The talk addresses several key problems for reliable and efficient modeling and monitoring of smart grids. In term of model calibration, a new framework for identification and correction of model parameter errors is presented. The Largest Normalized Lagrange Multiplier (LNLM) test is introduced, and approaches for enhancing the reliability and computational efficiency of model error identification are presented. In terms of system monitoring, a unified robust state estimation approach against measurement and parameter errors is introduced. A fast and parallel implementation of bad data processing methods is also presented. Finally, the cyber-security issues in the modeling of smart grids are discussed. A security vulnerability regarding model databases which may affect the operation of electricity markets is identified, and possible countermeasures are discussed.

Yuzhang Lin is currently an assistant professor in the Department of Electrical and Computer Engineering at the University of Massachusetts, Lowell, MA, USA. He received his bachelor and master’s degrees in electrical engineering from Tsinghua University, Beijing, China, and his PhD degree in electrical engineering from Northeastern University, Boston, MA, USA. Dr. Lin is a recipient of the prestigious Outstanding Graduate Student Research Award (at most two each year) at Northeastern University, Boston, MA, USA. His current research interests include modeling, monitoring, data analysis, and cyber-physical security of smart grids.