Coordination of loads with frequency domain consumer constraints for grid support
Flexible loads can provide virtual energy storage (VES) by varying their demand around a baseline, just like charging and discharging a battery. Since the capacity of each load is small, the actions of many loads must be coordinated. We propose a decentralized coordination architecture to provide VES in the time-scale of minutes to hours. This is achieved through one-way broadcast from a balancing authority to all the loads, and local measurement of the grid frequency at each load. The local controller at a load uses a Model Predictive Control scheme which ensures consumers’ quality of service (QoS) by constraining the Fourier transforms of the control signal (the demand variation). The frequency domain approach to characterize load flexibility is quite general, as it can encompass electro-chemical batteries and generators. The algorithm has provable convergence and robustness (to measurement noise) guarantees. Simulation studies are provided to assess its performance.
Associate Professor and University of Florida Term Professor Mechanical and Aerospace Engineering , University of Florida Gainesville, FL 32611
Professor Barooah received his Ph.D. in 2007 from the University of California, Santa Barbara. From 1999 to 2002 he was a research engineer at United Technologies Research Center, East Hartford, CT. He received the M. S. degree in Mechanical Engineering from the University of Delaware in 1999 and the B.Tech degree in Mechanical Engineering from the Indian Institute of Technology, Kanpur, in 1996. Dr. Barooah is the winner of the ASEE SE Section’s Outstanding Researcher Award (2013), NSF CAREER award (2010), General Chairs' Recognition Award for Interactive papers at the 48th IEEE Conference on Decision and Control (2009), best paper award at the 2nd Int. Conf. on Intelligent Sensing and Information Processing (2005), and NASA group achievement award (2003).
His research interests include:
Control systems for a smart and sustainable energy infrastructure (in particular, on enabling buildings to reduce their energy use and provide “virtual storage” to the electric grid for integrating renewable energy sources such as solar and wind), distributed estimation and control algorithms (for applications such as localization/synchronization of sensor networks and robotic swarms), and distributed optimization.