Advanced Pareto Front Non-Dominated Sorting Multi-Objective Particle Swarm Optimization for Optimal Placement and Sizing of Distributed Generation

This paper proposes an advanced Pareto-front non-dominated sorting multi-objective particle swarm optimization (Advanced-PFNDMOPSO) method for optimal configuration (placement and sizing) of distributed generation (DG) in the radial distribution system. The distributed generation consists of single...

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Main Authors: Kumar Mahesh, Perumal Nallagownden, Irraivan Elamvazuthi
Format: Article
Language:English
Published: MDPI AG 2016-11-01
Series:Energies
Subjects:
Online Access:http://www.mdpi.com/1996-1073/9/12/982
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spelling doaj-e6b3dcde34d94bd3a4ebf8e4743037912020-11-24T22:37:38ZengMDPI AGEnergies1996-10732016-11-0191298210.3390/en9120982en9120982Advanced Pareto Front Non-Dominated Sorting Multi-Objective Particle Swarm Optimization for Optimal Placement and Sizing of Distributed GenerationKumar Mahesh0Perumal Nallagownden1Irraivan Elamvazuthi2Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak Darul Ridzuan, MalaysiaDepartment of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak Darul Ridzuan, MalaysiaDepartment of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak Darul Ridzuan, MalaysiaThis paper proposes an advanced Pareto-front non-dominated sorting multi-objective particle swarm optimization (Advanced-PFNDMOPSO) method for optimal configuration (placement and sizing) of distributed generation (DG) in the radial distribution system. The distributed generation consists of single and multiple numbers of active power DG, reactive power DG and simultaneous placement of active-reactive power DG. The optimization problem considers two multi-objective functions, i.e., power loss reduction and voltage stability improvements with voltage profile and power balance as constraints. First, the numerical output results of objective functions are obtained in the Pareto-optimal set. Later, fuzzy decision model is engendered for final selection of the compromised solution. The proposed method is employed and tested on standard IEEE 33 bus systems. Moreover, the results of proposed method are validated with other optimization algorithms as reported by others in the literature. The overall outcome shows that the proposed method for optimal placement and sizing gives higher capability and effectiveness to the final solution. The study also reveals that simultaneous placement of active-reactive power DG reduces more power losses, increases voltage stability and voltage profile of the system.http://www.mdpi.com/1996-1073/9/12/982distributed generationplacement and sizingdistribution systempower loss reductionvoltage stabilitymulti-objective particle swarm optimization (PSO)non-dominated sorting
collection DOAJ
language English
format Article
sources DOAJ
author Kumar Mahesh
Perumal Nallagownden
Irraivan Elamvazuthi
spellingShingle Kumar Mahesh
Perumal Nallagownden
Irraivan Elamvazuthi
Advanced Pareto Front Non-Dominated Sorting Multi-Objective Particle Swarm Optimization for Optimal Placement and Sizing of Distributed Generation
Energies
distributed generation
placement and sizing
distribution system
power loss reduction
voltage stability
multi-objective particle swarm optimization (PSO)
non-dominated sorting
author_facet Kumar Mahesh
Perumal Nallagownden
Irraivan Elamvazuthi
author_sort Kumar Mahesh
title Advanced Pareto Front Non-Dominated Sorting Multi-Objective Particle Swarm Optimization for Optimal Placement and Sizing of Distributed Generation
title_short Advanced Pareto Front Non-Dominated Sorting Multi-Objective Particle Swarm Optimization for Optimal Placement and Sizing of Distributed Generation
title_full Advanced Pareto Front Non-Dominated Sorting Multi-Objective Particle Swarm Optimization for Optimal Placement and Sizing of Distributed Generation
title_fullStr Advanced Pareto Front Non-Dominated Sorting Multi-Objective Particle Swarm Optimization for Optimal Placement and Sizing of Distributed Generation
title_full_unstemmed Advanced Pareto Front Non-Dominated Sorting Multi-Objective Particle Swarm Optimization for Optimal Placement and Sizing of Distributed Generation
title_sort advanced pareto front non-dominated sorting multi-objective particle swarm optimization for optimal placement and sizing of distributed generation
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2016-11-01
description This paper proposes an advanced Pareto-front non-dominated sorting multi-objective particle swarm optimization (Advanced-PFNDMOPSO) method for optimal configuration (placement and sizing) of distributed generation (DG) in the radial distribution system. The distributed generation consists of single and multiple numbers of active power DG, reactive power DG and simultaneous placement of active-reactive power DG. The optimization problem considers two multi-objective functions, i.e., power loss reduction and voltage stability improvements with voltage profile and power balance as constraints. First, the numerical output results of objective functions are obtained in the Pareto-optimal set. Later, fuzzy decision model is engendered for final selection of the compromised solution. The proposed method is employed and tested on standard IEEE 33 bus systems. Moreover, the results of proposed method are validated with other optimization algorithms as reported by others in the literature. The overall outcome shows that the proposed method for optimal placement and sizing gives higher capability and effectiveness to the final solution. The study also reveals that simultaneous placement of active-reactive power DG reduces more power losses, increases voltage stability and voltage profile of the system.
topic distributed generation
placement and sizing
distribution system
power loss reduction
voltage stability
multi-objective particle swarm optimization (PSO)
non-dominated sorting
url http://www.mdpi.com/1996-1073/9/12/982
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AT irraivanelamvazuthi advancedparetofrontnondominatedsortingmultiobjectiveparticleswarmoptimizationforoptimalplacementandsizingofdistributedgeneration
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