Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm

This paper presents a Genetic Algorithm (GA) for optimal location and sizing of multiple distributed generation (DG) for loss minimization. The study is implemented on a 33-bus radial distribution system to optimally allocate different numbers of DGs through the minimization of total active power lo...

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Main Authors: Musa A., Tengku Hashim T.J.
Other Authors: 57205693832
Format: Article
Published: Institute of Advanced Engineering and Science 2023
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spelling my.uniten.dspace-249452023-05-29T15:29:15Z Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm Musa A. Tengku Hashim T.J. 57205693832 55241766100 This paper presents a Genetic Algorithm (GA) for optimal location and sizing of multiple distributed generation (DG) for loss minimization. The study is implemented on a 33-bus radial distribution system to optimally allocate different numbers of DGs through the minimization of total active power losses and voltage deviation at power constraints of 0�2 MW and 0�3 MW respectively. The study proposed a PQ model of DG and Direct Load Flow (DLF) technique that uses Bus Incidence to Branch current (BIBC) and Branch Current to Bus Voltage (BCBV) matrices. The result obtained a minimum base case voltage level of 0.9898 p.u at bus 18 with variations of voltage improvements at other buses after single and multiple DG allocations in the system. Besides, the total power loss before DG allocation is observed as 0.2243 MW, and total power loss after DG allocation was determined based on the power constraints. Various optimal locations were seen depending on the power limits of different DG sizes. The results have shown that the impact of optimal allocation and sizing of three DG is more advantageous concerning voltage improvement, reduction of the voltage deviation and also total power loss in the distribution system. The results obtained in the 0�2 MW power limit is consistent to the 0�3 MW power limits regarding the influence of allocating DG to the network and minimization of total power losses. Copyright � 2019 Institute of Advanced Engineering and Science. All rights reserved. Final 2023-05-29T07:29:15Z 2023-05-29T07:29:15Z 2019 Article 10.11591/ijeecs.v16.i2.pp956-963 2-s2.0-85073502890 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073502890&doi=10.11591%2fijeecs.v16.i2.pp956-963&partnerID=40&md5=a8561f4882934df306b6e29c7fbfd041 https://irepository.uniten.edu.my/handle/123456789/24945 16 2 956 963 All Open Access, Hybrid Gold Institute of Advanced Engineering and Science Scopus
institution Universiti Tenaga Nasional
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description This paper presents a Genetic Algorithm (GA) for optimal location and sizing of multiple distributed generation (DG) for loss minimization. The study is implemented on a 33-bus radial distribution system to optimally allocate different numbers of DGs through the minimization of total active power losses and voltage deviation at power constraints of 0�2 MW and 0�3 MW respectively. The study proposed a PQ model of DG and Direct Load Flow (DLF) technique that uses Bus Incidence to Branch current (BIBC) and Branch Current to Bus Voltage (BCBV) matrices. The result obtained a minimum base case voltage level of 0.9898 p.u at bus 18 with variations of voltage improvements at other buses after single and multiple DG allocations in the system. Besides, the total power loss before DG allocation is observed as 0.2243 MW, and total power loss after DG allocation was determined based on the power constraints. Various optimal locations were seen depending on the power limits of different DG sizes. The results have shown that the impact of optimal allocation and sizing of three DG is more advantageous concerning voltage improvement, reduction of the voltage deviation and also total power loss in the distribution system. The results obtained in the 0�2 MW power limit is consistent to the 0�3 MW power limits regarding the influence of allocating DG to the network and minimization of total power losses. Copyright � 2019 Institute of Advanced Engineering and Science. All rights reserved.
author2 57205693832
author_facet 57205693832
Musa A.
Tengku Hashim T.J.
format Article
author Musa A.
Tengku Hashim T.J.
spellingShingle Musa A.
Tengku Hashim T.J.
Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm
author_sort Musa A.
title Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm
title_short Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm
title_full Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm
title_fullStr Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm
title_full_unstemmed Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm
title_sort optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm
publisher Institute of Advanced Engineering and Science
publishDate 2023
_version_ 1806426588964716544
score 13.214268