Reduction of the Optimal Power Flow Problem through Meta-Optimization (Papers Track)
Letif Mones (Invenia Labs); Alex Robson (Invenia Labs); Mahdi Jamei (Invenia Labs); Cozmin Ududec (Invenia Labs)
Abstract
We introduce a method for solving Optimal Power Flow (OPF) using meta-optimization, which can substantially reduce solution times. A pre-trained classifier that predicts the binding constraints of the system is used to generate an initial reduced OPF problem, defined by removing the predicted non-binding constraints. Through an iterative procedure, this initial set of constraints is then ex- tended by those constraints that are violated but not represented in the reduced OPF, guaranteeing an optimal solution of the original OPF problem with the full set of constraints. The classifier is trained using a meta-loss objective, defined by the computational cost of the series of reduced OPF problems.