State-Aware Stochastic Optimal Power Flow

The increase in distributed generation (DG) and variable load mandates system operators to perform decision-making considering uncertainties. This paper introduces a novel state-aware stochastic optimal power flow (SA-SOPF) problem formulation. proposed SA-SOPF has objective find day-ahead base-solution that minimizes the cost expectation of deviations node voltage set-points during real-time operation. We formulate for given affine policy employ Gaussian process learning obtain distributionally robust (DR) set-point change real-time. In simulations, GP-based shown distributional robustness over three different uncertainty distributions IEEE 14-bus system. results also depict SA-OPF formulation can reduce deviation more than 60% operation with an additional scheduling 4.68% only For, 30-bus system, reduction deviation, is achieved be greater 90% 1.195% extra cost. These are strong indicators possibility achieving solution which lead lower minimal increase.