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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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Published in: | Alexandria engineering journal 2020-12, Vol.59 (6), p.4771-4786 |
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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. |
doi_str_mv | 10.1016/j.aej.2020.08.042 |
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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. 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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.</description><subject>APSO</subject><subject>DG size and site</subject><subject>MGSA</subject><subject>MOO</subject><subject>Power loss</subject><issn>1110-0168</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNp9kc9q3DAQxn1IISHNA_SmF7Az8j9Z9LSEJg0EUkh7FmNptIyxrSB5A-kD9Lmr3S3trboMaL7vN9J8RfFJQiVB9rdThTRVNdRQwVBBW18UV1JKKHNzuCxuUpogn07pVvdXxa_dPAeLG4dVBC8cpy3yeNjIiT2tFE-dJHgVER3j_E9xdKT3tNGSxCHxuhfo8HXjNxLfXp4Frk4swbHnjHp42YnlMG9chnEiexKFrF3453nAx-KDxznRzZ96Xfy4__L97mv59PzweLd7Km0L_VZab3vQhN2galC1a2zrG1Cjko4UKK27WmYhNa300OvONqibsW28s6rrgJrr4vHMdQEn8xp5wfhuArI5XYS4Nxg3tjMZTRmKfa075duxq9HaRg4SJY2jA-ozS55ZNoaUIvm_PAnmmIWZTM7CHLMwMJicRfZ8Pnsof_KNKZpkmVZLjmPeS34F_8f9GxW2llc</recordid><startdate>202012</startdate><enddate>202012</enddate><creator>Eid, Ahmad</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>DOA</scope></search><sort><creationdate>202012</creationdate><title>Allocation of distributed generations in radial distribution systems using adaptive PSO and modified GSA multi-objective optimizations</title><author>Eid, Ahmad</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c406t-cfc609ea5872072d3c4f307b71de70799521406e341f0695c3a93b43fdc7550e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>APSO</topic><topic>DG size and site</topic><topic>MGSA</topic><topic>MOO</topic><topic>Power loss</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Eid, Ahmad</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>Directory of Open Access Journals</collection><jtitle>Alexandria engineering journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Eid, Ahmad</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Allocation of distributed generations in radial distribution systems using adaptive PSO and modified GSA multi-objective optimizations</atitle><jtitle>Alexandria engineering journal</jtitle><date>2020-12</date><risdate>2020</risdate><volume>59</volume><issue>6</issue><spage>4771</spage><epage>4786</epage><pages>4771-4786</pages><issn>1110-0168</issn><abstract>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. 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subjects | APSO DG size and site MGSA MOO Power loss |
title | Allocation of distributed generations in radial distribution systems using adaptive PSO and modified GSA multi-objective optimizations |
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