讲座主题：Managing Uncertainty in Future Smart Grid: An Online-Algorithmic Approach towards Robust and Efficient Decisions
Title: Managing Uncertainty in Future Smart Grid: An Online-Algorithmic Approach towards Robust and Efficient Decisions
How to respond to uncertainty is one of the primary challenges facing future power systems, which must operate under 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 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 decision algorithms that can achieve not only efficient outcomes on average, but also robust worst-case performance against future uncertainty.
In this talk, we argue that competitive online algorithms could be a useful framework for solving this type of sequential decision problems in future smart grid. We present one such study in the context of managing EV (Electric Vehicle) charging along with renewable supply to minimize the peak consumption from the grid. In the typical CS literature, an optimal competitive online algorithm, which achieves the smallest possible worst-case competitive ratio compared to the offline solution, can be found even when there is absolutely no prior information about the future input. However, in power systems, such competitive ratios could be quite pessimistic because it does not exploit any partial (yet inaccurate) future information that may be available. Instead, we demonstrate how to utilize partial future knowledge in the form of day-ahead and hour-ahead forecasts to develop new online algorithms with both greatly-reduced worst-case competitive ratios and superior average-case performance.
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 communication networks and cyber-physical systems. 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.