and climate modeling, uncertainty quantification, and optimization and control with an emphasis on renewable energy systems. He uses synergistic approaches including simulations, laboratory and field experiments, and modeling to understand the operation of renewable energy systems, with the goal of improving the efficiency, predictability, and reliability of low-carbon energy generation. He was the recipient of the Robert George Gerstmyer Award, the Creel Family Teaching Award, and the James F. Bell Award from Johns Hopkins University. At Stanford, he received the Tau Beta Pi scholarship, NSF Graduate Research Fellowship, a Stanford Graduate Fellowship, and was awarded as a Precourt Energy Institute Distinguished Student Lecturer. At MIT, he has received the Maseeh Excellence in Teaching Award and the Office of Naval Research (ONR) Young Investigator Program (YIP) award.
Abstract:
To meet net-zero carbon emissions targets by mid-century, an order of magnitude increase in renewable power capacity is required. Yet it is not clear where wind and solar generation should be sited to maximally support resilient power systems with low cost. To guide energy system planning and operation, wind and solar resources are estimated using uncertain numerical models of the multi-scale Earth system. Planning for decarbonization, therefore, depends on the quality of meteorological data that is used, including resolution, accuracy, and uncertainty. We design minimum-cost decarbonized energy systems in three diverse geographic and grid regions (ISO-NE, CAISO, ERCOT) and we interpret these kilometer-scale energy system designs based on their geophysical drivers for generalizable insights. The supply of wind and solar power generation relies on spatiotemporal variations and correlations within and across the wind and solar resources. Using downscaled meteorological data at km-scale yields lower cost compared with typical meteorological data at resolutions over 30 km (i.e. standard reanalysis) by revealing opportunities for complementarity between spatiotemporal variations in wind and solar supply to align with demand. Further, current renewable energy tax incentives in the United States reward total energy production – this study suggests that wind and solar complementarity can lead to a more cost-effective energy system design. The wind and solar siting locations that minimize the energy system cost differ significantly from the locations with the highest wind/solar resource potential on average. Moving forward, critical uncertainties in future climate conditions and their impacts on renewable supply and energy demand will pose risks to the resilience of future energy systems if they are not addressed in planning. We leverage gradient-free optimization and machine learning for a thousand-fold speed-up in the quantification of uncertainty in meteorological models. This acceleration enables energy system optimization under uncertainty to uncover practical strategies for renewable siting to minimize risk to climate change and extreme events. Climate change affects the resilience of resource adequacy in decarbonized energy systems and transforms the optimal locations for renewable deployment because of compounding impacts on renewable power supply and demand, as revealed by integrated climate and energy models.