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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.