Optimized placement, sizing, and selection of distributed generation using the salp swarm algorithm
The salp swarm algorithm (SSA) was introduced as a method for efficiently selecting the optimal location, size, and type of distributed generation (DG) in a distribution system. SSA is a probabilistic algorithm that simulates the behavior of a population of agents, specifically by replicating the...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Accent Social and Welfare Society
2025
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29139/2/0062812092025103756.pdf http://eprints.utem.edu.my/id/eprint/29139/ https://accentsjournals.org/PaperDirectory/Journal/IJATEE/2025/3/3.pdf http://dx.doi.org/10.19101/IJATEE.2024.111100877 |
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| Summary: | The salp swarm algorithm (SSA) was introduced as a method for efficiently selecting the optimal location, size, and type
of distributed generation (DG) in a distribution system. SSA is a probabilistic algorithm that simulates the behavior of a
population of agents, specifically by replicating the foraging behavior of salps in water. Salps often form cohesive groups
called salp chains in deep waters. This behavior enables them to optimize locomotion through coordinated and swift
movements while maximizing their foraging efficiency. This study investigated three types of DG: photovoltaic (PV),
wind, and diesel. The methodology distinguishes between different types of DG, determines their optimal placement, and optimizes their sizing for maximum performance. Simulations are conducted on the IEEE 69-bus system. The results indicate that the proposed SSA approach successfully identifies the most suitable sites, sizes, and types of DG. A
benchmark comparison is performed to assess the effectiveness of the proposed SSA method against the evolutionary programming (EP) approach. The results demonstrate that SSA outperforms EP in reducing power losses and improving the voltage profile. |
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