{"id":106650,"date":"2025-02-19T15:14:47","date_gmt":"2025-02-19T21:14:47","guid":{"rendered":"https:\/\/engineering.wisc.edu\/?post_type=tribe_events&p=106650"},"modified":"2025-02-21T14:12:42","modified_gmt":"2025-02-21T20:12:42","slug":"neep-seminar-series-rabab-haider-university-of-michigan","status":"publish","type":"tribe_events","link":"https:\/\/engineering.wisc.edu\/event\/neep-seminar-series-rabab-haider-university-of-michigan\/","title":{"rendered":"NEEP Seminar: Rabab Haider, University of Michigan"},"content":{"rendered":"
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\n\t\t\n\t\t\tFebruary 27\t\t<\/span>\n\n\t\t\t\t\t\n\t\t\t\t @ \t\t\t<\/span>\n\t\t\t\n\t\t\t\t12:00 PM\t\t\t<\/span>\n\t\t\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t – \t\t\t\t<\/span>\n\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t1:00 PM\t\t\t\t<\/span>\n\t\t\t\n\t\t\t\t\t\t<\/h2>\n<\/div>\n\n\n\n


Thursday, February 27
12:00 – 1:00pm
ERB 106 (Contact office@ep.wisc.edu<\/a> for assistance with remote participation.) <\/p>\n\n\n\n

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Speaker:<\/strong> Rabab Haider, University of Michigan<\/p>\n\n\n\n

Title:<\/strong> Fast algorithms and equitable frameworks for renewable-rich power systems<\/p>\n\n\n\n

Abstract:<\/strong> Power grids are rapidly decarbonizing to address climate challenges and expanding to meet increasing demand from electrification and AI growth. However, integrating increasing quantities of intermittent renewable generation presents a challenge to how we operate power grids, reducing decision-making timescales from days to minutes. Digit twins layered with AI applications can provide much needed real-time visibility and control, such as network reconfiguration. Reconfiguration is vital for rapidly integrating more renewable generation while maintaining grid reliability, yet its large-scale, nonlinear nature is challenging for traditional algorithms. We will introduce the specialized use of physics-informed machine learning for fast and accurate reconfiguration decisions. In the second part of the talk, we will explore the implications of the changing power grid on electricity affordability \u2013 a tenet of energy equity. We propose a new framework based on locational marginal burden, which provides an interface between energy burden and the optimal power flow problem by leveraging tools from differentiable optimization.<\/p>\n\n\n\n

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Bio:<\/strong> Dr. Rabab Haider<\/a> 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\u2019s 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). <\/p>\n\n\n\n

This seminar is presented by the Institute for Nuclear Energy Systems<\/a> and the Nuclear Engineering & Engineering Physics Department<\/a>.<\/p>\n\n\n

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