Convex Relaxation of Optimal Power Flow-Part I: Formulations and Equivalence

被引：817

作者：

Low, Steven H. ^{[1
]}

机构：

[1] CALTECH, EAS, Pasadena, CA 91125 USA

来源：

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS | 2014年 / 1卷 / 01期

基金：

美国国家科学基金会;

关键词：

Convex relaxation; optimal power flow; power systems; quadratically constrained quadratic program (QCQP); second-order cone program (SOCP); semidefinite program (SDP); semidefinite relaxation;

D O I：

10.1109/TCNS.2014.2309732

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 [计算机科学与技术];

摘要：

This tutorial summarizes recent advances in the convex relaxation of the optimal power flow (OPF) problem, focusing on structural properties rather than algorithms. Part I presents two power flow models, formulates OPF and their relaxations in each model, and proves equivalence relationships among them. Part II presents sufficient conditions under which the convex relaxations are exact.

引用

页码：15 / 27

页数：13

共 67 条

[1]

FURTHER DEVELOPMENTS IN LP-BASED OPTIMAL POWER FLOW [J].