An improved binary particle swarm optimization for unit commitment problem

被引：156

作者：

Yuan, Xiaohui ^{[1
]}

Nie, Hao ^{[1
]}

So, Anjun ^{[1
]}

Wang, Liang ^{[1
]}

Yuan, Yanbin ^{[2
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Hydropower & Informat Engn, Wuhan 430074, Peoples R China

[2] Wuhan Univ Technol, Sch Resources & Environm Engn, Wuhan 430070, Peoples R China

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2009年 / 36卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Unit commitment; Priority list; Particle swarm optimization; Heuristic search; LAGRANGIAN-RELAXATION; ALGORITHM;

D O I：

10.1016/j.eswa.2008.10.047

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a new improved binary PSO (IBPSO) method to solve the unit commitment (UC) problem, which is integrated binary particle swarm optimization (BPSO) with lambda-iteration method. The IBPSO is improved by priority list based on the unit characteristics and heuristic search strategies to repair the spinning reserve and minimum up/down time constraints. To verify the advantages of the IBPSO method, the IBPSO is tested and compared to the other methods on the systems with the number of units in the range of 10-100. Numerical results demonstrate that the IBPSO is Superior to other methods reported in the literature in terms of lower production cost and shorter computational time. (c) 2008 Elsevier Ltd. All rights reserved.

引用

页码：8049 / 8055

页数：7

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