论文标题
基于数据驱动的凸面，不确定性感知的三相最佳功率流
Uncertainty-aware Three-phase Optimal Power Flow based on Data-driven Convexification
论文作者
论文摘要
本文提出了一个新的优化框架，该框架是制定涉及不确定性的三相最佳功率流。提出的不确定性感知优化（UAO）框架是：1）确定性框架不如涉及不确定性的现有优化框架复杂，而2）凸音，使其接收多项式时算法和成熟的分布式优化方法。为了构建此UAO框架，一种学习辅助的不确定性意识建模的方法论，并提出了随机变量的预测错误，作为不确定性的测量，并提出了数据驱动的凸化理论。从理论上讲，UAO框架适用于对不确定性下的一般优化问题进行建模。
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.
