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An effective strategy for unit commitment of microgrid power systems integrated with renewable energy sources including effects of battery degradation and uncertainties

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dc.citedby8
dc.contributor.authorManoharan P.en_US
dc.contributor.authorChandrasekaran K.en_US
dc.contributor.authorChandran R.en_US
dc.contributor.authorRavichandran S.en_US
dc.contributor.authorMohammad S.en_US
dc.contributor.authorJangir P.en_US
dc.contributor.authorid57191413142en_US
dc.contributor.authorid57214039358en_US
dc.contributor.authorid58873007200en_US
dc.contributor.authorid57219263030en_US
dc.contributor.authorid57208800196en_US
dc.contributor.authorid56857572500en_US
dc.date.accessioned2025-03-03T07:45:23Z
dc.date.available2025-03-03T07:45:23Z
dc.date.issued2024
dc.description.abstractThe large use of renewable sources and plug-in electric vehicles (PEVs) would play a critical part in achieving a low-carbon energy source and reducing greenhouse gas emissions, which are the primary cause of global warming. On the other hand, predicting the instability and intermittent nature of wind and solar power output poses significant challenges. To reduce the unpredictable and random nature of renewable microgrids (MGs) and additional unreliable energy sources, a battery energy storage system (BESS) is connected to an MG system. The uncoordinated charging of PEVs offers further hurdles to the unit commitment (UC) required in contemporary MG management. The UC problem is an exceptionally difficult optimization problem due to the mixed-integer structure, large scale, and nonlinearity. It is further complicated by the multiple uncertainties associated with renewable sources, PEV charging and discharging, and electricity market pricing, in addition to the BESS degradation factor. Therefore, in this study, a new variant of mixed-integer particle swarm optimizer is introduced as a reliable optimization framework to handle the UC problem. This study considers�six various case studies of UC problems, including uncertainties and battery degradation to validate the reliability and robustness of the proposed algorithm. Out of which, two case studies defined as a multiobjective problem, and it has been transformed into a single-objective model using different weight factors. The simulation findings demonstrate that the proposed approach and improved methodology for the UC problem are effective�than its�peers. Based on the average results, the economic consequences of numerous scenarios are thoroughly examined and contrasted, and some significant conclusions are presented. ? The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11356-023-31608-z
dc.identifier.epage11080
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85184522069
dc.identifier.spage11037
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85184522069&doi=10.1007%2fs11356-023-31608-z&partnerID=40&md5=19dd07a7ca868f9b90918c3f698829b1
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36874
dc.identifier.volume31
dc.pagecount43
dc.publisherSpringeren_US
dc.sourceScopus
dc.sourcetitleEnvironmental Science and Pollution Research
dc.subjectElectric Power Supplies
dc.subjectEnergy-Generating Resources
dc.subjectRenewable Energy
dc.subjectReproducibility of Results
dc.subjectSolar Energy
dc.subjectWind
dc.subjectCharging (batteries)
dc.subjectElectric energy storage
dc.subjectGas emissions
dc.subjectGlobal warming
dc.subjectInteger programming
dc.subjectParticle swarm optimization (PSO)
dc.subjectPlug-in electric vehicles
dc.subjectSecondary batteries
dc.subjectSolar energy
dc.subjectEnergy
dc.subjectMicrogrid
dc.subjectMixed integer
dc.subjectMixed-integer algorithm
dc.subjectParticle swarm
dc.subjectParticle swarm optimization
dc.subjectSwarm optimization
dc.subjectUncertainty
dc.subjectUnit Commitment
dc.subjectUnit-commitment problems
dc.subjectalgorithm
dc.subjectalternative energy
dc.subjectdegradation
dc.subjectelectric vehicle
dc.subjectelectricity
dc.subjectenergy storage
dc.subjectfuel cell
dc.subjectgreenhouse gas
dc.subjectintegrated approach
dc.subjectoptimization
dc.subjectsmart grid
dc.subjectstrategic approach
dc.subjectuncertainty analysis
dc.subjectenergy resource
dc.subjectpower supply
dc.subjectrenewable energy
dc.subjectreproducibility
dc.subjectsolar energy
dc.subjectwind
dc.subjectGreenhouse gases
dc.titleAn effective strategy for unit commitment of microgrid power systems integrated with renewable energy sources including effects of battery degradation and uncertaintiesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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