学术报告:2021年 09月24日 13:45-14:45,Texas A&M University at Qatar-Tingwen Huang

发布者:吕小俊发布时间:2021-09-24浏览次数:13

东南大学数学学院邀请专家申请表


报告人

Tingwen Huang

单位

Texas A&M University at Qatar

报告题目

Efficient Computational Approaches and Applications to Some Optimization Problems in Smart Grid

报告时间

924

13:45-14:45

地点

腾讯会议:490769787

邀请人

曹进德,许文盈

报告摘要

In a smart grid context, a demand response strategy of electric vehicle charging is modelled by a stochastic game, where a big data analytic framework is proposed for controlling the electric vehicle charging behaviours. We will look at Plug-In Electric Vehicles (PEVs) Charging: Feeder Overload Control problem. Moreover, a two-stage stochastic game theoretical model is proposed for energy trading problem in a multi-energy microgrid system. In these two 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. Concerning the privacy, a research branch of reinforcement learning (RL) that dominates distributed learning for years will be presented by making the first attempt to apply RL-based algorithms in the energy trading game among smart microgrids where no information concerning the distribution of payoffs is a priori available and the strategy chosen by each microgrid is private to opponents, even trading partners. To solve this challenge, a new energy trading framework based on the repeated game that enables each microgrid to individually and randomly choose a strategy with probability to trade the energy in an independent market so as to maximize his/her average revenue. In addition, for a large scale economic dispatch problem, different distributed optimization algorithms are developed, including a fast event-triggered scheme and consensus based multiagent methods. 

报告人简介

Prof. Tingwen Huang's research focuses on dynamics of nonlinear systems including neural networks, complex networks and multi-agent and their applications to smart grids and cybersecurity. He is the first recipient of Dean’s Fellow for Recognition of Faculty’s Excellence and Achievements awarded by Texas A&M University at Qatar (TAMUQ) in 2014, was bestowed Faculty Research Excellence Award by TAMUQ in 2015, was elected as IEEE Fellow in 2018, conferred Changjiang Chair Professor in 2019 by Ministry of Education of China, was elected as Academician of the International Academy for Systems and Cybernetic Sciences recently.