A feasible reduced space method for real-time optimal power flow

We propose a novel feasible-path algorithm to solve the optimal power flow (OPF) problem for real-time use cases. The method augments seminal work of Dommel and Tinney with second-order derivatives directly in reduced space induced by equations. In space, optimization includes only inequality constraints corresponding operational constraints. While formulation enforces physical constraints, are softly enforced through Augmented Lagrangian penalty terms. contrast interior-point algorithms (state-of-the art solving OPF), our maintains feasibility at each iteration, which makes it suitable application. By exploiting accelerator hardware (Graphic Processing Units) compute Hessian, we show that is numerically tractable effective both static OPF problems. • A reduced-space ported on GPU. Combined an augmented algorithm, allows track solution setting. design, iterates computed satisfy Extensive numerical experiments effectiveness method. An open-source implementation provided reproduce results presented.