Resilience and Risk in Networked Systems

April 22, 2014
11:00am-12:00pm
Location: Davis Auditorium
Speaker: Prof. Munther A. Dahleh, Associate Head of Electrical Engineering and Computer Science, MIT

Abstract

We will present recent work on the resilience and risk of failure emerging in cyber-physical infrastructures such as smart transportation systems and the smart grid.

In the first part of the talk, we present results on the volatility and risk of failure associated with real-time response in the future smart grid. Real-time demand response has been postulated as the solution to the intermittency problem created by renewable generation. The proposed market architecture is simple, namely, consumers react directly to spot market prices in order to fulfill their demands. This mechanism creates a closed loop system between price and demand that has implications on efficiency, demand and price volatility, and risk of demand spikes. In this talk, we first present an analysis of this closed loop system for homogeneous consumers and highlight the tradeoffs between market efficiency and demand and price volatility. Then, we present an abstracted framework to analyze the tradeoffs between efficiency and risk for heterogeneous consumers in the presence of shiftable demands. In this context, we expand the market mechanism to study the impact of coordination on such a tradeoff. We show that although the non-cooperative load-shifting scheme leads to an efficiency loss (otherwise known as the price of anarchy), the scheme has a smaller tail probability of the aggregate unshiftable demand distribution than cooperative schemes. This tail distribution is important as it corresponds to rare and undesirable demand spikes. Such instances highlight the role of the market mechanisms in striking a balance between efficiency and risk in real-time markets.

In the second part of the talk, we present results on the robustness (resilience) properties of transportation networks for various agents' route-choice behavior. We perform the analysis within a dynamical system framework over a directed acyclic graph between a single origin-destination pair. We give a precise characterization of various margins of resilience of the network with respect to the topology, "pre-disturbance" equilibrium, and agents' local route-choice behavior. We show that the cooperative route choice behavior is maximally resilient in this setting. We also setup a simple convex optimization problem to find the most resilient "pre-disturbance" equilibrium for the network and determine link-wise tolls that yield such an equilibrium. Finally, we extend the analysis to link-wise outflow functions that accommodate the possibility of cascaded failures and study the effect of such phenomena on the margins of resilience of the network.

Speaker Bio

Munther A. DahlehMunther A. Dahleh received his BS in Electrical Engineering from Texas A&M University in 1983 and his PhD in Electrical Engineering from Rice University in 1987. He has been a professor at the Electrical Engineering and Computer Science Department since 1998 and the associate department head since 2011. Prof. Dahleh has also served as the associate head of MIT's Laboratory for Information and Decision Systems (LIDS) between 2007 and 2010. His work has been recognized by numerous awards, including a Presidential Young Investigator Award in 1991.

Prof. Dahleh is interested in problems at the interface of robust control, filtering, information theory, and computation, which include control problems with communication constraints and distributed mobile agents with local decision capabilities. His interests include problems in network science, such as distributed computation over noisy networks and information propagation over complex social networks. He also studies model reduction problems for discrete-alphabet hidden Markov models and universal learning approaches for systems with both continuous and discrete alphabets. His research includes the interface between systems theory and neurobiology, and in particular, providing an anatomically consistent model of the motor control system.


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