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Uncertainty-aware Three-phase Optimal Power Flow based on Data-driven Convexification
- Publication Year :
- 2020
-
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.<br />Comment: Accepted for pubication in the IEEE Transactions on Power Systems
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2005.13075
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/TPWRS.2021.3050926