概述
日期
2022年12月02日
09:00 - 10:00
地点
活动行、Bilibili

Learning and Control in Power Distribution Grids

IEEE TNSE Distinguished Seminar Series is co-sponsored by IEEE Transactions on Network Science and Engineering (TNSE) and Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), with joint support from The Chinese University of Hong Kong, Shenzhen, Network Communication and Economics Laboratory (NCEL), and IEEE. This series aims to bring together top international experts and scholars in the field of network science and engineering to share cutting-edge scientific and technological achievements.

Join the seminar on December 2 through 活动行 (http://hdxu.cn/WdWHC) or Bilibili (http://live.bilibili.com/22587709).

  • Jianwei Huang
    Vice President, AIRS; Presidential Chair Professor, CUHK-Shenzhen; Editor-in-Chief, IEEE TNSE; IEEE Fellow; AAIA Fellow
    Executive Chair
  • Steven Low
    Gilloon Professor of the Department of Computing & Mathematical Sciences and the Department of Electrical Engineering at Caltech, Honorary Professor of the University of Melbourne, IEEE/ACM/CSEE Fellow
    Learning and Control in Power Distribution Grids

    Steven Low is the Gilloon Professor of the Department of Computing & Mathematical Sciences and the Department of Electrical Engineering at Caltech and Honorary Professor of the University of Melbourne, Australia.  He is an awardee of the IEEE INFOCOM Achievement Award and the ACM SIGMETRICS Test of Time Award, and is a Fellow of the IEEE, ACM, and CSEE. He received his B.S. from Cornell and PhD from Berkeley, both in EE.

    Our energy system is undergoing a historic transformation to become more sustainable, dynamic, and open. The power distribution system, where most smart grid innovations will happen, is not well modeled, with the topology and line parameters poorly documented, inaccurate, or missing. This makes maintaining voltage stability challenging as renewable generation continues to proliferate. We present three results to address this challenge. The first result is a method to exactly identify the topology and line admittances of a radial network from voltage and current measurements even when measurements are available only at a subset of the nodes, provided every hidden node has a degree at least 3. The second result is a learning-augmented feedback controller that can leverage real-time measurements to stabilize voltages without explicit knowledge of the network model. We provide convergence guarantee for the proposed method. Finally, we describe the design and deployment of a large-scale EV charging system and an open-source research facility built upon it.

Video Archive