Abstract:  The growth of wind energy motivates new models and control designs that account for a wider range of conditions and enable wind farms to provide the grid services that are often required of conventional generators. This talk introduces a suite of models for the design and control of wind farms. We first introduce the area localized coupled (ALC) model, which couples the steady state solution of a dynamic wake model with a localized top-down model that focuses on the affect of the farm on the atmospheric boundary layer.  The ALC model improves the accuracy of power output and local velocity predictions over both conventional wake models and top-down models, while extending the applicability of this type of coupled model to arbitrary wind farm layouts. In the second part of the talk we employ the dynamic wake model within a model-based wind farm control approach for tracking a time-varying power signal, such as a frequency regulation command. The underlying time-varying wake model extends commonly used static models to account for wake advection and lateral wake interactions. We then perform numerical studies of the controlled wind farm using a large eddy simulation (LES) with actuator disks as a wind farm model with local turbine thrust coefficients (‘synthetic pitch’) as the control actuation.  Our results show that embedding this type of dynamic wake model within a model-based receding horizon control framework leads to a controlled wind farm that qualifies to participate in markets for correcting short-term imbalances in active power generation and load on the power grid (frequency regulation).  Accounting for the aerodynamic interactions between turbines within the proposed control strategy yields large increases in efficiency over prevailing approaches by achieving commensurate up-regulation with smaller derates (reductions in wind farm power set points). This potential for derate reduction has important economic implications because smaller derates directly correspond to reductions in the loss of bulk power revenue associated with participating in regulation markets. 

Bio:  Dennice F. Gayme is an Associate Professor in Mechanical Engineering and the Carol Croft Linde Faculty Scholar at the Johns Hopkins University.  She earned her B. Eng. & Society from McMaster University in 1997 and an M.S. from the University of California at Berkeley in 1998, both in Mechanical Engineering. She received her Ph.D. in Control and Dynamical Systems in 2010 from the California Institute of Technology, where she was a recipient of the P.E.O. scholar award in 2007 and the James Irvine Foundation Graduate Fellowship in 2003. Her research interests are in modeling, analysis and control for spatially distributed and large-scale networked systems in applications such as wall-bounded turbulent flows, wind farms, power grids and vehicular networks.  She was a recipient of the JHU Catalyst Award in 2015, ONR Young Investigator and NSF CAREER awards in 2017, a JHU Discovery Award in 2019 and a Whiting School of Engineering Johns Hopkins Alumni Association Excellence in Teaching Award, 2020.