GlobalSIP 2018:

Information Processing, Learning and Optimization for Smart Energy Infrastructures

[Download the PDF Call for Papers]

The energy grid is undergoing a rapid transformation toward a more sustainable, efficient, resilient and secure infrastructure, in which advanced information science and technology play an essential role. New challenges emerge with the increasing penetration of renewable energy sources, extreme weather conditions, and cyber and physical security threats. Meanwhile, promising opportunities arise with nextgeneration monitoring and control devices as well as increasing customer participation. The rich algorithmic and analytical toolsets developed by the classical signal processing community along with contemporary and emerging advancements in the field are actively contributing to the development of innovative solutions for smart energy systems. Conversely, smart grid challenges are accelerating developments in core signal and information processing theory that are also applicable to new domains. This symposium aims to bring together researchers in the fields of information and signal processing, learning, and optimization for smart energy infrastructures.

Distinguished Symposium Talk

Anna Scaglione Photo

Anna Scaglione

Arizona State University

Grid Graph Signal Processing

Power systems sensors data analysis relies on Kirchhoff and Ohm’s laws to model the sensor field. Interestingly, these basic laws we learn as undergraduates, induce a structure in the current and voltage measurement data that finds parallels in classical RADAR sensor array processing as well as the emerging field of Graph Signal Processing. Also, given the predictable sparse harmonic content of the power-line carrier, the grid is an ideal realm to apply sub-Nyquist sampling. This talk will go from basic models to the abstraction of the problem formulation that makes these connections apparent, with the goal of establishing a taxonomy of Grid-Graph Signal Processing problem and highlighting some of the opportunities that exist for advancing how these critical systems are monitored.

Anna Scaglione (M.Sc.'95, Ph.D. '99) is currently a professor in electrical and computer engineering at Arizona State University. She was Professor of Electrical Engineering previously at the University of California at Davis (2008-2014), and at Cornell University, (2001-2008). Prior to joining the engineering faculty at Cornell, Scaglione was an assistant professor at the University of New Mexico (2000-2001). Dr. Scaglione’s expertise is in the broad area of statistical signal processing for communication, electric power systems and information networks. Her current research focuses on studying and enabling decentralized learning and signal processing in networks of sensors. Dr. Scaglione was elected an IEEE fellow in 2011, honored by both the Signal Processing and the Communication Societies. She was editor in chief in (2012-2013) of the IEEE Signal Processing Letters, and served as associate editor for the IEEE Transactions on Wireless Communications from 2002 to 2005. From 2008 to 2011, she served on the editorial board of the IEEE Transactions on Signal Processing from 2008, where she was area editor in 2010-11. She is currently Senior Editor for the IEEE Transactions on Control of Networked Systems.

She was general chair of the SPAWC 2005 workshop and on the Signal Processing for Communication Committee from 2004 to 2009. She has been an IEEE SmartGridComm conference steering committee member from 2010 to 2015, and was on board of governors of the IEEE Signal Processing Society during 2011-2014. Dr. Scaglione received the 2000 IEEE Signal Processing Transactions Best Paper Award and the 2013, IEEE Donald G. Fink Prize Paper Award for the best review paper in that year in the IEEE publications. Her research with her students was also honored with the 2013 IEEE Signal Processing Society Young Author Best Paper Award (Lin Li), and three conference best paper awards: the Ellersick Best Paper Award (MILCOM 2005) and the student best paper award at Smartgridcomm 2014 and the student best paper award at ICASSP 2017. Se was also a recipient of the NSF CAREER grant (2002).

Gil Zussman Photo

Gil Zussman

Columbia University

Power Grid State Recovery following a Joint Cyber and Physical Attack

We focus on joint cyber and physical attacks on power grids and present methods to retrieve the grid state information following such attacks. We consider models where an adversary attacks an area by (i) physically disconnecting some of the power lines, and (ii) blocking/modifying the measurements from monitoring devices within the area to mask the line failures. We use tools from linear algebra and graph theory, and leverage the properties of the power flow equations to develop methods for state recovery. Namely, using information observed outside of the attacked area, these methods recover information about the disconnected lines and the state inside the attacked area. We identify sufficient conditions on the area structure and constraints on the attack characteristics such that these methods can correctly recover the state. We consider the DC and AC power flow models, measurement noise, and false data injection attacks, and present corresponding analytical and numerical results.

Based on joint work with Saleh Soltan (Princeton) and Mihalis Yannakakis (Columbia)

Gil Zussman received the Ph.D. degree in Electrical Engineering from the Technion in 2004. Between 2004 and 2007 he was a Postdoctoral Associate at MIT. Since 2007 he has been with Columbia University where he is now an Associate Professor of Electrical Engineering and Computer Science, and member of the Data Science Institute. Between 2014 and 2016 he was a Visiting Scientist in the School of Computer Science in Tel Aviv University. His research interests are in the area of networking, and in particular in the areas of wireless, mobile, and resilient networks. Gil received the Knesset (Israeli Parliament) award for distinguished students, two Marie Curie fellowships, the Fulbright Fellowship, the DTRA Young Investigator Award, and the NSF CAREER Award. He was the PI of a team that won the 1st place in the 2009 Vodafone Americas Foundation Wireless Innovation Project competition and is currently the Columbia PI of the NSF PAWR COSMOS testbed. He is a co-recipient of seven best paper awards, including the ACM SIGMETRICS/IFIP Performance’06 Best Paper Award, the 2011 IEEE Communications Society Award for Advances in Communication, and the ACM CoNEXT’16 Best Paper Award.


Wednesday, November 28
08:30 - 09:30
Plenary PLEN-2: Anna Scaglione: "Grid Graph Signal Processing"
11:00 - 12:30
SMI-L.1: Optimization and Control in Smart Grids
14:00 - 15:30
SMI-L.2: Resilience and Security of Power Grids
Thursday, November 29
09:40 - 10:40
DL DL-SMI.1: Gil Zussman: "Power Grid State Recovery following a Joint Cyber and Physical Attack"
11:00 - 12:30
SMI-L.3: Learning in Energy Systems
14:00 - 15:30
SMI-P.1: Monitoring, Control and Markets in Energy Systems

Organizing Committee

General Chair

Technical Program Chairs

Submissions are welcome on topics including:

Paper Submission

Prospective authors are invited to submit full-length papers (up to 4 pages for technical content including figures and possible references, and with one additional optional 5th page containing only references) and extended abstracts (up to 2 pages, for paper-less industry presentations and Ongoing Work presentations).. Manuscripts should be original (not submitted/published anywhere else) and written in accordance with the standard IEEE double-column paper template. Accepted full-length papers will be indexed on IEEE Xplore. Accepted abstracts will not be indexed in IEEE Xplore, however the abstracts and/or the presentations will be included in the IEEE SPS SigPort. Accepted papers and abstracts will be scheduled in lecture and poster sessions.

Important Dates

Paper Submission DeadlineJune 17, 2018 June 29, 2018
Review Results AnnouncedSeptember 7, 2018
Camera-Ready Papers DueSeptember 24, 2018
November 5, 2018Hotel Room Reservation Deadline