Michael Howland, PhD | California Institute of Technology (Caltech)
TITLE"From turbine to wind farm: aerodynamic wind energy modeling and collective power optimization"
ABSTRACTThe study of wind farms within the atmospheric boundary layer is critical to understand the governing physics of the system, develop physics-based models, and to design optimal operational protocols. Historically, control protocols have optimized the performance of individual wind turbines resulting in aerodynamic wakes which typically reduce total wind farm power production 10-20% and increase the cost of electricity for this resource. Considering the wind farm as a collective, we designed a physics- and data-driven wake steering control method to increase the power production of wind farms. The method was tested in a multi-turbine array at a utility-scale operational wind farm, where it statistically significantly increased the power production over standard operation. The analytic gradient-based wind farm power optimization methodology we developed can optimize the yaw misalignment angles for large wind farms on the order of seconds, enabling online real-time control. In order to rapidly design and improve dynamic closed-loop wind farm controllers, we developed wind farm large eddy simulation capabilities that incorporate Coriolis and stratification effects. Dynamic wake steering controllers are tested in simulations and, altogether, the results indicate that closed-loop wake steering control can significantly increase wind farm power production over greedy operation provided that site-specific wind farm data is assimilated into the optimal control model.
BIOMichael F. Howland is a Postdoctoral Scholar at the California Institute of Technology in the Department of Aerospace. He earned his B.S. from Johns Hopkins University and his M.S. from Stanford University, both in Mechanical Engineering. He received his Ph.D. in Mechanical Engineering from Stanford University, where he was the recipient of the NSF and Stanford graduate fellowships. His research focuses on the flow physics of Earth’s atmosphere and the modeling, optimization, and control of renewable energy generation systems. This 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. In Fall 2021, he will join MIT Civil and Environmental Engineering as an Assistant Professor.
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