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Integrated Grasshopper Algorithm-Evolutionary Programming Technique for Distributed Energy Resources Allocation

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dc.contributor.authorKamalrolzaman M.A.en_US
dc.contributor.authorMusirin I.en_US
dc.contributor.authorMansor M.H.en_US
dc.contributor.authorSalimin R.H.en_US
dc.contributor.authorIsmail N.L.en_US
dc.contributor.authorMohamed Kamari N.A.en_US
dc.contributor.authorid58068996200en_US
dc.contributor.authorid8620004100en_US
dc.contributor.authorid56372667100en_US
dc.contributor.authorid25928749500en_US
dc.contributor.authorid57190935802en_US
dc.contributor.authorid57781285800en_US
dc.date.accessioned2023-05-29T09:38:55Z
dc.date.available2023-05-29T09:38:55Z
dc.date.issued2022
dc.descriptionComputer programming; Distributed computer systems; Electric power transmission; Electric power transmission networks; Energy resources; Evolutionary algorithms; Optimal systems; Distributed energy resource; Distributed Energy Resources; Distributed generation installation; Evolutionary programming techniques; Fitness; Objective functions; Optimisations; Optimization techniques; Power system networks; Resources allocation; Installationen_US
dc.description.abstractDistributed energy resources (DER) are among important components or additional supplies to an existing power system network. Its installation in an existing power system network will help improve the voltage level in a system, reduce current values through the transmission lines and reduce the total transmission loss. The sizing and locations for the installation of DER or distributed generation (DG) require optimal values. Otherwise, the system will experience either over-compensation or under-compensation phenomena. Thus, a reliable optimization technique is a crucial factor. Several optimization techniques are not reliable enough as they cannot reach optimal solutions and sometimes, they are not accurate. This paper presents a new optimization technique termed Integrated Grasshopper Evolutionary Programming Technique (IGEPT). IGEPT integrates some operators in the grasshopper optimization into the original evolutionary programming (EP). It was validated on the IEEE 30-Bus Reliability Test System (RTS) for voltage maximization effort as the objective function. Several cases were taken into account so as to highlight the robustness of this technique. A comparative study with other techniques is also conducted, which highlights the merit of IGEPT. � 2022 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/PECon54459.2022.9988769
dc.identifier.epage321
dc.identifier.scopus2-s2.0-85146415552
dc.identifier.spage316
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85146415552&doi=10.1109%2fPECon54459.2022.9988769&partnerID=40&md5=1ba8d3e55b01b1b3edf3f29e258ccd30
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27040
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2022 IEEE International Conference on Power and Energy: Advancement in Power and Energy Systems towards Sustainable and Resilient Energy Supply, PECon 2022
dc.titleIntegrated Grasshopper Algorithm-Evolutionary Programming Technique for Distributed Energy Resources Allocationen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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