UFL, Mechanical and Aerospace Engineering
Prabir Barooah is an Associate Professor of Mechanical and Aerospace Engineering at the University of Florida, where he has been since 2007. He received the Ph.D. degree in Electrical and Computer Engineering 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 Endeavour Executive Fellowship (2016) from the Australian Government, ASEE-SE (American Society of Engineering Education, South East Section) outstanding researcher award (2012), NSF CAREER award (2010), General Chairs’s 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). More information can be obtained from: http://web.mae.ufl.edu/pbarooah.
As we move away from fossil fuels toward renewable energy sources such as solar and wind, inexpensive energy storage technologies are required. This is so since renewable energy sources, such as solar and wind, are intermittent. An alternative to batteries – which are quite expensive – is “smart loads”, such as air conditioners equipped with computation and communication capability. With appropriate software, the power consumption of air conditioning — and many other loads — can be varied around a baseline. This variation is analogous to the charging and discharging of a battery. Loads equipped with such intelligence have the potential to provide a vast and inexpensive source of energy storage. Two principal challenges in creating a reliable virtual battery from millions of consumer loads include (1) maintaining consumers’ Quality of Service (QoS) within strict bounds, and (2) coordinating the actions of loads with minimal communication to ensure accurate reference tracking by the aggregate. In addition, the solution needs to be robust to cyber attacks and require low communication overhead. This talk describes our work in developing a unified framework in addressing these challenges. Two types of loads will be considered: commercial loads whose demand can be varied continuously in a range, and residential loads that are of “on/off” type. The nature of proposed solutions for these two loads are quite distinct. In the former we use local measurement of grid-frequency to communicate information without exchanging bits. In the latter, we use randomized control to reduce a combinatorial optimization problem to a problem of controller design for a linear dynamic system.