Clustering-based Decentralized Optimization Approaches for DC Optimal Power Flow

被引：0

作者：

Zhang, Kai ^{[1
]}

Hanif, Sarmad ^{[1
]}

Recalde, Dante ^{[1
]}

机构：

[1] TUM CREATE Ltd, 10-02 CREATE Tower, Singapore 138602, Singapore

来源：

2017 IEEE INNOVATIVE SMART GRID TECHNOLOGIES - ASIA (ISGT-ASIA) | 2017年

基金：

新加坡国家研究基金会;

关键词：

Decentralized control; DC-OPF; ADMM; KKT; Clustering;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This paper studies two decentralized scheme to solve DC optimal power flow (DC-OPF). The first scheme considers the decomposition of DC-OPF based upon augmented Lagrangian relaxation and uses alternating direction method of multipliers (ADMM) algorithm to solve the consensus optimization problem. An adaptive penalty method is proposed for the ADMM algorithm to improve the convergence performance. The second scheme utilizes Karush-Kuhn-Tucker (KKT) conditions and solves the coupled linear equations of DC-OPF directly. We show the impact of different cluster formations on both schemes. Both schemes are evaluated in terms of flexibility, robustness and iteration time using the IEEE 14-bus test system.

引用

页码：110 / 115

页数：6

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