LDRD Seminar Series: ‘Optimal Power Flow with Robust Feasibility Guarantees’
Computational Engineer Daniel Kenneth Molzahn (ES) will discuss his Laboratory-Directed Research and Development (LDRD) sponsored work at the LDRD Seminar Series presentation Tuesday, April 17, 2018. “Optimal Power Flow with Robust Feasibility Guarantees” begins at 12:30 p.m. in the Building 203 Auditorium. All are welcome to attend.
An algorithm for optimizing the operation of electric power systems while considering uncertainty from renewable generation.
Optimal power flow (OPF) is an important problem in the operation of electric power systems. The solution to an OPF problem provides a minimum cost operating point that satisfies both engineering limits and the power flow equations corresponding to the network physics. A variety of applications, such as distribution grid optimization and transmission grid security assessment, call for network models that use the nonlinear “AC” power flow equations. Additionally, with growing penetrations of renewable generation, OPF problems are increasingly influenced by the forecast uncertainty and short-term fluctuations that are inherent to many renewable energy sources. Thus, reliable and efficient operation of power systems requires the solution of non-convex “AC OPF” problems that incorporate uncertainty.
This presentation describes a recently proposed iterative algorithm for the AC OPF problem that yields a solution with robust feasibility guarantees. The algorithm is based on the observation that considering uncertainty leads to a tightening of the original, deterministic constraints in order to safely accommodate fluctuations due to uncertain generation. The main challenge in solving the robust AC OPF problem is to guarantee the existence of feasible solutions for all points within the uncertainty set. To overcome this challenge, the proposed algorithm employs convex relaxations of the AC power flow equations to obtain a conservative estimate of the required tightenings. The presentation provides a detailed description of the algorithm and results showing the robustness of the proposed approach.
Daniel Molzahn is a computational engineer in the Energy Systems Division. Prior to his current position, Molzahn was a Dow Sustainability Fellow at the University of Michigan. He received his B.S., M.S. and Ph.D. degrees in electrical engineering and a 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.