Multi-Objective Optimal DG Sizing and Placement in Distribution Systems Using Particle Swarm Optimization

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

The radial structure of conventional distribution systems exposes electricity consumers to unavoidable downtimes. Downtimes spell-out outage cost to both utility providers and consumers. These downtimes can be mitigated through operation of tie-switch, vis-à-vis optimal sizing of DGs at strategic locations. This paper proposes a new approach to the problem of optimal sizing and location of DGs for optimizing benefit-cost analysis of DG installation and the total power losses during failures. The proposed approach considers the worst-case contingency scenarios of a distribution system with possible restoration via a tie-switch while meeting voltage constraints. A non-traditional multi-objective particle swarm optimization is developed for the system. The forward and backward propagation power flow analysis method is used. This approach is applied to the Roy Billinton Test System. The results demonstrate the effectiveness of the proposed method in obtaining the optimal size and location of DG in a distribution system.

Original languageEnglish
Title of host publication2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1857-1861
Number of pages5
ISBN (Electronic)9781728135205
DOIs
StatePublished - May 2019

Publication series

Name2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Distributed Generation
  • forward and backward propagation
  • particle swarm optimization
  • power losses

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Control and Optimization

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