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Optimization of distributed generation using mix-integer optimization by genetic algorithm (MIOGA) Considering Load Growth

In this paper, the planning of distributed generation (DG) is presented with a metaheuristic technique called mix-integer optimization by genetic algorithm (MIOGA). The solution of the distribution power flow is based on the backward/forward sweep method to compute the voltage at every node of the b...

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Main Authors: Che Muhamad Asad Safwan, Che Aziz, Norhafidzah, Mohd Saad, Mohammad Fadhil, Abas, Suliana, Ab Ghani, Ali, Abid
Format: Conference or Workshop Item
Language:English
English
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/39524/1/Optimization%20of%20Distributed%20Generation%20Using%20Mix-Integer.pdf
http://umpir.ump.edu.my/id/eprint/39524/2/Optimization%20of%20distributed%20generation%20using%20mix-integer%20optimization%20by%20genetic%20algorithm%20%28MIOGA%29%20Considering%20Load%20Growth_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39524/
https://doi.org/10.1007/978-981-16-8690-0_23
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spelling my.ump.umpir.395242023-12-06T03:09:04Z http://umpir.ump.edu.my/id/eprint/39524/ Optimization of distributed generation using mix-integer optimization by genetic algorithm (MIOGA) Considering Load Growth Che Muhamad Asad Safwan, Che Aziz Norhafidzah, Mohd Saad Mohammad Fadhil, Abas Suliana, Ab Ghani Ali, Abid T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering In this paper, the planning of distributed generation (DG) is presented with a metaheuristic technique called mix-integer optimization by genetic algorithm (MIOGA). The solution of the distribution power flow is based on the backward/forward sweep method to compute the voltage at every node of the buses followed by the determination of power loss. The main idea of the proposed method is to determine the size and location for the DG to be installed in the radial distribution network (RDN). The method is tested in 69 bus RDN in MATLAB. From the simulation results, the reduction in total power loss and improvement in bus voltage magnitudes are observed for the system with the installation of DG. The results show that power loss can be reduced up to 63.03% with DG installation at bus 61 at 1.8727 MW. Apart from the reductions in losses, the installation of DG using MIOGA also helps to improve the voltage profile of the RDN. The critical bus voltage at bus 65 has successfully been improved from 0.9092 p.u. to 0.9806 p.u. The results indicate that load growth has no effect on the optimal position, and only the optimal size of the DG unit is changed. The results also reveal that load growth will increase the power losses. Since the DG in this study solely supplies active power, the impact of DG in reducing power losses is more visible for the case real power demand is increased rather than the case when the reactive power demand is increased. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39524/1/Optimization%20of%20Distributed%20Generation%20Using%20Mix-Integer.pdf pdf en http://umpir.ump.edu.my/id/eprint/39524/2/Optimization%20of%20distributed%20generation%20using%20mix-integer%20optimization%20by%20genetic%20algorithm%20%28MIOGA%29%20Considering%20Load%20Growth_ABS.pdf Che Muhamad Asad Safwan, Che Aziz and Norhafidzah, Mohd Saad and Mohammad Fadhil, Abas and Suliana, Ab Ghani and Ali, Abid (2022) Optimization of distributed generation using mix-integer optimization by genetic algorithm (MIOGA) Considering Load Growth. In: Lecture Notes in Electrical Engineering; 6th International Conference on Electrical, Control and Computer Engineering, InECCE 2021, 23 August 2021 , Kuantan, Pahang. pp. 245-255., 842 (274719). ISSN 1876-1100 ISBN 978-981168689-4 https://doi.org/10.1007/978-981-16-8690-0_23
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Che Muhamad Asad Safwan, Che Aziz
Norhafidzah, Mohd Saad
Mohammad Fadhil, Abas
Suliana, Ab Ghani
Ali, Abid
Optimization of distributed generation using mix-integer optimization by genetic algorithm (MIOGA) Considering Load Growth
description In this paper, the planning of distributed generation (DG) is presented with a metaheuristic technique called mix-integer optimization by genetic algorithm (MIOGA). The solution of the distribution power flow is based on the backward/forward sweep method to compute the voltage at every node of the buses followed by the determination of power loss. The main idea of the proposed method is to determine the size and location for the DG to be installed in the radial distribution network (RDN). The method is tested in 69 bus RDN in MATLAB. From the simulation results, the reduction in total power loss and improvement in bus voltage magnitudes are observed for the system with the installation of DG. The results show that power loss can be reduced up to 63.03% with DG installation at bus 61 at 1.8727 MW. Apart from the reductions in losses, the installation of DG using MIOGA also helps to improve the voltage profile of the RDN. The critical bus voltage at bus 65 has successfully been improved from 0.9092 p.u. to 0.9806 p.u. The results indicate that load growth has no effect on the optimal position, and only the optimal size of the DG unit is changed. The results also reveal that load growth will increase the power losses. Since the DG in this study solely supplies active power, the impact of DG in reducing power losses is more visible for the case real power demand is increased rather than the case when the reactive power demand is increased.
format Conference or Workshop Item
author Che Muhamad Asad Safwan, Che Aziz
Norhafidzah, Mohd Saad
Mohammad Fadhil, Abas
Suliana, Ab Ghani
Ali, Abid
author_facet Che Muhamad Asad Safwan, Che Aziz
Norhafidzah, Mohd Saad
Mohammad Fadhil, Abas
Suliana, Ab Ghani
Ali, Abid
author_sort Che Muhamad Asad Safwan, Che Aziz
title Optimization of distributed generation using mix-integer optimization by genetic algorithm (MIOGA) Considering Load Growth
title_short Optimization of distributed generation using mix-integer optimization by genetic algorithm (MIOGA) Considering Load Growth
title_full Optimization of distributed generation using mix-integer optimization by genetic algorithm (MIOGA) Considering Load Growth
title_fullStr Optimization of distributed generation using mix-integer optimization by genetic algorithm (MIOGA) Considering Load Growth
title_full_unstemmed Optimization of distributed generation using mix-integer optimization by genetic algorithm (MIOGA) Considering Load Growth
title_sort optimization of distributed generation using mix-integer optimization by genetic algorithm (mioga) considering load growth
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/39524/1/Optimization%20of%20Distributed%20Generation%20Using%20Mix-Integer.pdf
http://umpir.ump.edu.my/id/eprint/39524/2/Optimization%20of%20distributed%20generation%20using%20mix-integer%20optimization%20by%20genetic%20algorithm%20%28MIOGA%29%20Considering%20Load%20Growth_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39524/
https://doi.org/10.1007/978-981-16-8690-0_23
_version_ 1822923910697451520
score 13.232414

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