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Binary Particle Swarm Optimization for Scheduling MG Integrated Virtual Power Plant Toward Energy Saving

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dc.citedby37
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorAbdolrasol M.G.M.en_US
dc.contributor.authorFaisal M.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorBegum R.A.en_US
dc.contributor.authorHussain A.en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid35796848700en_US
dc.contributor.authorid57215018777en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid14007780000en_US
dc.contributor.authorid57208481391en_US
dc.date.accessioned2023-05-29T07:29:36Z
dc.date.available2023-05-29T07:29:36Z
dc.date.issued2019
dc.descriptionComputer aided software engineering; Controllers; Electromagnetic wave emission; Emission control; Energy conservation; Energy management; Energy utilization; Optimization; Particle swarm optimization (PSO); Power plants; Renewable energy resources; Binary particle swarm optimization; Carbon reduction; Micro grid; Reducing energy consumption; Scheduling controllers; Sustainable energy management; Virtual power plants; Virtual power plants (VPP); Schedulingen_US
dc.description.abstractThis paper introduces a novel optimal schedule controller to manage renewable energy resources (RESs) in virtual power plant (VPP) using binary particle swarm optimization (BPSO) algorithm. It is crucial to minimize the costs giving priority for sustainable resources use instead of purchasing from the national grid. The effectiveness of the proposed approach is examined by the IEEE 14 bus system containing microgrids (MGs) integrated with RESs in the form of VPP. Real load demand recorded is used to model and simulate the test case studies of the system for 24 h in Perlis, Malaysia. Moreover, weather data collected from the Malaysian Meteorological Department such as wind, solar, fuel, and battery status data are used in the BPSO to find the best ON and OFF schedules. The results found that the developed BPSO algorithm is robust in reducing energy consumption and emissions of the VPP. This study contributes to the development of an optimization algorithm for an optimal scheduling controller of MG integrated VPP in order to reduce carbon emissions and manage sustainable energy. Finally, a comparative analysis of the optimal algorithms over conventional justifies the use of RESs integration and validates the developed BPSO for sustainable energy management and emissions reduction. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8787762
dc.identifier.doi10.1109/ACCESS.2019.2933010
dc.identifier.epage107951
dc.identifier.scopus2-s2.0-85071102319
dc.identifier.spage107937
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85071102319&doi=10.1109%2fACCESS.2019.2933010&partnerID=40&md5=30f083f1c95b3eed49e3df2c4e8bf579
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24967
dc.identifier.volume7
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.titleBinary Particle Swarm Optimization for Scheduling MG Integrated Virtual Power Plant Toward Energy Savingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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