Events Calendar

Competitive Energy Generation Scheduling in Microgrids

Speaker: Minghua Chen, Chinese University of Hong Kong

Talk Title: Competitive Energy Generation Scheduling in Microgrids

Abstract: 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 energy generation strategies that are critical for microgrid operation. Traditional generation scheduling paradigms assuming perfect prediction of renewable output and demand are no longer applicable to microgrids with intermittent renewable output and co-generation (that depends on both electricity and heat demand).

In this talk, we will first give a brief overview on microgrids and its potentials in addressing major challenges faced by power grids today. We then present a competitive-optimization paradigm for microgrid energy generation scheduling. Our objective is to maximize the microgrid economic benefit (in terms of cost saving) in an online fashion, i.e., without relying on predicting future demand and renewable output. Based on insights from the offline optimal solution that is computed with perfect future knowledge, 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, i.e., the cost of CHASE is at most 3 times of the offline optimal under arbitrary demand and renewable output. We also extend CHASE to intelligently leverage on limited prediction of the future, such as near-term demand or wind forecast, to further improve its performance. 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.

Biography: 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 Assistant 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), 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 recent research interests include smart (micro) grids, data centers, distributed network optimization, multimedia networking, p2p networking, wireless networking, network coding, and distributed storage systems.

Host: Michael Neely, x.03505, mjneely@usc.edu

Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

Audiences: Everyone Is Invited

Contact: Gerrielyn Ramos