Zhang and His Students Look To Advance Power Systems Into The Future

Peng with the members of his Power and Energy Systems Lab. (UConn Photo/Christopher Larosa)

Over 350 million people in the United States depend on the reliability and consistency of the 450,000 miles of high-voltage lines that form the U.S. power grid to do important daily tasks. With stronger weather events and an increasing number of cyber-attacks, reliable safeguards and technologies are needed to protect this very important utility. The Power and Energy Systems Laboratory, run by Dr. Peng Zhang, F.L. Castleman Associate Professor of Electrical and Computer Engineering and his graduate students, aims at tackling these important issues.

The lab, which focuses on smart grid technology, microgrids, and sustainable energy, has worked on several crucial projects over the past several years, including a dedicated approach to networking the grid system, determining risk assessment models for unintentional islanding of power generators, using ocean waves to generate a sustainable power source, and many other related areas of research.

The lab’s most recent research, which focuses on national infrastructure, is looking to enhance the connectedness of the fractured U.S. grid system:  

“The goal of our research is to make our nation’s energy infrastructure resilient, reliable, secure, and sustainable,” Zhang said. “One of our main areas of focus now is large systems power stability, which is important, because there are no tools available to really assess and predict the status of the system. This tool is highly needed, especially for connected systems.”

That tool, which is being developed from a $1.05 million Department of Energy grant, is being worked on by Zhang and Ph.D. student Yan Li. The idea for the grant was inspired by the learnings Zhang and his students gathered from studying the tools used by local utility company, Eversource Energy:

“The whole U.S. and Canadian grid are connected together, and it’s a huge system, so it’s  therefore very difficult to monitor, assess and control its stability ,” Zhang said. “If you look at Connecticut, companies like Eversource, for the most part, use off-line tools which run many scenarios and only look at snapshots of the system, but that kind of work is not great for analysis, it needs to be monitored and assessed in real-time.”

In particular, Li and Zhang will be forming a formal theory with mathematical rigor, which will be established for computing the bounds of all possible trajectories and estimating the stability margin for the entire system, including the integrated transmission and distribution network.

Furthermore, a new open-source tool via reachable set computations will be developed for real-time dynamic analysis and stability margin calculations. It will be applicable for not only forecasting and monitoring grid performance, but also formally verifying various resiliency enhancement strategies, such as new schemes for system integrity protection and automation to adapt to this evolution of electric networks. 

Zhang and his students are also making significant contributions to knowledge advancement in the state of Connecticut and the region, with their work at the Eversource Energy Center at UConn, which conducts research related to advancing energy technology, as well as performs significant consulting work with Eversource Energy.

Zhang and his students have specifically performed work on research related to unintentional islanding, which is a phenomenon in which a distributed generator continues to be electrified and running, even when the electrical grid surrounding it is no longer active. Traditional methods of detection can often be fooled to think that grid conditions are normal, especially when multiple power generation devices are connected to the same line. 

(UConn Photo/Christopher Larosa)

This kind of scenario is very dangerous to field workers, as the lack of knowledge could cause them to be electrocuted. Zhang said that the research that’s currently being done by himself and his team addresses the creation a risk assessment model to safely avoid danger:

“This kind of research is using machine learning to predict risk in the non-detection zone,” Zhang said. “By non-detection we mean that when an island occurs, there are certain scenarios where the utility company, but with this new risk assessment model, we’ll be able to accurately predict when this scenario is likely to occur.”

Most importantly though, Zhang is happy that the graduate students that he is mentoring, and providing hands-on opportunities to, are getting the necessary experience needed to launch their careers in research and academia.

Ph.D. student Taofeek Orekan, one of the members of the lab, is one of the students that will be using that experience, as he looks for post-grad opportunities in the next few months:

“This is a great lab to start off in,” Orekan said. “I know that any lab that I launch during my career in academia will absolutely be an extension of this lab.”

For more information on the lab, visit http://power.engr.uconn.edu.