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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Online Access: | http://www.mdpi.com/1996-1073/9/12/982 |
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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 |
work_keys_str_mv |
AT kumarmahesh advancedparetofrontnondominatedsortingmultiobjectiveparticleswarmoptimizationforoptimalplacementandsizingofdistributedgeneration AT perumalnallagownden advancedparetofrontnondominatedsortingmultiobjectiveparticleswarmoptimizationforoptimalplacementandsizingofdistributedgeneration AT irraivanelamvazuthi advancedparetofrontnondominatedsortingmultiobjectiveparticleswarmoptimizationforoptimalplacementandsizingofdistributedgeneration |
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