Wind farm modeling and control for power grid support


The seminar is free to attend but registration is required – please email the organizers to receive an invitation

This talk introduces model-based wind farm control approaches for tracking a time-varying power signal and estimating wind farm power output under changing wind direction. The underlying time-varying wake models extend commonly used static models to account for wake advection and lateral wake interactions. We first focus on the application of power tracking for frequency regulation, specifically we 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. Furthermore, 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. We then describe a graph theoretic approach to predicting wind farm velocity and power output signals under changing wind conditions. We simulate the system using an event-based update framework to capture the time-dependent topology changes due to the shift in the wind directions. We validate the power tracking performance through a change in wind direction using comparisons to results generated using the FLOw Redirection and Induction in Steady State (FLORIS) wake model.

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 in 2020.