Fitness-Distance Balance based adaptive guided differential evolution algorithm for security-constrained optimal power flow problem incorporating renewable energy sources

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dc.authoridDuman, Serhat/0000-0002-1091-125X
dc.authoridkahraman, hamdi/0000-0001-9985-6324
dc.authorwosidDuman, Serhat/O-9406-2014
dc.authorwosidkahraman, hamdi/N-5248-2014
dc.contributor.authorGuvenc, Ugur
dc.contributor.authorDuman, Serhat
dc.contributor.authorKahraman, Hamdi Tolga
dc.contributor.authorAras, Sefa
dc.contributor.authorKati, Mehmet
dc.date.accessioned2021-12-01T18:50:05Z
dc.date.available2021-12-01T18:50:05Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractOne of the most difficult types of problems computationally is the security-constrained optimal power flow (SCOPF), a non-convex, nonlinear, large-scale, nondeterministic polynomial time optimization problem. With the use of renewable energy sources in the SCOPF process, the uncertainties of operating conditions and stress on power systems have increased even more. Thus, finding a feasible solution for the problem has become a still greater challenge. Even modern powerful optimization algorithms have been unable to find realistic solutions for the problem. In order to solve this kind of difficult problem, an optimization algorithm needs to have an unusual exploration ability as well as exploitation-exploration balance. In this study, we have presented an optimization model of the SCOPF problem involving wind and solar energy systems. This model has one problem space and innumerable local solution traps, plus a high level of complexity and discrete and continuous variables. To enable the optimization model to find the solution effectively, the adaptive guided differential evolution (AGDE) algorithm was improved by using the Fitness-Distance Balance (FDB) method with its balanced searching and high-powered diversity abilities. By using the FDB method, solution candidates guiding the search process in the AGDE algorithm could be selected more effectively as in nature. In this way, AGDE's exploration and balanced search capabilities were improved. To solve the SCOPF problem involving wind and solar energy systems, the developed algorithm was tested on an IEEE 30-bus test system under different operational conditionals. The simulation results obtained from the proposed algorithm were effective in finding the optimal solution compared to the results of the metaheuristics algorithms and reported in the literature. (C) 2021 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.asoc.2021.107421
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85104806872en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2021.107421
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10825
dc.identifier.volume108en_US
dc.identifier.wosWOS:000670068500009en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFitness-Distance Balanceen_US
dc.subjectAdaptive guided differential evolutionen_US
dc.subjectRenewable energy sourcesen_US
dc.subjectOptimal power flowen_US
dc.subjectOptimization Algorithmen_US
dc.subjectGlobal Optimizationen_US
dc.subjectStochastic Winden_US
dc.subjectCuckoo Searchen_US
dc.subjectIntelligenceen_US
dc.subjectEmissionen_US
dc.subjectSystemen_US
dc.subjectSolveen_US
dc.subjectTestsen_US
dc.subjectSolaren_US
dc.titleFitness-Distance Balance based adaptive guided differential evolution algorithm for security-constrained optimal power flow problem incorporating renewable energy sourcesen_US
dc.typeArticleen_US

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