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

February 14 at 2:00 PM - 3:00 PM

To meet net-zero carbon emissions targets by mid-century, up to a -fold increase in wind power capacity is required. Acceleration to this rate requires urgent improvements to efficiency and reliability of installed wind farms, as well as cost reductions for future offshore farms. To expand energy production, wind turbines are rapidly increasing in size, wind farms are proliferating to new locations and are increasing in size and siting density, and novel wind farm design and control methods are increasingly deployed. But current engineering models driving wind power design and control rely on idealized theory that neglects key aspects of the rotor aerodynamics and the turbulent atmospheric boundary layer, which are increasingly important for larger turbines and farms. We revisit the first-principles of mass, momentum, and energy conservation to develop a Unified Momentum theory for rotors across operating regimes, accounting for arbitrary misalignments between rotor and inflow and thrust coefficients. The model is validated against large eddy simulations and generalizes and replaces both classical momentum theory and the Betz limit. Going from the scale of a turbine to a farm, wake losses
can reduce farm energy by 30%, a significant loss that negatively impacts economics and is increasing given wind power expansion. Using large eddy simulations of wind turbines operating in a range of atmospheric conditions, we systematically uncover the significant roles of Coriolis effects and stability on wake recovery, trajectory, and morphology. A new fast-running wind farm model that accounts for the coupled rotor operational and atmospheric effects on wakes is developed. The wind farm model is
leveraged for applications including collective control and for control co-design, applied in both simulations and utility-scale field experiments. Collective control can increase the energy generation of wind farms through software modifications, without additional turbines or hardware. 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.

Michael F. Howland

Professor of Civil and Environmental Engineering at MIT

Michael F. Howland is the Esther and Harold E. Edgerton 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.

Details

Date:
February 14
Time:
2:00 PM - 3:00 PM
Event Category:

Organizer

Penn Institute for Computational Science (PICS)
Phone
215-573-6037
Email
dkparks@seas.upenn.edu
View Organizer Website

Venue

PICS Conference Room 534 – A Wing , 5th Floor
3401 Walnut Street
Philadelphia, PA 19104 United States
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