Publication:
An Optimized Binary Scheduling Controller for Microgrid Energy Management Considering Real Load Conditions

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dc.citedby0
dc.contributor.authorMannan M.en_US
dc.contributor.authorRoslan M.F.en_US
dc.contributor.authorReza M.S.en_US
dc.contributor.authorMansor M.en_US
dc.contributor.authorJern K.P.en_US
dc.contributor.authorHossain M.J.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorid57224923024en_US
dc.contributor.authorid57220188085en_US
dc.contributor.authorid59055914200en_US
dc.contributor.authorid6701749037en_US
dc.contributor.authorid57220589801en_US
dc.contributor.authorid57209871691en_US
dc.contributor.authorid7103014445en_US
dc.date.accessioned2024-10-14T03:19:46Z
dc.date.available2024-10-14T03:19:46Z
dc.date.issued2023
dc.description.abstractA dynamical power demand and stochastic nature of energy resources posses difficulties in controlling and managing output power. These challenges lead to instability and inconsistency of the entire operation which can cause unstable and power quality issues. This study presents an optimal schedule controller for microgrid energy management, utilizing the Binary Particle Swarm algorithm (BPSO) to minimize costs and ensure optimal power delivery to loads. The controller's aims include minimizing total operating costs for distributed energy resources and solving intricate constraint optimization issues with scheduling management operations. The proposed approach's effectiveness is evaluated within an IEEE 14-bus configuration with five microgrids (MGs) integrated with RESs using real load data from Perlis, Malaysia. The BPSO optimization technique offers an exceptional binary fitness function to find the optimal cell, utilizing real data such as solar radiation, wind speed, battery charging/discharging, fuel conditions, and demand. To confirm the efficiency of the developed controller, a comparison is conducted between the results achieved with and without microgrid (MG) integration. The results reveal the robustness of the BPSO algorithm in reducing energy consumption and cost by 199.6 MW to 316.53 MW and RM 87,250.35 to RM 138,327.5 respectively. As a result, an optimized scheduling controller-based BPSO optimization outperforms in terms of savings cost, reduced energy consumption, optimal DER use, and decreased CO2 emissions. � 2023 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ETFG55873.2023.10407196
dc.identifier.scopus2-s2.0-85185792899
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85185792899&doi=10.1109%2fETFG55873.2023.10407196&partnerID=40&md5=0e7746461533b8acbdc5365c2911cc4b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34435
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.subjectBinary Particle Swarm Optimization
dc.subjectDistributed energy resources
dc.subjectEnergy management
dc.subjectMicrogrid
dc.subjectOptimization algorithm
dc.subjectScheduling controller
dc.subjectConstrained optimization
dc.subjectControllers
dc.subjectCost reduction
dc.subjectEnergy resources
dc.subjectEnergy utilization
dc.subjectOperating costs
dc.subjectParticle swarm optimization (PSO)
dc.subjectPower quality
dc.subjectScheduling algorithms
dc.subjectStochastic systems
dc.subjectWind
dc.subjectAlgorithms optimizations
dc.subjectBinary particle swarm
dc.subjectBinary particle swarm optimization
dc.subjectDistributed Energy Resources
dc.subjectLoad condition
dc.subjectMicrogrid
dc.subjectOptimization algorithms
dc.subjectParticle swarm algorithm
dc.subjectPower demands
dc.subjectScheduling controllers
dc.subjectEnergy management
dc.titleAn Optimized Binary Scheduling Controller for Microgrid Energy Management Considering Real Load Conditionsen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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