Infrastructure Systems

Smart Grid Control and Optimization

Led by Yufei Tang, Ph.D.

Dr. Yufei Tang

Yufei Tang is an Assistant Professor in the Department of Computer & Electrical Engineering and Computer Science at Florida Atlantic University (FAU). He is also a Faculty Fellow of the FAU’s Institute for Sensing and Embedded Network Systems Engineering (I-SENSE). He received his Bachelor's degree in Electrical Engineering and Automation in 2008, and Master's degree in Power Systems in 2011, from Hohai University, Nanjing, China. After that, he got his Ph.D. in Electrical Engineering from the University of Rhode Island in 2016. His research interests lie in the general area of Computational Intelligence and the applications in Cyber-Physical Energy Systems. In particular, he is interested in developing new intelligent algorithms (e.g., reinforcement learning, adaptive dynamic programming) and building efficient and resilient electrical power system (e.g., power system stability control and optimization, cyber-physical smart grid security).

Personal website

PROJECT

Smart Energy Systems

This project focuses on the development of new intelligent algorithms and controllers for renewable resources integrated smart grid optimal control and operation.
The REU participant will learn the impact of the renewable resources integration into the traditional electric power system, and design effective control and optimization methods for building efficient and resilient smart energy system. In particular, the participant will be introduced to the exciting area of smart grid research, including wind and solar power integration, energy storage based damping control, and micro-grid operation and management. To facilitate the implementation, the design will be carried out using both Matlab/Simulink and professional power system simulation tools, such as PSCAD/EMTDC. Depending on background, the participant can investigate the characteristics of new smart grid models or develop new intelligent algorithms and test the effectiveness on current benchmarks. The project will provide a meaningful experience for the participant, while contributing to Yufei Tang’s ongoing work in this area.