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Energy Management in Integrated Microgrids: An Optimal Schedule Controller Utilizing Gradient Descent Algorithm

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dc.citedby0
dc.contributor.authorAbdolrasol M.G.M.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorAyob A.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorid35796848700en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid26666566900en_US
dc.contributor.authorid15128307800en_US
dc.date.accessioned2024-10-14T03:19:38Z
dc.date.available2024-10-14T03:19:38Z
dc.date.issued2023
dc.description.abstractThis paper presents a new optimal controller using the Binary Gradient Descent (BGD) algorithm to manage distributed generations effectively in a grid network. The algorithm aims to minimize power consumption from the main grid and prioritize sustainable resource utilization over buying electricity from the local network grid. The proposed approach is evaluated on the IEEE fourteen bus test system with integrated Microgrids (MGs) and distributed generations, using real load demand data from Perlis, Malaysia, for 24-hour test case studies. Weather data, including wind, solar, fuel, and battery status, is integrated into the BGD algorithm for optimizing ON and OFF schedules. The results demonstrate a significant 46.3% reduction in energy consumption achieved by the BGD algorithm, contributing to the advancement of optimization algorithms for sustainable energy management. The developed BGD algorithm's effectiveness is further validated through a comparative analysis with conventional methods. � 2023 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ETFG55873.2023.10408061
dc.identifier.scopus2-s2.0-85185801552
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85185801552&doi=10.1109%2fETFG55873.2023.10408061&partnerID=40&md5=3e371c5801497c1227a89db5813e6d47
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34417
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023
dc.subjectEfficient Energy Management
dc.subjectEnergy Scheduling
dc.subjectGradient Descent Algorithm
dc.subjectIntegrated Microgrids
dc.subjectRenewable Energy Integration
dc.subjectRenewable Resources
dc.subjectDistributed power generation
dc.subjectElectric loads
dc.subjectEnergy efficiency
dc.subjectEnergy utilization
dc.subjectGradient methods
dc.subjectMicrogrids
dc.subjectOptimization
dc.subjectSmart power grids
dc.subjectBinary gradients
dc.subjectEfficient energy management
dc.subjectEnergy
dc.subjectEnergy scheduling
dc.subjectGradient descent algorithms
dc.subjectIntegrated microgrid
dc.subjectMicrogrid
dc.subjectOptimal schedule
dc.subjectRenewable energy integrations
dc.subjectRenewable resource
dc.subjectEnergy management
dc.titleEnergy Management in Integrated Microgrids: An Optimal Schedule Controller Utilizing Gradient Descent Algorithmen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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