Quantum Genetic Algorithm for Dynamic Economic Dispatch of Active Distribution Network with Microgrid Including Renewable Energy Source

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dc.contributor.authorAndic, Cenk
dc.contributor.authorÖztürk, Ali
dc.contributor.authorTürkay, Belgin Emre
dc.date.accessioned2025-10-11T20:45:23Z
dc.date.available2025-10-11T20:45:23Z
dc.date.issued2024
dc.departmentDüzce Üniversitesien_US
dc.descriptionIEEE SMC; IEEE Turkiye Sectionen_US
dc.description2024 Innovations in Intelligent Systems and Applications Conference -- ASYU 2024 -- Ankara -- AX6204562en_US
dc.description.abstractThis paper presents a Quantum Genetic Algorithm (QGA) for the Dynamic Economic Dispatch (DED) of Active Distribution Networks (ADNs) with a Microgrid (MG) including renewable energy source. The economic dispatch problem aims to minimize the operating cost of the power system while meeting the load demand and satisfying operational constraints. The integration of renewable energy sources, such as wind and solar, into the power system presents new challenges due to their intermittent and uncertain nature. The proposed QGA-based DED approach considers the uncertainties of renewable energy source and enables the effective optimization of the system operation. The QGA combines the advantages of both quantum computing and genetic algorithm to provide a more efficient and effective solution. Therefore, quantum-bits (qubits) offer a much wider computational capability than the classical bits used in traditional genetic algorithms. The proposed approach is tested on the IEEE 37 bus system with two photovoltaic systems, and the results demonstrate its superior performance compared to other well-known methods which are Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. The proposed QGA provides an effective solution for the DED problem in ADNs with MGs and renewable energy sources, which can contribute to the development of sustainable and efficient power systems. © 2024 Elsevier B.V., All rights reserved.en_US
dc.identifier.doi10.1109/ASYU62119.2024.10757003
dc.identifier.isbn9798350379433
dc.identifier.scopus2-s2.0-85213401764en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ASYU62119.2024.10757003
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21325
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_Scopus_20250911
dc.subjectActive Distribution Networken_US
dc.subjectDynamic Economic Dispatchen_US
dc.subjectMicrogridsen_US
dc.subjectPhotovoltaicen_US
dc.subjectQuantum Genetic Algorithmen_US
dc.subjectMicrogridsen_US
dc.subjectParticle Swarm Optimization (pso)en_US
dc.subjectPower Distribution Networksen_US
dc.subjectScheduling Algorithmsen_US
dc.subjectActive Distribution Networken_US
dc.subjectActive Distributionsen_US
dc.subjectDynamic Economic Dispatchen_US
dc.subjectEconomic Dispatch Problemsen_US
dc.subjectEffective Solutionen_US
dc.subjectMicrogriden_US
dc.subjectPhotovoltaicsen_US
dc.subjectPoweren_US
dc.subjectQuantum Genetic Algorithmen_US
dc.subjectRenewable Energy Sourceen_US
dc.subjectQuantum Computersen_US
dc.titleQuantum Genetic Algorithm for Dynamic Economic Dispatch of Active Distribution Network with Microgrid Including Renewable Energy Sourceen_US
dc.typeConference Objecten_US

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