Uncertainty-aware Three-phase Optimal Power Flow based on Data-driven Convexification

TL;DR

This paper introduces a data-driven, convex, and less complex optimization framework for three-phase optimal power flow that effectively manages uncertainty using learning-based models and convexification techniques.

Contribution

It proposes a novel uncertainty-aware optimization framework that is deterministic, convex, and suitable for polynomial-time algorithms, advancing power flow optimization under uncertainty.

Findings

Framework is less complex than existing methods.

Enables polynomial-time solutions for uncertain power flow.

Applicable to general optimization problems under uncertainty.

Abstract

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