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dc.contributor.authorVaran, Metin
dc.contributor.authorErduman, Ali
dc.contributor.authorMenevseoglu, Furkan
dc.date.accessioned2023-11-08T07:10:45Z
dc.date.available2023-11-08T07:10:45Z
dc.date.issued2023en_US
dc.identifier.citationMetin Varan, Erduman, A., & Furkan Menevşeoğlu. (2023). A Grey Wolf Optimization Algorithm-Based Optimal Reactive Power Dispatch with Wind-Integrated Power Systems. Energies, 16(13), 5021–5021. https://doi.org/10.3390/en16135021 ‌en_US
dc.identifier.urihttp://dx.doi.org/10.3390/en16135021
dc.identifier.urihttps://hdl.handle.net/20.500.14002/2062
dc.description.abstractKeeping the bus voltage within acceptable limits depends on dispatching reactive power. Power quality improves as a result of creating an effective power flow system, which also helps to reduce power loss. Therefore, optimal reactive power dispatch (ORPD) studies aim at designing appropriate system configurations to enable a reliable operation of power systems. Establishment of such a configuration is handled through control variables in power systems. Various control variables, such as adjusting generator bus voltages, transformer tap locations, and switchable shunt capacitor sizes, are utilized to achieve this objective. Additionally, the integration of wind power can greatly impact power quality and mitigate power loss. In this study, the Grey Wolf Optimization (GWO) approach was applied to the ORPD issue for the first time to discover the best placement of newly installed wind power in the power system while taking into account tap changer settings, shunt capacitor sizes, and generated power levels. The main objective was to determine optimal wind placement to minimize power loss and voltage deviation, while maintaining control variables within specified limits. On the basis of IEEE 30-bus and IEEE 118-bus systems, the performance of the proposed method was investigated. The results demonstrated the superiority of GWO in multiple scenarios. In IEEE-30, GWO outperformed the PSO, GA, ABC, OGSA, HBMO, and HFA methods, reducing total loss by 10.36%, 18.03%, 9.19%, 7.13%, 5.23%, and 7.73%, respectively, and voltage deviation by 68.00%, 1.59%, 36.34%, 41.97%, 46.29%, and 71.08%, respectively. In wind integration scenarios, GWO achieved the simultaneous reduction of power loss and voltage deviation. In IEEE-118, GWO outperformed the ABC, PSO, GSA, and CFA methods, reducing power loss by approximately 19.91%, 16.83%, 14.09%, and 4.36%, respectively, and voltage deviation by 8.50%, 14.15%, 16.19%, and 7.17%, respectively. These promising results highlighted the potential of the GWO algorithm to facilitate the integration of renewable energy sources, and its role in promoting sustainable energy solutions. In addition, this study conducted an analysis to investigate site-specific wind placement by using the Weibull distribution function and commercial wind turbines.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.ispartofENERGIESen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectreactive power dispatch; optimum power flow; grey wolf optimization (GWO); wind power; renewable power integrationen_US
dc.subjectPARTICLE SWARM OPTIMIZATION; IMPROVED GENETIC ALGORITHM; REAL; FLOWen_US
dc.titleA Grey Wolf Optimization Algorithm-Based Optimal Reactive Power Dispatch with Wind-Integrated Power Systemsen_US
dc.typearticleen_US
dc.authorid0000-0001-6099-6768en_US
dc.authorid0000-0003-4116-3159en_US
dc.authorid0009-0009-6193-998Xen_US
dc.departmentFakülteler, Teknoloji Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.institutionauthorVaram, Metin
dc.institutionauthorErduman, Ali
dc.institutionauthorMenevseoglu, Furkan
dc.identifier.doi10.3390/en16135021en_US
dc.identifier.volume16en_US
dc.identifier.issue13en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarıen_US
dc.authorwosidGDS-3282-2022en_US
dc.authorwosidIYH-3430-2023en_US
dc.authorwosidIYD-6142-2023en_US
dc.authorscopusid48661591200en_US
dc.authorscopusid55537389700en_US
dc.authorscopusid58487548100en_US
dc.identifier.wosqualityQ3en_US
dc.identifier.wosWOS:001028256000001en_US
dc.identifier.scopus2-s2.0-85164769843en_US


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