Villanova Engineering Students Earn Best Paper Awards at 2026 Conferences

Mozhi Chen

Mozhi Chen and Mingyu Zhai, both Electrical Engineering PhD students at Villanova University, were recently recognized with 2026 Best Paper awards at international conferences. The two students are advised by Liang Du, PhD, associate professor of Electrical and Computer Engineering.

Chen, in collaboration with Shengyi Wang, PhD, from the University of Arkansas and Zhenyu Zhao, from Imperial College London, received Best Paper at the 2026 Institute of Electrical and Electronics Engineers (IEEE) Power & Energy Society (PES) General Meeting, held in Montreal, Canada. The paper, “Leveraging Time Series Reprogramming of Large Language Models for Solar Disaggregation,” explores a new approach to using large language models (LLMs) to identify and estimate residential solar generation from a home’s overall electricity-use data. Chen and his collaborators adapted an existing LLM to analyze energy time-series data, enabling the model to distinguish solar generation from a household’s other electricity activity.

“Receiving the Best Paper Award was a great honor and especially meaningful to me as a first-year PhD student at Villanova,” Chen said. “Having our work recognized by the power and energy community was very encouraging, and it motivates me to continue exploring new ideas and pushing our research further.”

Mingyu Zhai

Zhai, in collaboration with Bo Jie, PhD, of the University of Tokyo, received Best Paper at the 2026 5th International Conference on Power Systems and Electrical Technology (PSET) in Osaka, Japan. The paper, “Uncertainty-Aware DER Flexibility Aggregation via Convex Hull Approximation and Deep Learning,” centers on a new approach for determining how much flexibility distributed energy resources, such as solar panels and batteries, can provide to the electric grid. The duo developed a framework that integrates physics-based modeling, mathematical optimization and deep learning to balance for the operational constraints of these resources. Zhai also received the award for Best Oral Presentation at the same conference.

“Looking ahead, I hope to further develop this research by extending the proposed methods to larger and more realistic power and energy systems,” Zhai said. “Ultimately, I hope this work can contribute to more efficient, reliable and sustainable operation of future energy systems.”