Publication:
Optimal Sizing of PV-Battery based Hybrid Renewable System using Particle Swarm Optimization for Economic Sustainability

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dc.citedby0
dc.contributor.authorWali S.B.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorKiong T.S.en_US
dc.contributor.authorid56402940200en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid57216824752en_US
dc.date.accessioned2024-10-14T03:19:38Z
dc.date.available2024-10-14T03:19:38Z
dc.date.issued2023
dc.description.abstractIntegrating energy storage (ES) such as batteries with renewable sources like photovoltaic (PV) systems offers eco-friendly power generation, but optimizing the scale of hybrid renewable systems (HRSs) is complex due to PV intermittency, discharge uncertainty, and economic factors. The article has proposed an optimal solution for a small-scale PV-battery-based hybrid renewable system aimed at improving economic sustainability using particle swarm optimization (PSO). The main objective is to minimize the levelized cost of energy (LCOE) while finding the optimal PV and battery sizes. By conducting simulations and analyses using MATLAB, the findings vividly illustrate the significant influence of PSO in reducing the overall LOCE of 80.36%. Through iterative exploration and optimization of PV capacity, battery capacity, and power rating, the PSO algorithm achieves an optimal configuration, minimizing costs while meeting energy demands. The optimal configuration includes a 3.3kW of PV and a one kWh battery with an NPC of $24,974.29 and an LCOE of 0.011 $/kWh. The system has a renewable fraction (RF) of 100% with no CO2 emission. The PSO-driven method, based on real-world data on power demand, PV generation, and EV charging, demonstrates its novel impact on renewable energy system design, accelerating the transition to greener and more cost-effective energy solutions � 2023 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ETFG55873.2023.10408314
dc.identifier.scopus2-s2.0-85185787323
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85185787323&doi=10.1109%2fETFG55873.2023.10408314&partnerID=40&md5=cbe1e74ef5916867dcc98714403992dd
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34418
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.subjectEnergy storage
dc.subjecthybrid renewable system
dc.subjectlevelized cost of energy
dc.subjectparticle swarm optimization
dc.subjectRenewable energy sources
dc.subjectCost effectiveness
dc.subjectDigital storage
dc.subjectEnergy storage
dc.subjectIterative methods
dc.subjectParticle size analysis
dc.subjectParticle swarm optimization (PSO)
dc.subjectRenewable energy
dc.subjectSecondary batteries
dc.subjectSustainable development
dc.subjectCost of energies
dc.subjectEconomic sustainability
dc.subjectHybrid renewable system
dc.subjectLevelized cost of energy
dc.subjectLevelized costs
dc.subjectParticle swarm
dc.subjectParticle swarm optimization
dc.subjectPhotovoltaics
dc.subjectRenewable energy source
dc.subjectSwarm optimization
dc.subjectMATLAB
dc.titleOptimal Sizing of PV-Battery based Hybrid Renewable System using Particle Swarm Optimization for Economic Sustainabilityen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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