Uncertainty-Aware Three-Phase Optimal Power Flow Based on Data-Driven Convexification

This letter presents a novel optimization framework of formulating the three-phase optimal power flow that involves uncertainty. The proposed uncertainty-aware (UaO) is: 1) deterministic is less complex than existing frameworks involving uncertainty, and 2) convex such it admits polynomial-time algorithms mature distributed methods. To construct this UaO framework, methodology learning-aided modeling, with prediction errors stochastic variables as measurement theory data-driven convexification are proposed. Theoretically, applicable for modeling general problems under