Kameshwar Poolla received the Ph.D. degree from the University of Florida, Gainesville in 1984. He has served on the faculty of the University of Illinois, Urbana from 1984 to 1991. Since then, he has been with the University of California, Berkeley where he is the Cadence Distinguished Professor of Mechanical Engineering and Electrical Engineering & Computer Sciences. He also serves as the Founding Director of the IMPACT Center for Integrated Circuit manufacturing at the University of California. Dr. Poolla co-founded OnWafer Technologies which offers metrology based yield enhancement solutions for the semiconductor industry. OnWafer was acquired by KLA-Tencor in 2007. He has also served as a technology and mergers/acquisitions consultant for Cadence Design Systems.
Dr. Poolla has been awarded a 1988 NSF Presidential Young Investigator Award, the 1993 Hugo Schuck Best Paper Prize, the 1994 Donald P. Eckman Award, the 1998 Distinguished Teaching Award of the University of California, the 2005 and 2007 IEEE Transactions on Semiconductor Manufacturing Best Paper Prizes, and the 2009 IEEE CSS Transition to Practice Award. His current research interests covers many aspects of the Smart Grid: Renewable Integration, Demand Response, Cybersecurity, Experimental Economics, and Sensor Networks.
Pressing environmental problems, energy supply security issues, and nuclear power safety concerns drive the worldwide interest in renewable energy. Renewable energy sources such as wind and solar exhibit variability: they are not dispatchable, exhibit large fluctuations, and are uncertain. Variability is the most important obstacle to deep integration of renewable generation. The current approach is to absorbthis variability in operating reserves. This works at today’s modest penetration levels. But it will not scale. At deep penetration levels (>30%) the levels of necessary reserves are economically untenable, and defeat the net carbon benefit.
So how can we economically enable deep penetration of renewable generation? The emerging consensus is that much this new generation must be placed at hundreds of thousands of locations in the distribution system, and that the attendant variability can be absorbed by the coordinated aggregation and control of distributed resources such as storage, programmable loads, and smart appliances. Tomorrow’s grid will have an intelligent periphery.
We will explore the architectural and algorithmic components for managing this intelligent periphery. Clusters of distributed energy resources are coordinated to efficiently and reliably offer services (ex: bulk power, regulation) in theface of uncertainty (ex: renewables, consumers). We begin by formulating a general class of stochastic dynamic programming problems that arise in the context of coordinated aggregation. We then consider specific real-time scheduling problems for allocating power to resources. We show that no causal optimal policy exists that respects rate constraints (ex: maximum EV charging rates). Next, we explore the benefits of coordinated aggregation in the metric of operating reserves savings. We close by suggesting several challenging problems in monetizing and incentivizing resource participation.