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Particle swarm optimised fuzzy controller for charging�discharging and scheduling of battery energy storage system in MG applications

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dc.citedby26
dc.contributor.authorFaisal M.en_US
dc.contributor.authorHannan M.A.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorRahman M.S.A.en_US
dc.contributor.authorBegum R.A.en_US
dc.contributor.authorMahlia T.M.I.en_US
dc.contributor.authorid57215018777en_US
dc.contributor.authorid7103014445en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid36609854400en_US
dc.contributor.authorid14007780000en_US
dc.contributor.authorid56997615100en_US
dc.date.accessioned2023-05-29T08:06:51Z
dc.date.available2023-05-29T08:06:51Z
dc.date.issued2020
dc.descriptionBattery management systems; Battery storage; Charging (batteries); Controllers; Cost reduction; Electric power transmission networks; Electric power utilization; Fuzzy control; Fuzzy logic; Membership functions; Microgrids; Particle swarm optimization (PSO); Scheduling; Secondary batteries; Temperature control; Battery energy storage systems; Battery temperature; Conventional systems; Distributed sources; Fuzzy logic controllers; Mathematical calculations; Particle swarm optimisation; Scheduling controllers; Electric power system controlen_US
dc.description.abstractAiming at reducing the power consumption and costs of grids, this paper deals with the development of particle swarm optimisation (PSO) based fuzzy logic controller (FLC) for charging�discharging and scheduling of the battery energy storage systems (ESSs) in microgrid (MG) applications. Initially, FLC was developed to control the charging�discharging of the storage system to avoid mathematical calculation of the conventional system. However, to improve the charging�discharging control, the membership function of the FLC is optimised using PSO technique considering the available power, load demand, battery temperature and state of charge (SOC). The scheduling controller is the optimal solution to achieve low-cost uninterrupted reliable power according to the loads. To reduce the grid power demand and consumption costs, an optimal binary PSO is also introduced to schedule the ESS, grid and distributed sources under various load conditions at different times of the day. The obtained results proved that the robustness of the developed PSO based fuzzy control can effectively manage the battery charging�discharging with reducing the significant grid power consumption of 42.26% and the costs of the energy usage by 45.11% which also demonstrates the contribution of the research. � 2020 The Authorsen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.egyr.2020.12.007
dc.identifier.epage228
dc.identifier.scopus2-s2.0-85097742059
dc.identifier.spage215
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097742059&doi=10.1016%2fj.egyr.2020.12.007&partnerID=40&md5=b173bcf3cdefbf8c6a4d6dba98db9950
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25116
dc.identifier.volume6
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleEnergy Reports
dc.titleParticle swarm optimised fuzzy controller for charging�discharging and scheduling of battery energy storage system in MG applicationsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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