Speaker: Prof.Tingwen Huang（Texas A&M University）

Title: Efficient Computational Approaches applying to Several Optimization Problems in Smart Grid

Time: Wed, Jul.17 2019, AM:10:00-11:00

Location: Room 4318, Building No.4, Wushan Campus

Abstract:   

Efficient computational approaches to game theoretic model, large scale optimization problem would be introduced to solving challenging problems. In a smart grid context, a demand response strategy of electric vehicle charging is modeled by a stochastic game. Moreover, a two-stage stochastic game theoretical model is proposed for energy trading problem in a multi-energy microgrid system. In this work, the risk measurement technique, conditional value at risk(CVaR), is harnessed to estimate the overload risk during the peak hour and the overbidding risk while distributed alternating direction method of multipliers (ADMM) is accelerated by Nesterov gradient method to solve two game models. In addition, for a large scale economic dispatch problem, different distributed optimization algorithms are developed.

Biography:  

      Tingwen Huang is a Professor at Texas A&M University at Qatar， an IEEE Fellow. He received his B.S. degree from Southwest Normal University (now Southwest University), China, 1990, his M.S. degree from Sichuan University, China, 1993, and his Ph.D. degree from Texas A&M University, College Station, Texas, 2002. After graduated from Texas A&M University, he worked as a Visiting Assistant Professor there. Then he joined Texas A&M University at Qatar (TAMUQ) as an Assistant Professor in August 2003, then he was promoted to Professor in 2013. Dr. Huang’s research areas include neural networks, chaotic dynamical systems, complex networks, optimization and control. His 400+ journal publications in these areas including 140+ IEEE Transactions, and 50+ international conference papers including IJCAI. He was name the 2018 Highly Cited Researcher by Clarivate Analytics (formerly Thomson Reuters). One of his research project was awarded the Best Research Project by Qatar National Research Fund in 2015.