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  <title><![CDATA[PhD Defense by Haoruo Zhao ]]></title>
  <body><![CDATA[<p>Title: Advances in Large-Scale Security-Constrained Economic Dispatch: Loss Modeling, Stochastic Dispatch, and Proxy Verification<br>Date: April 16th, 2025<br>Time: 1:00 - 3:00 pm ET<br>Location: Coda C1215 Midtown (meeting link)</p><p>Haoruo Zhao<br>Operations Research PhD Student<br>H. Milton Stewart School of Industrial and Systems Engineering<br>Georgia Institute of Technology</p><p>Committee:<br>Dr. Pascal Van Hentenryck (Advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology<br>Dr. Constance Crozier, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology<br>Dr. Mathieu Dahan, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology<br>Dr. Daniel Molzahn, School of Electrical and Computer Engineering, Georgia Institute of Technology<br>Dr. Hassan Hijazi, Gurobi</p><p>Abstract:<br>Security-Constrained Economic Dispatch (SCED) optimizes generation resources to meet demand at minimal cost while maintaining grid reliability. This thesis presents novel optimization techniques for large-scale SCED problems that improve computational tractability and solution quality for modern power systems. Chapter 1 introduces a novel linear model for line loss outer approximation (LLOA) in DC optimal power flow, a critical component of SCED that models network constraints, providing an efficient and practical approach that balances accuracy and computational tractability for large-scale power systems. Chapter 2 develops a stochastic look-ahead dispatch (SLAD) framework for real-time energy markets, demonstrating that stochastic optimization is now computationally viable for five-minute market clearing, offering greater savings compared to the traditional deterministic formulation with flexible ramping products. Chapter 3 presents an optimization-based method for bound tightening in a rolling-horizon fashion for neural network verification and Chapter 4 introduces a compact formulation for optimality verification of optimization proxies for DC optimal power flow and knapsack problems.</p><p>&nbsp;</p>]]></body>
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