联系人：吴文传
Abstract:
In this talk, I will present recent advances in solving large-scale optimization problems with non-convexity and multi-stage decision structure. In the first part of the talk, I will present strong second-order cone programming (SOCP) relaxation techniques for solving large-scale non-convex quadratic constrained quadratic programming problems over graphs and their application in solving the Optimal Power Flow in electric power systems. Extensive experiments show the proposed SOCP relaxations produce very high quality solutions compared to the traditional SDP relaxations and are orders of magnitude faster. In the second part of the talk, I will present our recent work on multistage robust and stochastic optimization problems. We propose simplified affine control policies and develop efficient constraint generation algorithms that can solve multistage robust UC problems in real-world power systems in a time framework suitable for today’s industry practice.

Bio:
Andy Sun is an assistant professor in the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology. He received bachelor 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 before joining Georgia Tech. His research interests are in the areas of robust and stochastic optimization, large-scale non-convex optimization, and mathematical modeling of electric energy systems. Dr. Sun’s research has won several awards, including the INFORMS Georgia B. Dantzig Doctoral Dissertation Award, the INFORMS Junior Faculty Interests Group (JFIG) Paper Competition, and the INFORMS ENRE Best Publication in Energy. Dr. Sun has been working with several leading utility companies such as the California ISO, MISO, ISO New England, and the Southern Company on robust power system operations.