Date and Time:
In this talk, we will present our recent work on two fundamental optimization problems in electric power systems, namely the optimal power flow (OPF) problem and the unit commitment (UC) problem. The first one is highly non-convex, and the second one is subject to significant uncertainty. In the first-part of the talk, we will present three strong second-order cone programming (SOCP) relaxations for solving large-scale AC OPF problems. Extensive computational experiments show that these relaxations have numerous advantages over existing convex relaxations in the literature: their solution quality is extremely close to that of the standard SDP relaxation; their solutions can be directly used to obtain a high quality feasible OPF solution, and they can be solved an order of magnitude faster than the standard SDP relaxation. In the second-part of the talk, we will present our recent work on multistage robust optimization for the UC problem. We propose simplified affine control policies and develop efficient constraint generation algorithms that solve real-world power systems of more than 3000 buses in a time framework suitable for today's industry practice. Extensive computational experiments show that the multistage robust UC model significantly improves economic and reliability performance over existing operational models for power systems with high penetration of renewable energy.
Andy Sun is an assistant professor in the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. He received B.E. degree from Tsinghua University in Electronic Engineering and a doctoral degree from Massachusetts Institute of Technology in Operations Research. He was a postdoctoral researcher at IBM Thomas J. Watson Research Center from 2011-2012. His research interests are in the areas of robust and stochastic optimization, large-scale non-convex optimization, and mathematical modeling of electric power systems. Dr. Sun has been working with leading utility companies such as the ISO New England on power system optimization problems.