Reachable Optimal Power Flow

The uncertainty introduced by inverter-based resources (IBRs) increasingly challenges secure and economic power system operation, as conventional deterministic optimal power flow (OPF) cannot rigorously capture the full range of optimal operating states. To address this, this paper devises a reachable OPF (ReachOPF) framework that reformulates OPF under uncertainty as a reachability problem of an augmented continuous-time ordinary differential equation (ODE) system. By embedding the OPF constraints into a preconditioned augmented-Lagrangian flow with residual filtering and parameter-as-state modeling, ReachOPF enables one-shot propagation of uncertainty through zonotopic reachable-set computation instead of repeated sampling. Case studies on 3-bus and 30bus systems show that ReachOPF achieves guaranteed coverage of all Monte Carlo solutions with significantly reduced computational cost, demonstrating its rigor, scalability, and suitability for uncertainty-aware OPF analysis.