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Techno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzy

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dc.citedby154
dc.contributor.authorRajkumar R.K.en_US
dc.contributor.authorRamachandaramurthy V.K.en_US
dc.contributor.authorYong B.L.en_US
dc.contributor.authorChia D.B.en_US
dc.contributor.authorid35093088900en_US
dc.contributor.authorid6602912020en_US
dc.contributor.authorid36351346900en_US
dc.contributor.authorid36350595200en_US
dc.date.accessioned2023-12-28T07:05:45Z
dc.date.available2023-12-28T07:05:45Z
dc.date.issued2011
dc.description.abstractHigh cost of renewable energy systems has led to its slow adoption in many countries. Hence, it is vital to select an appropriate size of the system in order to reduce the cost and excess energy produced as well as to maximize the available resources. The sizing of hybrid system must satisfy the LPSP (Loss of Power Supply Probability) which determines the ability of the system to meet the load requirements. Once the lowest configurations are determined, the cost of the system must then be taken into consideration to determine the system with the lowest cost. The optimization methodology proposed in this paper uses the ANFIS (Adaptive Neuro-Fuzzy Inference System) to model the PV and wind sources. The algorithm developed is compared to HOMER (Hybrid Optimization Model for Electric Renewables) and HOGA (Hybrid Optimization by Genetic Algorithms) software and the results demonstrate an accuracy of 96% for PV and wind. The optimized system is simulated in PSCAD/EMTDC and the results show that low excess energy is achieved. The optimized system is also able to supply power to the load without any renewable sources for a longer period, while conforming to the desired LPSP. � 2011 Elsevier Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.energy.2011.06.017
dc.identifier.epage5153
dc.identifier.issue8
dc.identifier.scopus2-s2.0-79961026342
dc.identifier.spage5148
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-79961026342&doi=10.1016%2fj.energy.2011.06.017&partnerID=40&md5=f26ee37f27e5eafa191ea828d51a0dc9
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29600
dc.identifier.volume36
dc.pagecount5
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleEnergy
dc.subjectAdaptive Neuro-Fuzzy Inference System
dc.subjectLoss of power supply probability
dc.subjectPhotovoltaic
dc.subjectAlgorithms
dc.subjectCosts
dc.subjectElectric power systems
dc.subjectFuzzy inference
dc.subjectFuzzy systems
dc.subjectHybrid systems
dc.subjectLoss of load probability
dc.subjectAdaptive neuro-fuzzy inference system
dc.subjectExcess energy
dc.subjectHigh costs
dc.subjectHybrid optimization
dc.subjectLoss of power supply probability
dc.subjectNeuro-Fuzzy
dc.subjectOptimization methodology
dc.subjectOptimized system
dc.subjectPhotovoltaic
dc.subjectPSCAD/EMTDC
dc.subjectRenewable energy systems
dc.subjectRenewable sources
dc.subjectRenewables
dc.subjectaccuracy assessment
dc.subjectcost-benefit analysis
dc.subjectenergy efficiency
dc.subjectgenetic algorithm
dc.subjectnumerical model
dc.subjectoptimization
dc.subjectrenewable resource
dc.subjectOptimization
dc.titleTechno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzyen_US
dc.typeArticleen_US
dspace.entity.typePublication
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