讲座时间：2014年7月5日（周六），9:30 -10:30
讲座地点：西南交通大学九里校区交通运输与物流学院 01109
主讲人简介：章君山 教授， 美国亚利桑那州立大学
Junshan Zhang received his Ph.D. degree from the School of ECE at Purdue University in 2000. He joined the School of ECEE at Arizona State University in August 2000, where he has been Professor since 2010. His interests include cyber-physical systems, communications networks, and network science. His current research focuses on fundamental problems in information networks and energy networks, including modeling and optimization for smart grid, network optimization/control, mobile social networks, crowd sourcing, cognitive radio, and network information theory. Prof. Zhang is a fellow of the IEEE, and a recipient of the ONR Young Investigator Award in 2005 and the NSF CAREER award in 2003. He co-authored two papers that won the Best Paper Runner-up Award of IEEE INFOCOM 2009 and IEEE INFOCOM 2014, and a paper that won IEEE ICC 2008 Best Paper Award. He was TPC co-chair for a number of major conferences in communication networks, including INFOCOM 2012, WICON 2008 and IPCCC'06. He was the general chair for IEEE Communication Theory Workshop 2007. He was an Associate Editor for IEEE Transactions on Wireless Communications, an editor for the Computer Network journal and an editor IEEE Wireless Communication Magazine. He is currently serving as an editor-at-large for IEEE/ACM Transactions on Networking and an editor for IEEE Network Magazine. He is a Distinguished Lecturer of the IEEE Communications Society.
讲座内容简介：
主题：Big Data Processing and Stochastic Optimization for Wind Energy Integration
A central issue in meeting renewable portfolio standards (RPS) for smart grid is the integration of wind generation, which is challenging due to the high variability and non-dispatchability of wind energy. In this talk, we take a data analytics perspective to explore rigorous approaches in modeling, optimization, and control of wind generation integration. We first investigate short-term forecast of wind farm generation by applying spatio-temporal analysis to extensive measurement data collected (over two consecutive years) from a large wind farm where a number of wind turbines are installed over an extended geographical area . Departing from conventional approaches based on wind speed forecast, we devise graph models to capture the spatial dependence structure across wind turbines’ power outputs. Then, graph-learning based spatial analysis is carried out to characterize the statistical distribution of the overall wind farm generation and time series analysis is used to quantify the level crossing rate. Built on these characterizations, finite-state Markov chains are constructed for each epoch of three hours, which account for the diurnal non-stationarity and the seasonality of wind generation. Exploiting the Markovian property of the forecast model, we then cast the joint optimization of economic dispatch (ED) and interruptible load management as a Markov decision process (MDP) problem. A greedy policy is developed to reduce the complexity therein.