A multi-objective chaotic particle swarm optimization for environmental/economic dispatch

被引：141

作者：

Cai, Jiejin ^{[1
]}

Ma, Xiaoqian ^{[2
]}

Li, Qiong ^{[3
]}

Li, Lixiang ^{[4
]}

Peng, Haipeng ^{[4
]}

机构：

[1] Univ Tokyo, Sch Engn, Tokyo 1138656, Japan

[2] S China Univ Technol, Elect Power Coll, Guangzhou 510640, Peoples R China

[3] S China Univ Technol, Res Ctr Bldg Energy Efficiency, State Key Lab Subtrop Bldg Sci, Guangzhou 510640, Peoples R China

[4] Beijing Univ Posts & Telecommun, Informat Secur Ctr, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China

来源：

ENERGY CONVERSION AND MANAGEMENT | 2009年 / 50卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Chaotic particle swarm optimization; Environmental/economic dispatch; Multi-objective optimization; Swarm intelligence; EMISSION LOAD DISPATCH; ECONOMIC-DISPATCH; DYNAMIC DISPATCH; COST; PSO;

D O I：

10.1016/j.enconman.2009.01.013

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

A multi-objective chaotic particle swarm optimization (MOCPSO) method has been developed to solve the environmental/economic dipatch (EED) problems considering both economic and environmental issues. The proposed MOCPSO method has been applied in two test power systems. Compared with the conventional multi-objective particle swarm optimization (MOPSO) method, for the compromising minimum fuel cost and emission case, the fuel cost and pollutant emission obtained from MOCPSO method can be reduced about 50.08 $/h and 2.95 kg/h, respectively, in test system 1, about 0.02 $/h and 1.11 kg/h, respectively, in test system 2. The MOCPSO method also results in higher quality solutions for the minimum fuel cost case and the minimum emission case in both of the test power systems. Hence, MOCPSO method can result in great environmental and economic effects. For EED problems, the MOCPSO method is more feasible and more effective alternative approach than the conventional MOPSO method. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：1318 / 1325

页数：8

共 29 条

[1] A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch [J].