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dc.contributor.authorNibaruta, R.
dc.contributor.authorMuriithi, Christopher M.
dc.contributor.authorNgoo, L
dc.date.accessioned2020-08-13T13:57:05Z
dc.date.available2020-08-13T13:57:05Z
dc.date.issued2019
dc.identifier.citationEngineering, Technology & Applied Science Research Vol. 9, No. 6, 2019, 4905-4911en_US
dc.identifier.urihttps://www.etasr.com/index.php/ETASR/article/view/3094
dc.identifier.urihttps://zenodo.org/record/3566136#.XzVFBCgzbIU
dc.identifier.urihttp://hdl.handle.net/123456789/4419
dc.description.abstractThis paper proposes a new optimization technique that uses Particle Swarm Optimization (PSO) in residential grid connected photovoltaic systems. The optimization technique targets the sizing of the battery storage system. With the liberation of power systems, the residential grid-connected photovoltaic system can supply power to the grid during peak hours or charge the battery during non-peak hours for later domestic use or for selling back to the grid during peak hours. However, this can only be achieved when the battery energy system in the residential photovoltaic system is optimized. The developed PSO algorithm aims at optimizing the battery capacity that will lower the operation cost of the system. The computational efficiency of the developed algorithm is demonstrated using real PV data from Strathmore University. A comparative study of a PV system with and without battery energy storage is carried out and the simulation results demonstrate that PV system with battery is more efficient when optimized with PSO.en_US
dc.language.isoenen_US
dc.subjectgrid-connected PVen_US
dc.subjectelectricity surplusen_US
dc.subjectsizingen_US
dc.subjectbattery energy storageen_US
dc.subjectelectricity pricesen_US
dc.subjectnet meteringen_US
dc.subjectPSOen_US
dc.titleOptimal Battery Sizing of a Grid-connected Residential Photovoltaic System for Cost Minimization using PSO Algorithmen_US
dc.typeArticleen_US


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