Location-based optimal sizing of hybrid renewable energy systems using deterministic and heuristic algorithms

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dc.authoridDemolli, Halil/0000-0001-6474-3549
dc.authoridGokcek, Murat/0000-0002-7951-4236
dc.authoridDokuz, Ahmet Sakir/0000-0002-1775-0954
dc.contributor.authorDemolli, Halil
dc.contributor.authorDokuz, Ahmet Sakir
dc.contributor.authorEcemis, Alper
dc.contributor.authorGokcek, Murat
dc.date.accessioned2024-11-07T13:25:09Z
dc.date.available2024-11-07T13:25:09Z
dc.date.issued2021
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractThe application of renewable energy sources in electrical energy generation is becoming widespread due to the decrease of installation costs and the increase of environmental concerns. Hybrid power generation systems are advantageous to meet the load demand, but optimal sizing is the main concern for having a cost-effective system based on given load demand and techno-economic indicators. This paper proposes a deterministic algorithm and utilizes genetic and artificial bee colony (ABC) optimization algorithms for optimal sizing of PV/battery and PV/WT/battery hybrid systems with minimum levelized cost of electricity (LCOE) constraint for two locations, Nigde and Bozcaada, in Turkey. The loss of power supply probability (LPSP) is used to build a reliable system and to make sure that the system produces required energy. Experimental results showed that optimal sizing of each location is different due to different wind and solar characteristics of locations. PV/battery model is more suitable for Nigde location with 1.22% LPSP and 0.1514 [$/kWh] LCOE, while PV/WT/battery model is more cost-efficient for Bozcaada location with 1.952% LPSP and 0.0872 [$/kWh] LCOE. Time performances of the algorithms are also investigated. It has been seen that the ABC algorithm has better performance and less execution time. This study demonstrated that heuristic algorithms are more applicable than deterministic algorithms, due to fast discovery of optimal solutions for hybrid renewable energy systems.
dc.identifier.doi10.1002/er.6849
dc.identifier.endpage16175
dc.identifier.issn0363-907X
dc.identifier.issn1099-114X
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85106015401
dc.identifier.scopusqualityQ1
dc.identifier.startpage16155
dc.identifier.urihttps://doi.org/10.1002/er.6849
dc.identifier.urihttps://hdl.handle.net/11480/14543
dc.identifier.volume45
dc.identifier.wosWOS:000652058700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofInternational Journal of Energy Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectheuristic algorithms
dc.subjecthybrid renewable energy systems
dc.subjectLCOE
dc.subjectLPSP
dc.subjectphotovoltaic system
dc.subjectwind energy
dc.titleLocation-based optimal sizing of hybrid renewable energy systems using deterministic and heuristic algorithms
dc.typeArticle

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