Publication:
A rule-based energy management system for hybrid renewable energy sources with battery bank optimized by genetic algorithm optimization

dc.citedby11
dc.contributor.authorJamal S.en_US
dc.contributor.authorPasupuleti J.en_US
dc.contributor.authorEkanayake J.en_US
dc.contributor.authorid57265080900en_US
dc.contributor.authorid11340187300en_US
dc.contributor.authorid7003409510en_US
dc.date.accessioned2025-03-03T07:41:37Z
dc.date.available2025-03-03T07:41:37Z
dc.date.issued2024
dc.description.abstractA Nanogrid (NG) model is described as a power distribution system that integrates Hybrid Renewable Energy Sources (HRESs) and Energy Storage Systems (ESSs) into the primary grid. However, this process is affected by several factors, like load variability, market pricing, and the intermittent nature of Wind Turbines (WTs) and Photovoltaic (PV) systems. Hence, other researchers in the past have used a few optimization-based processes to improve the development of Energy Management Systems (EMSs) and ESSs, which further enhanced the operational performance of NGs. It was seen that EMS acts as the distributed energy source in the NG setup and assists in power generation, usage, dissemination, and differential pricing. Hence this study employed the MATLAB Simulink software for modelling the grid-connected NG that included HRES; such as wind and PV; in addition to 3 Battery Storage Devices (BSDs) to design an effective EMS for the NG system and decrease its overall costs. For this purpose, a Rule-Based EMS (RB-EMS) that employs State Flow (SF) to guarantee a safe and reliable operating power flow to the NG has been developed. In addition to that, a Genetic Algorithm (GA)-based optimization system and Simulated Annealing optimization Algorithm (SAA) were proposed to determine an economical solution for decreasing the cost of the NG system depending on its operational constraints. Lastly, comparison about the cost between RB-EMS, GA and SAA has been presented. According to the simulation results, the proposed GA displayed an economical performance since it could achieve a 40% cost saving whereas the SAA system showed a 19.3% cost saving compared to the RB-EMS. It can be concluded from the findings that the GA-based optimization technique was very cost-effective displays many important features, like rapid convergence, simple design, and very few controlling factors. ? The Author(s) 2024.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo4865
dc.identifier.doi10.1038/s41598-024-54333-0
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85186179465
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85186179465&doi=10.1038%2fs41598-024-54333-0&partnerID=40&md5=0536c29fdbd212428514750166300134
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36222
dc.identifier.volume14
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access; Gold Open Access; Green Open Access
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.subjectalgorithm
dc.subjectarticle
dc.subjectbinocular convergence
dc.subjectcontrolled study
dc.subjectdata analysis software
dc.subjectenergy
dc.subjectenergy conservation
dc.subjectenergy resource
dc.subjectgenetic algorithm
dc.subjecthybrid
dc.subjectpharmaceutics
dc.subjectrenewable energy
dc.subjectsimulated annealing
dc.subjectsimulation
dc.subjectsoftware
dc.titleA rule-based energy management system for hybrid renewable energy sources with battery bank optimized by genetic algorithm optimizationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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