February 23, 2018, Daniel Molzahn, Argonne National Laboratory

Friday, February 23, 2018, 3pm-4pm
8 St. Mary’s Street, PHO 211
Refreshments at 2:45pm

Daniel Molzahn
Argonne National Laboratory

Recent Research in Power System Optimization: Feasible Space Computation and Approximation Error Qualification 

The power flow equations model the relationship between voltages phasors and power flows and are therefore at the heart of many optimization and control problems relevant to electric power systems. The nonlinearity of the power flow equations results in a variety of algorithmic and theoretical challenges, including non-convex feasible spaces for optimization problems constrained by these equations. This presentation first focuses on two recent developments relevant to the power flow equations. Using new algorithms, this presentation illustrates and characterizes challenging feasible spaces associated with power system optimization problems. Next, this presentation describes a newly developed algorithm that characterizes the largest possible error in the power flows predicted by various approximations and presents a method for adaptively computing better approximations

Daniel Molzahn is a computational engineer at Argonne National Laboratory. Prior to his current position, Daniel was a Dow Sustainability Fellow at the University of Michigan. Daniel received the B.S., M.S. and Ph.D. degrees in Electrical Engineering and the Masters of Public Affairs degree from the University of Wisconsin–Madison, where he was a National Science Foundation Graduate Research Fellow. His research interests are in applications of optimization techniques and control theory to electric power systems.

Faculty Host: Michael Caramanis
Student Host: Sean Sanchez