Publication:
Multi-Objective Optimal Energy Management of Nanogrid Using Improved Pelican Optimization Algorithm

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dc.citedby4
dc.contributor.authorJamal S.en_US
dc.contributor.authorPasupuleti J.en_US
dc.contributor.authorRahmat N.A.en_US
dc.contributor.authorTan N.M.L.en_US
dc.contributor.authorid57265080900en_US
dc.contributor.authorid11340187300en_US
dc.contributor.authorid55647163881en_US
dc.contributor.authorid24537965000en_US
dc.date.accessioned2025-03-03T07:47:50Z
dc.date.available2025-03-03T07:47:50Z
dc.date.issued2024
dc.description.abstractThe development of efficient energy management for nanogrid (NG) systems, while reducing both the carbon dioxide (CO2) emissions and power generation cost, is achievable through the effective utilization of available energy sources. This paper proposes a multi-objective optimal energy management strategy for grid-connected NG systems, which incorporates PV arrays and battery storage devices (BSDs), to reduce operating costs and CO2 emission simultaneously over a 24-hour scheduling period. This strategy, which is based on the improved pelican optimization algorithm (IPOA), involves the development of a multi-objective optimization (MOA) equation with several constraints, while taking into account the Malaysian grid purchasing and selling prices. An innovative IPOA-derived technique is developed to facilitate the NG's optimal energy management operation in multi-objective situations. The proposed algorithm is tested on three distinct scenarios to affirm its efficacy. It is assumed that (a) power exchange between the NG and the main grid is limitless, (b) power interchange between the NG and main grid has a predetermined limit and (c) operating at the maximum capacity of PV array. In order to demonstrate the effectiveness of the proposed algorithm, The outcomes of the simulation are juxtaposed with results obtained from the initial Pelican Optimisation Algorithm (POA), the Bat Algorithm, and the Improved Differential Evolutionary (IDE) Algorithm. The simulation reveals that the suggested IPOA algorithm exhibited the most economical performance and the lowest CO2 emissions. Moreover, in the second scenario, operational costs decreased by 9.5%, and CO2 emissions were reduced by 15%. Conversely, in Scenario 3, there was a 2% decrease in cost and 23% reduction in CO2 emissions as against the first scenario. ? 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ACCESS.2024.3377250
dc.identifier.epage41966
dc.identifier.scopus2-s2.0-85188437564
dc.identifier.spage41954
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85188437564&doi=10.1109%2fACCESS.2024.3377250&partnerID=40&md5=e1de1881cdb7e4987001ba5e44337cd8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37133
dc.identifier.volume12
dc.pagecount12
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.subjectCarbon dioxide
dc.subjectCost effectiveness
dc.subjectCost reduction
dc.subjectEmission control
dc.subjectEnergy efficiency
dc.subjectEnergy management
dc.subjectMultiobjective optimization
dc.subjectOperating costs
dc.subjectPower markets
dc.subjectVirtual storage
dc.subject> emission
dc.subjectCO<sub xmlns:ali="
dc.subjectCost effective
dc.subjectImproved pelican optimization algorithm
dc.subjectMicrogrid
dc.subjectMulti objective
dc.subjectMulti-objective energy management
dc.subjectNanogrids
dc.subjectOptimisations
dc.subjectOptimization algorithms
dc.subjectXmlns:mml="
dc.subjectXmlns:xlink="
dc.subjectXmlns:xsi="
dc.subjectGenetic algorithms
dc.titleMulti-Objective Optimal Energy Management of Nanogrid Using Improved Pelican Optimization Algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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