Recent Research in Power System Optimization: Approximation Error Quantification, Feasible Space Computation, and Convex Relaxations
|Event Date:||October 19, 2017|
|Speaker Affiliation:||Argonne National Laboratory
Energy Systems Division
|Contact Name:||Professor Dionysios Aliprantis
|School or Program:||Electrical and Computer Engineering
The power flow equations model the relationship between the voltages phasors and power flows in an electric power system. 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 focuses on three recent developments relevant to the power flow equations. By leveraging advances in convex relaxation techniques, this presentation first describes recent progress regarding a method for bounding the worst-case errors resulting from power flow linearizations. Using another new algorithm based on homotopy methods, this presentation next illustrates challenging feasible spaces associated with power system optimization problems. Finally, this presentation describes recent research using convex "moment" relaxations to find the global optima of many power system optimization problems.
Daniel Molzahn is a computational engineer at Argonne National Laboratory in the Center for Energy, Environmental, and Economic Systems Analysis. 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 the development of optimization and control techniques for electric power systems.