Multi-area economic dispatch in bulk system using self-learning Cuckoo search algorithm

Khai Phuc Nguyen, Goro Fujita

研究成果: Conference contribution

1 引用 (Scopus)

抜粋

This paper proposes the Self-learning Cuckoo search algorithm to solve Multi-Area Economic Dispatch problems. The main objective of multi-area economic dispatch is to minimize the total fuel cost while satisfying balanced-power constraint in each area and limitations of generators and transmission lines. In addition, the proposed method is an improvement of the Cuckoo search algorithm with a new strategy to enhance Cuckoo eggs. The Cuckoo eggs will learn together to give the better solutions. The proposed method has been evaluated on two case studies of MAED to investigate the efficiency. Numerical results show that the proposed method is better than the conventional Cuckoo search algorithm and other methods in literature. However, in large-scale system, the computational time is slower than other methods.

元の言語English
ホスト出版物のタイトル2017 52nd International Universities Power Engineering Conference, UPEC 2017
出版者Institute of Electrical and Electronics Engineers Inc.
ページ1-6
ページ数6
2017-January
ISBN(電子版)9781538623442
DOI
出版物ステータスPublished - 2017 12 19
イベント52nd International Universities Power Engineering Conference, UPEC 2017 - Heraklion, Crete, Greece
継続期間: 2017 8 282017 8 31

Other

Other52nd International Universities Power Engineering Conference, UPEC 2017
Greece
Heraklion, Crete
期間17/8/2817/8/31

    フィンガープリント

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

これを引用

Nguyen, K. P., & Fujita, G. (2017). Multi-area economic dispatch in bulk system using self-learning Cuckoo search algorithm. : 2017 52nd International Universities Power Engineering Conference, UPEC 2017 (巻 2017-January, pp. 1-6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UPEC.2017.8232028