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

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.

arXiv commentAccepted for pubication in the IEEE Transactions on Power Systems

References in corpus (2)

• Decision making under uncertainty in energy systems: state of the art
• Ensemble Learning Based Convex Approximation of Three-Phase Power Flow

Cited by in corpus (1)

• Optimal Operation of Power Systems with Energy Storage under Uncertainty: A Scenario-based Method with Strategic Sampling