Optimal amalgamation of DG units in radial distribution system for techno-economic study by improved SSA: Practical case study

•The optimal placement for DG units is suggested with techno-economic analysis.•Application of the improved salp swarm algorithm (ISSA) to the suggested optimal DG placement.•Real power loss, yearly economic loss, and the voltage profile are all improved by positioning optimally scaled DG units at i...

Full description

Saved in:
Bibliographic Details
Published in:Electric power systems research Vol. 241; p. 111365
Main Author: Neda, Omar Muhammed
Format: Journal Article
Language:English
Published: Elsevier B.V 01.04.2025
Subjects:
Distribute generation units
Improved salp swarm algorithm
Power loss
Traditional salp swarm algorithm
Voltage profile
Power loss
Traditional salp swarm algorithm
Voltage profile
Improved salp swarm algorithm
Distribute generation units
ISSN:0378-7796
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•The optimal placement for DG units is suggested with techno-economic analysis.•Application of the improved salp swarm algorithm (ISSA) to the suggested optimal DG placement.•Real power loss, yearly economic loss, and the voltage profile are all improved by positioning optimally scaled DG units at ideal locations.•The IEEE-33, IEEE-69, and Iraqi 36 bus are utilized as test systems.•The simulation results demonstrate the suggested ISSA's efficacy in comparison to standard SSA, GA and other algorithms reported in the literature. Distributed generating units can greatly improve system performance when integrated into radial distribution networks, but in order to reduce hazards, their ideal placement must be carefully considered. So, improved salp swarm algorithm was presented in this work for powerfully tackling the problem of determining optimum distributed generation placement within the radial distribution systems which may reflect positively on reducing power loss, cost and updating voltage profile, thus maximizing both economic and technical benefits. By adding a new update equation for the leader and followers, improved salp swarm algorithm improves on the traditional salp swarm algorithm, increasing exploration potential and avoiding premature convergence. An analysis is conducted on a local Iraqi-36 bus, IEEE-33 bus, and IEEE-69 bus systems for determining the efficiency of the presented algorithm. The results are then compared with those attained by the traditional salp swarm algorithm, genetic algorithm, and other methodologies in the literature. The findings demonstrate that improved salp swarm algorithm performs better than both traditional salp swarm algorithm and genetic algorithm, maximizing cost savings (51,673.5$ for IEEE 33, 60,945.48$ for IEEE 69, and 14,942.1$ for Iraqi 36) and achieving the highest power loss reduction (35.15 % for IEEE 33, 65.54 % for IEEE 69, and 35.65 % for Iraqi 36). Additionally, improved salp swarm algorithm ensures improved system performance and stability by improving the voltage profile from 0.9800 p.u. to 0.9899 p.u. Comparison results show that improved salp swarm algorithm is more robust, efficient, and superior in distributed generation placement optimization than other approaches currently used in the literature, including the conventional salp swarm algorithm, genetic algorithm, and others. [Display omitted]
ISSN:0378-7796
DOI:10.1016/j.epsr.2024.111365