Decentralized Stochastic Recursive Gradient Method for Fully Decentralized OPF in Multi-Area Power Systems

被引：14

作者：

Hussan, Umair ^{[1
]}

Wang, Huaizhi ^{[1
]}

Ayub, Muhammad Ahsan ^{[2
]}

Rasheed, Hamna ^{[2
]}

Majeed, Muhammad Asghar ^{[3
]}

Peng, Jianchun ^{[1
]}

Jiang, Hui ^{[2
]}

机构：

[1] Shenzhen Univ, Coll Mech & Control Engn, Shenzhen 518000, Peoples R China

[2] Shenzhen Univ, Coll Phys & Optoelect Engn, Minist Educ, Shenzhen 518000, Peoples R China

[3] Chulalongkorn Univ, Fac Engn, Dept Elect Engn, Bangkok 10330, Pathumwan, Thailand

来源：

MATHEMATICS | 2024年 / 12卷 / 19期

基金：

中国国家自然科学基金;

关键词：

decentralized stochastic recursive gradient (DSRG); optimal power flow (OPF); decentralized operation; multi-area power system; ALTERNATING DIRECTION METHOD; FLOW; DISPATCH; ALGORITHM;

D O I：

10.3390/math12193064

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This paper addresses the critical challenge of optimizing power flow in multi-area power systems while maintaining information privacy and decentralized control. The main objective is to develop a novel decentralized stochastic recursive gradient (DSRG) method for solving the optimal power flow (OPF) problem in a fully decentralized manner. Unlike traditional centralized approaches, which require extensive data sharing and centralized control, the DSRG method ensures that each area within the power system can make independent decisions based on local information while still achieving global optimization. Numerical simulations are conducted using MATLAB (Version 24.1.0.2603908) to evaluate the performance of the DSRG method on a 3-area, 9-bus test system. The results demonstrate that the DSRG method converges significantly faster than other decentralized OPF methods, reducing the overall computation time while maintaining cost efficiency and system stability. These findings highlight the DSRG method's potential to significantly enhance the efficiency and scalability of decentralized OPF in modern power systems.

引用

页数：16

共 39 条

[1] Optimal energy management of MG for cost-effective operations and battery scheduling using BWO [J].