Speaker

Dr. Rabab Haider, Assistant Professor, Civil & Environmental Engineering, University of Michigan

Abstract

Power grids are rapidly decarbonizing to address climate challenges and expanding to meet increasing demand from electrification and datacenter growth. With the U.S. electric grid projected to have an accelerated 1-2% annual load growth, power grids face the unprecedented challenge of scaling secure and resilient infrastructure, and to do so amidst a changing supply mix and complex market dynamics. These challenges translate directly to power systems decision-making: to ensure there is sufficient generation capacity to meet demand, transmission capacity to deliver power to load centers, and operational flexibility to accommodate diverse load patterns and intermittent renewable generation. All of these problems must be solved at an industry-scale of tens of thousands of buses, which can be challenging for traditional algorithms. This talk will explore our recent efforts in developing scalable and robust optimization and ML algorithms for power systems planning and operations, deploying techniques including warm-starts, trust-region regularizations, physics-informed machine learning, and optimization proxies. 

Bio

Dr. Rabab Haider is an Assistant Professor of Civil and Environmental Engineering at the University of Michigan. Her research is centered on designing future energy systems that provide green, reliable, and affordable energy for All. Her group develops optimization and AI algorithms that advance energy system operations, planning, and market under deep decarbonization. Dr. Haider’s portfolio also includes engagement with multiple global organizations to enable widespread access to STEMM education, mentorship, and leadership training. She received her Ph.D. and S.M. degrees at MIT, and B.A.Sc in Engineering Science at the University of Toronto. She was previously named a MIT Energy Fellow and MathWorks-MIT Mechanical Engineering Fellow. Dr. Haider is also an Affiliate Faculty at the Georgia Institute of Technology and NSF AI Institute for Advances in Optimization (AI4OPT).

Location

In-Person: 3130 ECE Conference Room

Online (MS Teams): ID: 236 085 004 635 7, Passcode: Zm9MS93n