A multi-agent-based symbiotic organism search algorithm for DG coordination in electrical distribution networks
Abstract Metaheuristic algorithms have become popular in solving engineering optimization problems due to their advantages of simple implementation and the ability to find near-optimal solutions for complex and large-scale problems. However, most applications of metaheuristic algorithms consider cen...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-01-01
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| Series: | Journal of Electrical Systems and Information Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43067-023-00072-7 |
| _version_ | 1811175981023494144 |
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| author | Shamte Kawambwa Daudi Mnyanghwalo |
| author_facet | Shamte Kawambwa Daudi Mnyanghwalo |
| author_sort | Shamte Kawambwa |
| collection | DOAJ |
| description | Abstract Metaheuristic algorithms have become popular in solving engineering optimization problems due to their advantages of simple implementation and the ability to find near-optimal solutions for complex and large-scale problems. However, most applications of metaheuristic algorithms consider centralized design, assuming that all possible solutions are available in one machine or controller. In some applications, such as power systems, especially DG coordination, centralized design may not be efficient. This work integrates a multi-agent system (MAS) into a metaheuristic algorithm for enhanced performance. In a proposed multi-agent framework, the agent implements a metaheuristic algorithm and uses shared information with neighbours as input to optimize the solutions. In this study, a new distributed Symbiotic Organism Search (SOS) algorithm has been proposed and tested in the proposed multi-agent framework. The proposed algorithm is termed a multi-agent-based symbiotic organism search algorithm (MASOS). The MASOS has been tested and compared with other proficient algorithms through statistical analysis using benchmark functions. The results show that the proposed MASOS solves the considered benchmark functions efficiently. Then MASOS was tested for DGs coordination considering load variations in the Tanzanian electrical distribution network. The results show that the coordination of DG using the proposed algorithm reduces power loss and improves the voltage profiles of the power system. |
| first_indexed | 2024-04-10T19:44:38Z |
| format | Article |
| id | doaj.art-d2cc4148317a458791973d5d62038d1b |
| institution | Directory Open Access Journal |
| issn | 2314-7172 |
| language | English |
| last_indexed | 2024-04-10T19:44:38Z |
| publishDate | 2023-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Electrical Systems and Information Technology |
| spelling | doaj.art-d2cc4148317a458791973d5d62038d1b2023-01-29T12:06:42ZengSpringerOpenJournal of Electrical Systems and Information Technology2314-71722023-01-0110111910.1186/s43067-023-00072-7A multi-agent-based symbiotic organism search algorithm for DG coordination in electrical distribution networksShamte Kawambwa0Daudi Mnyanghwalo1Department of Electronics and Telecommunications Engineering, University of Dar es SalaamDepartment of Computer Science and Engineering, University of Dar es SalaamAbstract Metaheuristic algorithms have become popular in solving engineering optimization problems due to their advantages of simple implementation and the ability to find near-optimal solutions for complex and large-scale problems. However, most applications of metaheuristic algorithms consider centralized design, assuming that all possible solutions are available in one machine or controller. In some applications, such as power systems, especially DG coordination, centralized design may not be efficient. This work integrates a multi-agent system (MAS) into a metaheuristic algorithm for enhanced performance. In a proposed multi-agent framework, the agent implements a metaheuristic algorithm and uses shared information with neighbours as input to optimize the solutions. In this study, a new distributed Symbiotic Organism Search (SOS) algorithm has been proposed and tested in the proposed multi-agent framework. The proposed algorithm is termed a multi-agent-based symbiotic organism search algorithm (MASOS). The MASOS has been tested and compared with other proficient algorithms through statistical analysis using benchmark functions. The results show that the proposed MASOS solves the considered benchmark functions efficiently. Then MASOS was tested for DGs coordination considering load variations in the Tanzanian electrical distribution network. The results show that the coordination of DG using the proposed algorithm reduces power loss and improves the voltage profiles of the power system.https://doi.org/10.1186/s43067-023-00072-7MetaheuristicMulti-agentSymbiotic organism searchDG coordinationElectrical distribution network |
| spellingShingle | Shamte Kawambwa Daudi Mnyanghwalo A multi-agent-based symbiotic organism search algorithm for DG coordination in electrical distribution networks Journal of Electrical Systems and Information Technology Metaheuristic Multi-agent Symbiotic organism search DG coordination Electrical distribution network |
| title | A multi-agent-based symbiotic organism search algorithm for DG coordination in electrical distribution networks |
| title_full | A multi-agent-based symbiotic organism search algorithm for DG coordination in electrical distribution networks |
| title_fullStr | A multi-agent-based symbiotic organism search algorithm for DG coordination in electrical distribution networks |
| title_full_unstemmed | A multi-agent-based symbiotic organism search algorithm for DG coordination in electrical distribution networks |
| title_short | A multi-agent-based symbiotic organism search algorithm for DG coordination in electrical distribution networks |
| title_sort | multi agent based symbiotic organism search algorithm for dg coordination in electrical distribution networks |
| topic | Metaheuristic Multi-agent Symbiotic organism search DG coordination Electrical distribution network |
| url | https://doi.org/10.1186/s43067-023-00072-7 |
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