期刊
ENERGY
卷 38, 期 1, 页码 346-353出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2011.11.052
关键词
Economic dispatch; Fuzzy adaptive chaotic ant swarm optimization; Sequential quadratic programming; Evolutionary algorithm; Power system
资金
- Foundation for the Author of National Excellent Doctoral Dissertation of China (FANEDD) [200951]
- National Natural Science Foundation of China [61070209, 51008127]
- Fok Ying-Tong Education Foundation for Young Teachers in the Higher Education Institutions of China [121062]
- Natural Science Foundation of Guangdong Province of China [10451064101005823]
- Program for New Century Excellent Talents in University [NCET-10-0846]
- Fundamental Research Funds for the Central Universities [11Igpy90, 2012ZZ0068, BUPT2011RC0211]
- Specialized Research Fund for the Doctoral Program of Higher Education [20100172120006]
- State Key Laboratory of Subtropical Building Science, South China University of Technology, China [2011ZC20]
- One Hundred Talents Plan Funds
In this paper, a hybrid method integrating the fuzzy adaptive chaotic ant swarm optimization (FCASO) algorithm and the sequential quadratic programming (SQP) techniques, named FCASO-SQP method, is presented for solving the economic dispatch (ED) problems in power systems. The FCASO algorithm is the main optimizer in the hybrid method and the SQP technique is used to fine tune its results to improve the solution. The FCASO algorithm introduces a fuzzy system to dynamically tune the characteristic parameters psi(d) and r(i) of chaotic ant swarm optimization (CASO). The proposed method was applied to three different cases of power systems with three units, thirteen units and forty units, and the simulation results demonstrate its applicability and effectiveness to solve the ED problems with valve-point effect. (C) 2011 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据
分享论文
