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dc.contributor.authorTayab, UB
dc.contributor.authorLu, J
dc.contributor.authorYang, F
dc.contributor.authorAlGarni, TS
dc.contributor.authorKashif, M
dc.date.accessioned2021-10-14T05:26:13Z
dc.date.available2021-10-14T05:26:13Z
dc.date.issued2021
dc.identifier.issn0960-1481
dc.identifier.doi10.1016/j.renene.2021.08.070
dc.identifier.urihttp://hdl.handle.net/10072/409080
dc.description.abstractThe concept of a microgrid (MG) has been introduced to integrate the conventional generators, different renewable energy resources and energy storage systems (ESS) to meet the specific load demand. However, the intermittent nature of renewable energy resources produces a variable output, which drives an imbalance between power generation and demand in MG. The ESS is utilized to makes a balance between power generation and demand. When several renewable energy resources and ESS are available in MG as energy resources, then an energy management system (EMS) is required that can handle the stochastic nature of renewable energy resources, schedule the power of renewable energy resources and ESS for managing the power flow among MG resources and main grid while ensuing cost-effective operation. Therefore, this paper proposed an optimum EMS that aims to minimize the overall operating cost of grid-connected MG along with the short-term forecasting of PV power and load demand. The proposed EMS consists of four modules: forecasting, scheduling, data acquisition (DAQ), and human–machine interface (HMI) modules. An improved hybrid forecasting approach that combines a 3-level stationary wavelet transform (SWT) and grey wolf optimization-based least-square support vector machine (GWO-LSSVM) is proposed in the forecasting module to achieve day-ahead forecasting of PV power and load demand. In the scheduling module, the weighted salp swarm algorithm-based scheduling is applied to achieve the optimum power flow of grid-connected MG. Then, the DAQ and HMI module is used to monitor, analyze, and modified the input variables of the forecasting and scheduling module. The MATLAB/Simulink environment is then used to simulate the proposed EMS for grid-connected MG. Finally, numerical results demonstrate the efficiency of the proposed EMS for grid-connected MG with commercial load demand over the existing competitive approaches.
dc.description.peerreviewedYes
dc.description.sponsorshipCentral Queensland University
dc.description.sponsorshipSwinburne University of Technology ARC
dc.languageen
dc.publisherElsevier BV
dc.relation.ispartofpagefrom467
dc.relation.ispartofpageto481
dc.relation.ispartofjournalRenewable Energy
dc.relation.ispartofvolume180
dc.relation.urihttp://purl.org/au-research/grants/ARC/LP190101251
dc.relation.urihttp://purl.org/au-research/grants/ARC/DP160103567
dc.relation.grantIDLP190101251
dc.relation.grantIDDP160103567
dc.relation.fundersARC
dc.subject.fieldofresearchElectrical engineering
dc.subject.fieldofresearchMechanical engineering
dc.subject.fieldofresearchOther engineering
dc.subject.fieldofresearchcode4008
dc.subject.fieldofresearchcode4017
dc.subject.fieldofresearchcode4099
dc.titleEnergy management system for microgrids using weighted salp swarm algorithm and hybrid forecasting approach
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationTayab, UB; Lu, J; Yang, F; AlGarni, TS; Kashif, M, Energy management system for microgrids using weighted salp swarm algorithm and hybrid forecasting approach, Renewable Energy, 2021, 180, pp. 467-481
dc.date.updated2021-10-14T05:25:11Z
gro.hasfulltextNo Full Text
gro.griffith.authorYang, Fuwen
gro.griffith.authorTayab, Usman Bashir
gro.griffith.authorLu, Junwei


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