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理学院2017-2018学年春季第十四周学术报告(一)

发布时间:2018-06-08

学 术 报 告

 

报告题目:Optimization problems in Smart Grid

 

报告学者:Tingwen Huang

 

报告者单位Texas A&M University-Qatar

 

报告时间2018年6月10日星期日16:00—17:00

 

报告地点2号教学楼2层,生物研究生院会议室

 

摘要:Convex optimization technique and game theory have been understood with a more complex perspective in the past decades. Successful applications of convex optimization and game theory can be found in subjects of image and speech recognitions, medical diagnosis, robotics, business intelligence, smart manufacturing and so forth. In this talk, efficient computational approaches to game theoretic model, large scale optimization problem and Q-learning framework will be introduced to solving different challenging problems.

 

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. 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. 

 

For general control and optimization problems, neurodynamics based approaches to bilevel programming and nonconvex optimization are designed by a differential inclusion and an inertial dynamical system, respectively. In a time-varying directed communication networked environment, a distributed optimization method is studied where quantized information exchange is utilized to encode and decode the decision variables.  By considering an asynchronous information exchange rule, a distributed dual average algorithm is investigated with the Bregman distance. Furthermore, off-policy RL methods are employed to solve the model-free optimal control problems by using data from practical systems.

 

报告者简介:

Tingwen Huang is a professor at Texas A&M University-Qatar. 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. His research interests include neural networks based computational intelligence, distributed control and optimization, nonlinear dynamics and applications in smart grids. He has published more than three hundreds peer-review reputable journal papers. He was awarded with Dean’s Fellow as Recognition of Faculty’s Excellence and Achievements in 2014, and Faculty Research Excellence Award by TAMUQ in 2015. Moreover, he is also active in professional services. In 2012, he served as the President of Asia Pacific Neural Network Assembly (renamed to Asia Pacific Neural Network Society now). Currently, he serves as an associate editor for four journals including IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, and Cognitive Computation. He served as one of the Guest Editors for 8 volumes of Lecture Notes in Computer Science, 1 volume of Advances in Intelligent and Soft Computing, all published by Springer, and 5 special issues in peer-review journals. As the General Chair, he organized the 14th International Workshop on Complex Systems and Networks (IWCSN2017), the 9th International Conference on Advanced Computational Intelligence (ICACI2017), the 19th International Conference on Neural Information Processing (ICONIP2012)

 

主办教师:闻国光

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