PICS Colloquium: Powering decarbonization with modeling and optimization of renewables in the multi-scale atmosphere with Michael F. Howland

Abstract: Flow predictions in the stratified atmospheric boundary layer (ABL) are critical to a range of applications including energy meteorology, sustainability and resilience of the built environment, and wildfire management. Physical ABL processes such as boundary layer turbulence and surface-atmosphere interactions are parameterized in the weather and climate models that drive predictions used for decision-making across these applications, representing a primary source of error and uncertainty. To address these uncertainties in operational weather models, we develop a very efficient uncertainty quantification (UQ) methodology, leveraging stochastic optimization and machine learning to speed-up UQ by 1,000. This speed-up enables UQ of numerically expensive flow simulations and Bayesian experimental design for automated targeting of high-fidelity data acquisition (scale-resolving simulations and observations) for maximum uncertainty reduction in weather models. Meanwhile, to meet net-zero carbon emissions targets by mid-century, up to a ~30-fold increase in wind power capacity is required. Acceleration to this rate requires urgent improvements to the efficiency and reliability of wind power systems which are dictated by the flow physics of the stratified ABL.

But current engineering models driving wind power design and control rely on idealized theory that neglects or parameterizes key aspects of the stratified ABL, which are increasingly important for larger turbines and farms. Using large eddy simulations of wind turbines operating in a range of atmospheric conditions, we systematically uncover the significant roles of Coriolis effects and ABL stability on wake recovery, trajectory, and morphology. A new fast-running wind farm model that accounts for atmospheric Coriolis and stability effects on wakes is developed and validated against scale-resolving large eddy simulations of wind turbines in the stratified ABL. Going from the scale of a wind farm to the energy system, we leverage an integrated climate and energy system modeling framework to design minimum-cost decarbonized energy systems. Energy system optimization with high-resolution atmospheric predictions reveals opportunities for complementarity between spatiotemporal variations in wind and solar supply to align with energy demand and to lower the cost of decarbonized energy systems.

Bio: 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 recognized 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.