ABSTRACT:

Demand response (DR) plays a key role in enhancing the flexibility, reliability, and economic efficiency of modern power systems. In this talk, I will present two methods to enhance DR effectiveness through location-targeted strategies. The first uses a machine learning approach to identify optimal DR locations based on historical locational marginal price (LMP) patterns. The second reformulates the AC optimal power flow (AC-OPF) problem as a multi-objective nonlinear optimization to jointly analyze cost and emissions. A nonlinear sensitivity analysis framework is developed to study how both LMPs and locational marginal emissions (LMEs) respond to load variations at specific nodes. Together, these methods provide a foundation for more intelligent and impactful DR and storage deployment in future power systems.

BIOGRAPHY:

Wangbo Tang is a first-year Ph.D. student in the Edwardson School of Industrial Engineering at Purdue University, advised by Professor Sivaranjani Seetharaman. He holds a master's degree in applied mathematics and computational science from the University of Pennsylvania and a B.S degree in Applied Mathematics from the University of California, Davis. Prior to joining Purdue, he worked as a Graduate Research Assistant at the Penn Wharton Budget Model, where he studied optimal carbon policy using parallel computing to solve large-scale overlapping generations models.