Infrastructure Systems: Smart, Resilient, Energy Systems

Led by Yufei Tang, Ph.D.

Future smart cities will depend on intelligent, resilient, and highly interactive power systems that can withstand increasing demands, distributed energy integration, and climate-driven disturbances. However, traditional grid design and operation approaches treat sensing, control, communication, and resilience strategies separately, overlooking their strong interdependencies. This often results in sub-optimal performance and reduced reliability, especially in urban environments with dense, interconnected infrastructures. This REU project aims to develop AI-driven methodologies for smart, resilient power systems by leveraging large language models (LLMs) for enhanced situational awareness, anomaly detection, and real-time decision support. The research will explore the use of LLMs to interpret grid data, support operators through natural-language guidance, and improve resilience assessments for distribution systems and microgrids that serve smart city applications. Participants will gain engineering skills in power system modeling, smart grid analytics, machine learning, and LLM-based decision tools. These skills will be applied through simulations, data analysis, and the development of intelligent control or diagnostic methods relevant to modern urban energy infrastructures. Soft skills will be built through report writing, presentations, and potential conference/journal publications. This work extends prior REU efforts supervised by Dr. Tang in intelligent and data-driven energy systems.