Robust and Efficient Online Decisions for Managing Uncertainty in Future Smart Grid
|Author:||Xiaojun Lin School of Electrical and Computer Engineering, Purdue University|
|Date:||May 18, 2017|
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. from Zhongshan University, Guangzhou, China, in 1994, and his M.S. and Ph.D. degrees from Purdue University, West Lafayette, Indiana, in 2000 and 2005, respectively. He is currently an Associate Professor of Electrical and Computer Engineering at Purdue University. Dr. Lin's research interests are in the analysis, control and optimization of large and complex networked systems, including both communication networks and cyber-physical systems. Dr. Lin is a Fellow of IEEE.
He 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 is currently serving as an Area Editor for (Elsevier) Computer Networks journal and an Associate Editor for IEEE/ACM Transactions on Networking, and has served as a Guest Editor for (Elsevier) Ad Hoc Networks journal.