Many large-scale systems involve the interaction of a number of agents with loosely coupled dynamics and decisions, for instance in demand response management in electricity grids, where the agents locally optimize their decisions, but their eventual well being also depends on the aggregate of the decisions of all other agents. For such systems, it is typically impractical to impose a centralized control structure for a number of reasons (e.g., privacy concerns, computational and communication limitations). Instead one can provide suitable information to the agents and impose an appropriate penalty/reward feedback scheme to control the overall population using macroscopic commands only, so that the population exhibits a desirable macroscopic behavior. In this talk, we discuss an aggregative control structure via a game theoretical approach, and present technical results based on fixed point operator theory. The discussion is illustrated via the application to the charging coordination of large fleets of plug-in electric vehicles. Finally, some open research opportunities are presented.

Sergio Grammatico is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology, Netherlands. He received the B.Sc. in Computer Engineering in 2008, M.Sc. and Ph.D. degrees in Automation Engineering in 2009 and 2013, respectively, all from the University of Pisa, Italy. He also received a M.Sc. degree in Engineering Science from the Sant’Anna School of Advanced Studies, Pisa, Italy, in 2011. He visited the Department of Mathematics at the University of Hawai’i at Manoa in 2010 and 2011, and the Department of Electrical and Computer Engineering at U.C. Santa Barbara in 2012. During 2013-2015 he was  a post-doctoral Research Fellow at the Automatic Control Laboratory, ETH Zurich, Switzerland.
He was nominated IEEE TAC Outstanding Reviewer in 2013 and 2014. His research interests include Lyapunov control systems, stochastic, game-theoretic control and optimization, with application to large-scale smart energy systems.