Allocation of distributed generations in radial distribution systems using adaptive PSO and modified GSA multi-objective optimizations
In this paper, two metaheuristic techniques are used for optimal allocation of Distributed Generations (DGs) to reduce the power losses in Radial Distribution Systems (RDS). These techniques are the adaptive Particle Swarm Optimization (APSO) and the modified Gravitational Search Algorithm (MGSA). S...
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| Format: | Article |
| Language: | English |
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Elsevier
2020-12-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016820304300 |
| _version_ | 1818459132300099584 |
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| author | Ahmad Eid |
| author_facet | Ahmad Eid |
| author_sort | Ahmad Eid |
| collection | DOAJ |
| description | In this paper, two metaheuristic techniques are used for optimal allocation of Distributed Generations (DGs) to reduce the power losses in Radial Distribution Systems (RDS). These techniques are the adaptive Particle Swarm Optimization (APSO) and the modified Gravitational Search Algorithm (MGSA). Single, as well as multiple DGs, are optimized for the optimal size and site with unity- and optimal-PFs. Besides the reduction of power losses, the voltage stability and the total voltage deviation are considered as a multi-objective optimization (MOO) problem. For MOO operation, Pareto-optimal solution, aggregated sum, and ε-constrained techniques are used for determining the DG optimal size and site. The proposed algorithms have been applied to different RDSs, including the IEEE 69-bus and the 85-bus systems. The obtained results are matched favorably with those in the literature. The operation of the DG at optimal-PF is more effective than the UPF in the reduction of power losses. Besides, installing more DGs results in better performance of the systems. The MGSA and APSO algorithms, they are compared to the AEO algorithm according to different performance metrics. The results show that the MGSA and APSO outperform the AEO algorithm. Moreover, the obtained results are significantly approved by using a t-test. |
| first_indexed | 2024-12-14T23:09:29Z |
| format | Article |
| id | doaj.art-9e1dea62957f4b52acc3181a1ebbd0e6 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-12-14T23:09:29Z |
| publishDate | 2020-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-9e1dea62957f4b52acc3181a1ebbd0e62022-12-21T22:44:14ZengElsevierAlexandria Engineering Journal1110-01682020-12-0159647714786Allocation of distributed generations in radial distribution systems using adaptive PSO and modified GSA multi-objective optimizationsAhmad Eid0Address: Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt.; Department of Electrical Engineering, College of Engineering, Qassim University, Unaizah 56452, Saudi Arabia; Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, EgyptIn this paper, two metaheuristic techniques are used for optimal allocation of Distributed Generations (DGs) to reduce the power losses in Radial Distribution Systems (RDS). These techniques are the adaptive Particle Swarm Optimization (APSO) and the modified Gravitational Search Algorithm (MGSA). Single, as well as multiple DGs, are optimized for the optimal size and site with unity- and optimal-PFs. Besides the reduction of power losses, the voltage stability and the total voltage deviation are considered as a multi-objective optimization (MOO) problem. For MOO operation, Pareto-optimal solution, aggregated sum, and ε-constrained techniques are used for determining the DG optimal size and site. The proposed algorithms have been applied to different RDSs, including the IEEE 69-bus and the 85-bus systems. The obtained results are matched favorably with those in the literature. The operation of the DG at optimal-PF is more effective than the UPF in the reduction of power losses. Besides, installing more DGs results in better performance of the systems. The MGSA and APSO algorithms, they are compared to the AEO algorithm according to different performance metrics. The results show that the MGSA and APSO outperform the AEO algorithm. Moreover, the obtained results are significantly approved by using a t-test.http://www.sciencedirect.com/science/article/pii/S1110016820304300APSOMGSADG size and siteMOOPower loss |
| spellingShingle | Ahmad Eid Allocation of distributed generations in radial distribution systems using adaptive PSO and modified GSA multi-objective optimizations Alexandria Engineering Journal APSO MGSA DG size and site MOO Power loss |
| title | Allocation of distributed generations in radial distribution systems using adaptive PSO and modified GSA multi-objective optimizations |
| title_full | Allocation of distributed generations in radial distribution systems using adaptive PSO and modified GSA multi-objective optimizations |
| title_fullStr | Allocation of distributed generations in radial distribution systems using adaptive PSO and modified GSA multi-objective optimizations |
| title_full_unstemmed | Allocation of distributed generations in radial distribution systems using adaptive PSO and modified GSA multi-objective optimizations |
| title_short | Allocation of distributed generations in radial distribution systems using adaptive PSO and modified GSA multi-objective optimizations |
| title_sort | allocation of distributed generations in radial distribution systems using adaptive pso and modified gsa multi objective optimizations |
| topic | APSO MGSA DG size and site MOO Power loss |
| url | http://www.sciencedirect.com/science/article/pii/S1110016820304300 |
| work_keys_str_mv | AT ahmadeid allocationofdistributedgenerationsinradialdistributionsystemsusingadaptivepsoandmodifiedgsamultiobjectiveoptimizations |