Optimal Power Flow with Renewable Generation: A Modified NSGA-II-based Probabilistic Solution Approach

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUFMT-
Autor(es): dc.contributorCTG Brasil-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorAraujo, Elaynne Xavier Souza-
Autor(es): dc.creatorCerbantes, Marcel Chuma-
Autor(es): dc.creatorMantovani, José Roberto Sanches [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:34:30Z-
Data de disponibilização: dc.date.available2022-02-22T00:34:30Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-08-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/s40313-020-00596-7-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/201782-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/201782-
Descrição: dc.descriptionThe rapid expansion of renewable generation has drastically increased the planning complexity of modern power systems as additional uncertainties, environmental concerns, and technical–economic issues should be accounted for. Within this context, the best operation performance of contemporary power system operators (SOs) depends not just on tractable realistic optimal power flow (OPF) formulations, but also on powerful optimization approaches. In this work, a tractable life-like multi-objective probabilistic OPF-based model for the SO’s medium-term operation considering high penetration of renewable resources is proposed. This model includes an explicit formulation of the operation of dispatchable and non-dispatchable generation, shunt reactive power sources, and under-load tap-changing (ULTC) transformers. The resulting model is a large-scale probabilistic multi-objective non-convex nonlinear mixed-integer programming (NLMIP) problem with continuous, discrete, and binary variables. To ensure tractability, uncertainties are modeled through a fast and efficient 2m probabilistic approach. To handle the nonlinearities and non-continuous variables that characterize the problem, a modified non-dominated sorting genetic algorithm (NSGA)-II solution approach is proposed and effectively tested.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionUniversidade Estadual Paulista-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionFederal University of Mato Grosso UFMT-
Descrição: dc.descriptionChina Three Gorges Brasil CTG Brasil-
Descrição: dc.descriptionState University of São Paulo UNESP-
Descrição: dc.descriptionState University of São Paulo UNESP-
Descrição: dc.descriptionUniversidade Estadual Paulista: 028/2017-
Descrição: dc.descriptionFAPESP: 2013/13070-7-
Descrição: dc.descriptionFAPESP: 2015/21972-6-
Descrição: dc.descriptionCNPq: 305318/2016-0-
Formato: dc.format979-989-
Idioma: dc.languageen-
Relação: dc.relationJournal of Control, Automation and Electrical Systems-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectMulti-objective optimization-
Palavras-chave: dc.subjectNSGA-II-
Palavras-chave: dc.subjectOptimal power flow-
Palavras-chave: dc.subjectRenewable generation-
Título: dc.titleOptimal Power Flow with Renewable Generation: A Modified NSGA-II-based Probabilistic Solution Approach-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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