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

Khai Phuc Nguyen, Goro Fujita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2017 52nd International Universities Power Engineering Conference, UPEC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
Volume2017-January
ISBN (Electronic)9781538623442
DOIs
Publication statusPublished - 2017 Dec 19
Event52nd International Universities Power Engineering Conference, UPEC 2017 - Heraklion, Crete, Greece
Duration: 2017 Aug 282017 Aug 31

Other

Other52nd International Universities Power Engineering Conference, UPEC 2017
CountryGreece
CityHeraklion, Crete
Period17/8/2817/8/31

Keywords

  • Cuckoo search algorithm
  • Multi-area economic dispatch
  • multiple fuel cost functions
  • valve-point effects

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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