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Optimal Unit Commitment for Minimizing Total Operating Cost using Ant Lion Optimizer

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dc.citedby1
dc.contributor.authorYasin Z.M.en_US
dc.contributor.authorSam'On I.N.en_US
dc.contributor.authorSalim N.A.en_US
dc.contributor.authorAb Aziz N.F.en_US
dc.contributor.authorZakaria Z.en_US
dc.contributor.authorid57211410254en_US
dc.contributor.authorid57193868724en_US
dc.contributor.authorid36806685300en_US
dc.contributor.authorid57221906825en_US
dc.contributor.authorid56276791800en_US
dc.date.accessioned2023-05-29T09:08:49Z
dc.date.available2023-05-29T09:08:49Z
dc.date.issued2021
dc.descriptionOperating costs; Scheduling; Computation time; Generation scheduling; Global optimum solutions; Global solutions; Hunting behavior; Number of iterations; Reserve requirements; System constraints; Dynamic programmingen_US
dc.description.abstractThis paper presents a new technique namely Ant Lion Optimizer (ALO) to determine optimal unit commitment. The proposed technique is simulated on IEEE 39-bus test system which consists of 10-unit generators with consideration of system constraints in unit commitment such as power balance, system reserve requirement, generation limit of generators, and minimum up and down time constraints. ALO is inspired based on hunting behavior of ant lion. There are five main steps, which include random walk of ants, trapping of ants in antlions' trip, building trap, sliding of ants towards antlion, catching prey and rebuilding the pit. The proposed ALO algorithm is able to identify the global optimum solution since the intensity of ants' movement is adaptively decreased as the number of iterations increase. In addition, the exploration of search space is guaranteed within the limitation of set-up boundaries. This behavior will enhance the optimization towards the optimal and global solution. The performance of the proposed algorithm is compared with the performance of Dynamic Programming (DP) technique in terms of generation scheduling, total operating cost (TOC) and computation time. From the results obtained, ALO provides better generation scheduling with lower TOC, as compared to DP technique. The cost saving per year performed by ALO technique as compared to DP is $236,520. Based on the results, ALO provides better solution as compared to DP in terms of providing better generation scheduling, and significant reduction of TOC and with lower computation time. � 2021 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9417831
dc.identifier.doi10.1109/ICPEA51500.2021.9417831
dc.identifier.epage226
dc.identifier.scopus2-s2.0-85106432741
dc.identifier.spage221
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85106432741&doi=10.1109%2fICPEA51500.2021.9417831&partnerID=40&md5=c6edc467ec68ce2d5ab4dbc3fb98368f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26294
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleICPEA 2021 - 2021 IEEE International Conference in Power Engineering Application
dc.titleOptimal Unit Commitment for Minimizing Total Operating Cost using Ant Lion Optimizeren_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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