Bing Yan Headshot

Bing Yan

Assistant Professor

Department of Electrical and Microelectronic Engineering
Kate Gleason College of Engineering

585-475-2655
Office Location

Bing Yan

Assistant Professor

Department of Electrical and Microelectronic Engineering
Kate Gleason College of Engineering

Education

B.S. in information management and information system from Renmin University of China in 2010; M.S. degrees in electrical engineering and statistics from University of Connecticut in 2012 and 2017, respectively; Ph.D. degrees in electrical engineering from University of Connecticut in 2016.

Bio

Dr. Bing Yan is currently an assistant professor in the Department of Electrical and Microelectronic Engineering at Rochester Institute of Technology. She received the B.S. degree in information management and information system from Renmin University of China in 2010, M.S. degrees in electrical engineering and statistics from University of Connecticut in 2012 and 2017, respectively, and Ph.D. degrees in electrical engineering from University of Connecticut in 2016. Before joining Rochester Institute of Technology, she was an assistant research professor in the Department of Electrical and Computer Engineering, University of Connecticut. Dr. Yan’s research interests include power system optimization, grid integration of renewables (wind and solar), operation optimization of microgrids and distributed energy systems, scheduling of manufacturing systems, and mixed integer linear programming optimization. Dr. Yan has been working on many projects collaborated with industrial partners over the years, resulting in more than 30 peer-reviewed articles.  Either as PI, Co-PI or Senior Personnel, Dr. Yan has two grants from National Science Foundation, and multiple contracts from Brookhaven National Laboratory under Department of Energy, ISO-New England, Mid-Continent ISO, National Cheng Kung University, and ABB. 

585-475-2655

Personal Links

Currently Teaching

EEEE-524
3 Credits
This course will introduce the details of electric power markets and the techniques to better use the available resources. Topics include the description of steam generation and renewable energy sources. Formulation of the cost associated with the generation and the optimization methods to minimize this cost in the economic dispatch problem. Unit commitment. Optimal power flow formulation and its solution methods. Introduction to smart grid technologies and challenges.
EEEE-281
3 Credits
Covers basics of DC circuit analysis starting with the definition of voltage, current, resistance, power and energy. Linearity and superposition, together with Kirchhoff's laws, are applied to analysis of circuits having series, parallel and other combinations of circuit elements. Thevenin, Norton and maximum power transfer theorems are proved and applied. Circuits with ideal op-amps are introduced. Inductance and capacitance are introduced and the transient response of RL, RC and RLC circuits to step inputs is established. Practical aspects of the properties of passive devices and batteries are discussed, as are the characteristics of battery-powered circuitry. The laboratory component incorporates use of both computer and manually controlled instrumentation including power supplies, signal generators and oscilloscopes to reinforce concepts discussed in class as well as circuit design and simulation software.
EEEE-499
0 Credits
One semester of paid work experience in electrical engineering.
EEEE-624
3 Credits
This course will introduce the details of electric power markets and the techniques to better use the available resources. Topics include the description of steam generation and renewable energy sources. Formulation of the cost associated with the generation and the optimization methods to minimize this cost in the economic dispatch problem. Unit commitment. Optimal power flow formulation and its solution methods. Introduction to smart grid technologies and challenges.

In the News

  • May 27, 2021

    two people install monitoring equipment on a power pole.

    Micatu Inc. donates high-tech optical sensors for campus microgrid

    Micatu Inc. donated its groundbreaking Gridview optical sensors to RIT for a new campus learning lab. The equipment allows faculty and students to monitor renewable integration and manage the addition of distributed energy resources onto the campus microgrid.