The Benefits of Seeing the Future in DREAMS
|Event Date:||March 25, 2014|
|Speaker:||Dr. Minghua Chen|
|Speaker Affiliation:||The Chinese University of Hong Kong|
|Sponsor:||Prospective ECE faculty member|
|Contact Name:||Professor Xiaojun Lin
In this talk, I will first give an overview of the research of my lab – the Design of Reliable and Efficient Algorithms, Models and Systems (DREAMS) Lab – at The Chinese University of Hong Kong. Then I will focus on one major research theme of characterizing and capitalizing the benefits of looking-ahead in designing various engineering systems. The investigations are motivated by recent advance of applying machine learning techniques to leverage historical data to forecast near-future system inputs, e.g., workload for data centers or wind-power generation in microgrids. Then, as a concrete example, I will elaborate on our recent work on developing future-aware competitive energy generation strategies in microgrids.
Microgrids represent an emerging paradigm of future electric power systems that integrate both distributed and centralized generation. Two recent trends in microgrids are the integration of local renewable energy sources (such as wind farms) and the use of co-generation (i.e., to supply both electricity and heat). However, these trends also bring unprecedented challenges to the design of intelligent control strategies for the microgrids. Traditional generation scheduling paradigms assuming perfect prediction of future electricity supply and demand are no longer applicable to microgrids with unpredictable renewable energy supply and co-generation (that depends on both electricity and heat demand). In this work, we study online algorithms for the microgrid generation scheduling problem with intermittent renewable energy sources and co-generation, in order to maximize the cost savings without the need to predict future demand and supply. Based on insights from the structure of the offline optimal solution, we propose a class of competitive online algorithms, called CHASE (Competitive Heuristic Algorithm for Scheduling Energy-generation), that track the offline optimal in an online fashion. Under typical settings, we show that CHASE achieves the best competitive ratio of all deterministic online algorithms and the ratio is no larger than 3. We also extend our algorithms to intelligently leverage on limited prediction of the future, such as near-term demand or wind forecast. By extensive empirical evaluation using real-world traces, we show that our proposed algorithms can achieve near-offline-optimal performance. In a representative scenario, CHASE leads to around 20% cost savings with no future look-ahead at all, and the cost-savings further increase with limited future look-ahead.
Minghua Chen received his B.Eng. and M.S. degrees from the Department of Electronic Engineering at Tsinghua University in 1999 and 2001, respectively. He received his Ph.D. degree from the Department of Electrical Engineering and Computer Sciences at University of California at Berkeley in 2006. He spent one year visiting Microsoft Research Redmond as a Postdoc Researcher. He joined the Department of Information Engineering, the Chinese University of Hong Kong, in 2007, where he currently is an Associate Professor. He is also an Adjunct Associate Professor in Peking University Shenzhen Graduate School in 2011-2014. He received the Eli Jury award from UC Berkeley in 2007 (presented to a graduate student or recent alumnus for outstanding achievement in the area of Systems, Communications, Control, or Signal Processing) and The Chinese University of Hong Kong Young Researcher Award in 2013. He also received several best paper awards, including the IEEE ICME Best Paper Award in 2009, the IEEE Transactions on Multimedia Prize Paper Award in 2009, and the ACM Multimedia Best Paper Award in 2012. His current research interests include smart (micro) grids, energy efficient data centers, distributed network optimization and control, multimedia networking, wireless networking, and network coding.