Multi-Source Energy Mixing for Renewable Energy Microgrids by Particle Swarm Optimization

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dc.authoridAlagoz, Baris Baykant/0000-0001-5238-6433
dc.authoridKAYGUSUZ, ASİM/0000-0003-2905-1816;
dc.authorwosidAlagoz, Baris Baykant/ABG-8526-2020
dc.authorwosidKAYGUSUZ, ASİM/C-2265-2015
dc.authorwosidKeleş, Cemal/B-9025-2016
dc.contributor.authorKeles, Cemal
dc.contributor.authorAlagoz, Baris Baykant
dc.contributor.authorKaygusuz, Asim
dc.date.accessioned2024-08-04T20:44:11Z
dc.date.available2024-08-04T20:44:11Z
dc.date.issued2017
dc.departmentİnönü Üniversitesien_US
dc.description2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEYen_US
dc.description.abstractDistributed intelligence is one of the prominent prospects of future smart grids besides distributed generation, distributed storage and demand side load management. This study illustrates utilization of particle swarm optimization (PSO) method for cost-efficient energy management in multi-source renewable energy microgrids. PSO algorithm is used to find out optimal energy mixing rates that can minimize daily energy cost of a renewable microgrids under energy balance and antiislanding constraints. The optimal energy mixing rates can be used by multi-pulse width modulation (M-PWM) energy mixer units. In our numerical analyses, we consider a multi-source renewable energy grid scenario that includes solar energy system, wind energy system, battery system and utility grid connection. We assume that variable energy pricing is used in utility grid to control energy dispatches between microgrids. This numerical analysis shows that the proposed scheme can adjust energy mixing rates for M-PWM energy mixers to achieve the cost-efficient and energy balanced management of microgrid under varying generation, demand and price conditions. The proposed method illustrates an implementation of distributed intelligence in smart grids.en_US
dc.description.sponsorshipIEEE Turkey Sect,Anatolian Scien_US
dc.identifier.isbn978-1-5386-1880-6
dc.identifier.scopus2-s2.0-85039916414en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11616/98089
dc.identifier.wosWOS:000426868700003en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2017 International Artificial Intelligence and Data Processing Symposium (Idap)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectIntelligent systemsen_US
dc.subjectparticle swarm optimizationen_US
dc.subjectcost efficient energy mixingen_US
dc.subjectmicrogriden_US
dc.subjectsmart griden_US
dc.titleMulti-Source Energy Mixing for Renewable Energy Microgrids by Particle Swarm Optimizationen_US
dc.typeConference Objecten_US

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