特邀报告： Dr. Zhenyu Huang

报告题目：Computing and Computing Architecture to Maximize Grid Flexibility

地点：西主楼三区102

时间：2017年1月10日 9:00~16:00

联系人：陈颖

Zhenyu (Henry) Huang (M’01 SM’05 F’17 IEEE) received his B. Eng. from Huazhong University of Science and Technology, Wuhan, China, and Ph.D. degree from Tsinghua University, Beijing, China, in 1994 and 1999, respectively. From 1998 to 2003, he conducted extensive research at the University of Hong Kong, McGill University (Canada), and the University of Alberta (Canada). He is currently Chief Engineer and Technical Group Manager at Pacific Northwest National Laboratory, Richland, Washington, USA. Dr. Huang has over 140 peer-reviewed publications. His research interests include high performance computing, phasor technology, and power system stability and simulation. Dr. Huang is a Fellow of IEEE and active in several IEEE Power and Energy Society (PES) technical committees. He led the Richland Chapter to win the 2007 IEEE PES Outstanding Small Chapter Award. He is the recipient of the 2009 IEEE Power and Energy Society Outstanding Young Engineer Award. Dr. Huang is a registered Professional Engineer in Washington State.

The power grid has evolved in recent years at an unprecedented pace with a new mix of electricity generation and consumption. This evolution results in emerging dynamics and increased uncertainties. There is a need to maximize grid flexibility to accommodate the increasing complexity due to such dynamics and uncertainties. Flexibility starts with deep understanding of the system, which heavily relies on computing. The complexity in the grid demands higher performance of computing tools. Historically, the performance of power grid computing tools naturally increases as computing hardware and software advance. Now computing technologies continue to evolve but on a different path. It is time to examine computing and the associated computing architecture for utilizing new high-performance computing resources.

This talk will present recent advancements in applying high performance computing to power grid applications. Extending from computing, a data-driven computing architecture is proposed to link measurements to computation and then to visualization, so computing methods and tools can leverage new data sources and account for new grid behaviors in order to ensure a reliable, efficient, and secure future power grid. It would harmonize the grid evolution and the information revolution and convert data to actionable information. Examples will be provided to illustrate the concept and value of such an architecture. The examples cover real-time stability assessment, transmission congestion management, and uncertainty quantification. In these examples, significant flexibility can be identified even when the grid is subject to many constraints. This will greatly facilitate the adoption of new generation and loads such as renewable energy and demand response.

日程安排

专家

单位

报告题目

时间

Zhenyu Huang

Pacific Northwest National Laboratory, USA

-Chief Engineer and Technical Group Manager, IEEE Fellow

Computing and Computing Architecture to Maximize Grid Flexibility

9:00~10:00

薛巍

清华大学计算机系 & 地学中心

-副教授

神威太湖之光及其高性能计算应用

10:00~10:30

茶歇

10:30~10:45

张星

中国电科院

-国家电网仿真中心数字混合仿真室主任

国网仿真超算中心建设及其应用思考

10:45~11:15

汪玉

清华大学电子系 & 北京深鉴科技

-党委副书记 副教授 优青

Deep Learning on FPGA

11:15~11:45

午餐

11:45~13:30

周二专

北京科东电气有限公司 & InterPSS

快速在线安稳分析系统(DSA)的研发

13:30~14:00

李国良

清华大学计算机系

-副教授

Hybrid human-machine big data integration

14:00~14:30

陈颖

清华大学电机系

-副教授

云仿真平台CloudPSS构想和实践

14:30~15:00

自由讨论

15:00~16:00