Multi population genetic algorithm for allocation and sizing of distributed generation

W. S. Tan, M. Y. Hassan, M. S. Majid

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

3 Citations (Scopus)

Abstract

Distributed generation has been becoming more well-known in the power sector due to its ability in power loss reduction, low investment cost, increase reliability, and most significantly, to exploit renewable-energy resources. The optimal placement and sizing of distributed generation are necessary for maximizing the distributed generation potential benefits in a power system. In this paper, a novel multi population-based genetic algorithm is proposed for optimal location and sizing of distributed generation in a radial distribution system. The objective is to minimize the total real power losses in the system and improve voltage stability within the voltage constrains. Both the optimal size and location are obtained as outputs from the genetic algorithm toolbox. An analysis is carried out on 30 bus systems and compare with the analytical method and standard genetic algorithm to verify the effectiveness of the proposed methodology. Results show that the proposed method is more efficient in power losses reduction compared to analytical method, also faster in convergence than standard genetic algorithm.

Original languageEnglish
Title of host publication2012 IEEE International Power Engineering and Optimization Conference, PEOCO 2012 - Conference Proceedings
Pages108-113
Number of pages6
DOIs
Publication statusPublished - 2012
Externally publishedYes
EventIEEE International Power Engineering and Optimization Conference (PEOCO) 2012 - Melaka, Malaysia
Duration: 6 Jun 20127 Jun 2012
https://ieeexplore.ieee.org/xpl/conhome/6222608/proceeding (Proceedings)

Conference

ConferenceIEEE International Power Engineering and Optimization Conference (PEOCO) 2012
Abbreviated titlePEOCO 2012
Country/TerritoryMalaysia
CityMelaka
Period6/06/127/06/12
Internet address

Keywords

  • distributed generation
  • Multi Population Genetic Algorithm
  • optimal location
  • radial distribution system

Cite this