Optimal operation and planning of hybrid AC/DC power systems using multi-objective grasshopper optimization algorithm

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dc.authoridBakır, Hüseyin/0000-0001-5473-5158
dc.authoridKAHRAMAN, Hamdi Tolga/0000-0001-9985-6324
dc.authoridguvenc, ugur/0000-0002-5193-7990
dc.authorwosidBakır, Hüseyin/HMO-5183-2023
dc.authorwosidKAHRAMAN, Hamdi Tolga/AAW-5335-2020
dc.authorwosidguvenc, ugur/H-3029-2011
dc.contributor.authorBakır, Hüseyin
dc.contributor.authorGüvenç, Uğur
dc.contributor.authorKahraman, Hamdi Tolga
dc.date.accessioned2023-07-26T11:57:16Z
dc.date.available2023-07-26T11:57:16Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractOptimal power flow (OPF) in a hybrid alternating current and multi-terminal high-voltage direct current (AC-MTHVDC) grid is currently one of the most popular optimization problems in modern power systems. The critical necessity of addressing global warming and reducing generation costs is encouraging the integration of eco-friendly renewable energy sources (RESs) into the OPF problem. In this direction, the present research has centred on the formulation and solution of the multi-objective (MO) AC-MTHVDC-OPF problem incorporating RESs such as wind, solar, small-hydro, and tidal power. The available power of RESs is calculated by means of the Weibull, lognormal, and Gumbel probability density functions. The proposed MO-OPF optimizes the double and triple configurations of various objective functions, including total cost, the total cost with the valve-point effect, the total cost with emission and carbon tax, voltage deviation, and power loss. Multi-objective grasshopper optimization algorithm (MOGOA) is applied to find non-dominated Pareto-optimal solutions of the non-convex, nonlinear and high-dimensional MO/AC-MTHVDC-OPF problem. The obtained results are compared with the results of MSSA, MODA, MOALO, and MO_Ring_PSO_SCD algorithms. The comparison of results gives that MOGOA outperforms competitive optimizers with respect to the quality of Pareto-optimal solutions and their distribution.en_US
dc.identifier.doi10.1007/s00521-022-07670-y
dc.identifier.endpage22563en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue24en_US
dc.identifier.scopus2-s2.0-85137466754en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage22531en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-022-07670-y
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13101
dc.identifier.volume34en_US
dc.identifier.wosWOS:000849459900001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorGüvenç, Uğur
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectMulti-Objective Optimal Power Flow; Renewable Energy Sources; Multi-Terminal High-Voltage Direct Current System; Multi-Objective Grasshopper Optimization Algorithmen_US
dc.subjectIncorporating Stochastic Wind; Flow Solution; Losses; Emission; Costen_US
dc.titleOptimal operation and planning of hybrid AC/DC power systems using multi-objective grasshopper optimization algorithmen_US
dc.typeArticleen_US

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