Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems
This paper presents a hybrid approach based on the Genetic Algorithm (GA) and Moth Swarm Algorithm (MSA), namely Genetic Moth Swarm Algorithm (GMSA), for determining the optimal location and sizing of renewable distributed generation (DG) sources on radial distribution networks (RDN). Minimizing the...
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| Format: | Article |
| Language: | English |
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Universidad Internacional de La Rioja (UNIR)
2019-12-01
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| Series: | International Journal of Interactive Multimedia and Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.ijimai.org/journal/node/3529 |
| _version_ | 1818940197565366272 |
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| author | Emad Mohamed Al-Attar Ali Mohamed Yasunori Mitani |
| author_facet | Emad Mohamed Al-Attar Ali Mohamed Yasunori Mitani |
| author_sort | Emad Mohamed |
| collection | DOAJ |
| description | This paper presents a hybrid approach based on the Genetic Algorithm (GA) and Moth Swarm Algorithm (MSA), namely Genetic Moth Swarm Algorithm (GMSA), for determining the optimal location and sizing of renewable distributed generation (DG) sources on radial distribution networks (RDN). Minimizing the electrical power loss within the framework of system operation and under security constraints is the main objective of this study. In the proposed technique, the global search ability has been regulated by the incorporation of GA operations with adaptive mutation operator on the reconnaissance phase using genetic pathfinder moths. In addition, the selection of artificial light sources has been expanded over the swarm. The representation of individuals within the three phases of MSA has been modified in terms of quality and ratio. Elite individuals have been used to play different roles in order to reduce the design space and thus increase the exploitation ability. The developed GMSA has been applied on different scales of standard RDN of the (33 and 69-bus) power systems. Firstly, the most adequate buses for installing DGs are suggested using Voltage Stability Index (VSI). Then the proposed GMSA is applied to reduce real power generation, power loss, and total system cost, in addition, to improve the minimum bus voltage and the annual net saving by selecting the DGs size and their locations. Furthermore, GMSA is compared with other literature methods under several power system constraints and conditions, in single and multi-objective optimization space. The computational results prove the effectiveness and superiority of the GMSA with respect to power loss reduction and voltage profile enhancement using a minimum size of renewable DG units. |
| first_indexed | 2024-12-20T06:35:49Z |
| format | Article |
| id | doaj.art-54c0429bbd93450a8b2001c3a491f8f1 |
| institution | Directory Open Access Journal |
| issn | 1989-1660 1989-1660 |
| language | English |
| last_indexed | 2024-12-20T06:35:49Z |
| publishDate | 2019-12-01 |
| publisher | Universidad Internacional de La Rioja (UNIR) |
| record_format | Article |
| series | International Journal of Interactive Multimedia and Artificial Intelligence |
| spelling | doaj.art-54c0429bbd93450a8b2001c3a491f8f12022-12-21T19:50:01ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16601989-16602019-12-015710511710.9781/ijimai.2019.10.005ijimai.2019.10.005Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution SystemsEmad MohamedAl-Attar Ali MohamedYasunori MitaniThis paper presents a hybrid approach based on the Genetic Algorithm (GA) and Moth Swarm Algorithm (MSA), namely Genetic Moth Swarm Algorithm (GMSA), for determining the optimal location and sizing of renewable distributed generation (DG) sources on radial distribution networks (RDN). Minimizing the electrical power loss within the framework of system operation and under security constraints is the main objective of this study. In the proposed technique, the global search ability has been regulated by the incorporation of GA operations with adaptive mutation operator on the reconnaissance phase using genetic pathfinder moths. In addition, the selection of artificial light sources has been expanded over the swarm. The representation of individuals within the three phases of MSA has been modified in terms of quality and ratio. Elite individuals have been used to play different roles in order to reduce the design space and thus increase the exploitation ability. The developed GMSA has been applied on different scales of standard RDN of the (33 and 69-bus) power systems. Firstly, the most adequate buses for installing DGs are suggested using Voltage Stability Index (VSI). Then the proposed GMSA is applied to reduce real power generation, power loss, and total system cost, in addition, to improve the minimum bus voltage and the annual net saving by selecting the DGs size and their locations. Furthermore, GMSA is compared with other literature methods under several power system constraints and conditions, in single and multi-objective optimization space. The computational results prove the effectiveness and superiority of the GMSA with respect to power loss reduction and voltage profile enhancement using a minimum size of renewable DG units.http://www.ijimai.org/journal/node/3529genetic-moth swarm algorithmpower lossradial distribution systemrenewable distributed generation |
| spellingShingle | Emad Mohamed Al-Attar Ali Mohamed Yasunori Mitani Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems International Journal of Interactive Multimedia and Artificial Intelligence genetic-moth swarm algorithm power loss radial distribution system renewable distributed generation |
| title | Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems |
| title_full | Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems |
| title_fullStr | Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems |
| title_full_unstemmed | Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems |
| title_short | Genetic-Moth Swarm Algorithm for Optimal Placement and Capacity of Renewable DG Sources in Distribution Systems |
| title_sort | genetic moth swarm algorithm for optimal placement and capacity of renewable dg sources in distribution systems |
| topic | genetic-moth swarm algorithm power loss radial distribution system renewable distributed generation |
| url | http://www.ijimai.org/journal/node/3529 |
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