Robust Short-Term Operation of AC Power Network With Injection Uncertainties

With uncertain injections from Renewable Energy Sources (RESs) and loads, deterministic AC Optimal Power Flow (OPF) often fails to provide optimal setpoints of conventional generators. A computationally time-efficient, economical, and robust solution for ACOPF with short-term injection uncertainties is essential. Robust Optimization (RO), when used for the conventional non-linear ACOPF, may result in a computationally intractable Robust Counterpart (RC), which is undesirable as ACOPF is an operational problem. Hence, this article proposes a single-stage, non-integer, non-recursive, RC of ACOPF using a dual transformation for short-term injection uncertainties. The proposed RC is convex, tractable, and provides base-point active power generations and terminal voltage magnitudes (setpoints) of conventional generators that satisfy all constraints for all realizations of considered injection uncertainties. The non-linear impact of uncertainties on other variables is inherently modeled. The budget of uncertainty constraints are also considered for low conservatism of the obtained setpoints. Monte-Carlo Simulation (MCS) based participation factored AC power flows validate the robustness of the setpoints on NESTA and 9241pegase systems for different injection uncertainties. Comparison with previous approaches indicates the efficacy of the proposed approach in terms of low cost, robustness, and computation time.