Robust and Efficient Online Decisions for Managing Uncertainty in Future Smart Grid

Date and time: 
Thursday, May 18, 2017 - 15:30
220 Deschutes
Xiaojun Lin
School of Electrical and Computer Engineering, Purdue Univer
  • Lei Jiao


Advances in smart grid allow us to utilize tools from computing, communication, and control to solve pressing challenges in power systems. One of such critical challenges is how to respond to the significant uncertainty both in the renewable supply (wind/solar) and in the demand patterns. Such uncertainty is often revealed sequentially in time, and thus the decision at each time instant must be adjusted based on the information that has already been revealed, and yet be prepared for the remaining uncertainty towards the future. Further, the nature of the power systems often dictates that robust performance guarantees must be assured even at the worst-case uncertainty, e.g., the energy supply must always meet the demand, and otherwise the entire power grid may fall apart. Thus, there is a pressing need to develop sequential/online decision algorithms that can achieve not only efficient outcomes on average, but also robust worst-case performance guarantees against future uncertainty.

In this talk, we will demonstrate how to develop such sequential decision algorithms using ideas from both competitive online algorithms and adaptive robust optimization. Compared to existing results in the literature, the key novelty of our approach is twofold. First, we will intelligently utilize partial future knowledge in the form of day-ahead and/or hour-ahead forecasts to develop online solutions that strike the right balance between tractability and performance guarantees. Second, we will develop online solutions that perform well for both worst-case and average-case inputs. We will illustrate these features through two examples for future smart grid with high uncertainty: one at the transmission level for maintaining demand-supply balance at all time subject to generation/transmission constraints, and the other at the distribution level for managing EV (electrical vehicle) charging to minimize the peak consumption.


Xiaojun Lin received his B.S. degree in Electronics and Information Systems from Zhongshan University, Guangzhou, China, in 1994, and his M.S. and Ph.D. degrees in Electrical and Computer Engineering from Purdue University in 2000 and 2005. He joined the faculty of School of Electrical and Computer Engineering, Purdue University, as an Assistant Professor in August 2005, and has been an Associate Professor since July 2011.

Xiaojun's research interests are in the simplification of network dynamics in large communication networks, resource allocation, network pricing, Quality-of-Service routing, wireless cross-layer control, mobile ad hoc and sensor networks.

Xiaojun received the IEEE INFOCOM 2008 best paper award and 2005 best paper of the year award from Journal of Communications and Networks. His paper was also one of two runner-up papers for the best-paper award at IEEE INFOCOM 2005. He received the NSF CAREER award in 2007. He was the Workshop co-chair for IEEE GLOBECOM 2007, the Panel co-chair for WICON 2008, the TPC co-chair for ACM MobiHoc 2009, the Mini-Conference co-chair for IEEE INFOCOM 2012, the Workshop chair for WiOpt 2014, and the Publication chair for WiOpt 2016.

He is currently serving as an Associate Editor for IEEE/ACM Transactions on Networking and an Area Editor for (Elsevier) Computer Networks journal, and has served as a Guest Editor for (Elsevier) Ad Hoc Networks journal.

Xiaojun's research has been supported by NSF (National Science Foundation), Army Research Office, and Intel Corporation.