Métodos híbridos de otimização para despacho econômico e alocação de geradores distribuídos e estações de carregamento de veículos elétricos

• Ano de defesa: 2021
• Autor(a) principal: Silva, Felipe Zamborlini da
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Mestrado em Engenharia Elétrica; Centro Tecnológico; UFES; Programa de Pós-Graduação em Engenharia Elétrica
• Palavras-chave em Português: Programação não linear; algoritmos genéticos; despacho econômico; lobos cinzentos; geração distribuída; veículos elétricos; subject.br-rjbn; Engenharia Elétrica
• Link de acesso: http://repositorio.ufes.br/handle/10/15541

The greater yearn for a less polluting, sustainable and efficient energy consumption has fostered the search for electric vehicles as a mean to mitigate the intrinsic pollution of the actual transport system, which is a high consumer of fossil fuels. Nonetheless, the increase in the number of electric vehicles will ensue on an equivalent increase in the distribution system power demand. Thus, investments in renewable generation systems, implemented through distributed generation, are necessary to deal with these loads, otherwise, it would only alter the polluting source. The insertion of distributed generators in concomitance with electric vehicles loads, which are extremely stochastic, have an impact on the network dynamics and requires the application of optimization techniques to ensure that these assets are used to their best benefit. Therefore, in the present work, is proposed an application of two hybrid optimization methods: the Genetic Algorithms-Interior Points Method and the Grey Wolves-Interior Points Method; two techniques that combine metaheuristic methods, that possess the function of realizing the allocation of electric vehicle charging stations and distributed generators on the grid, with a classic method, that possess the function of defining the economic dispatch of the generators, aiming at minimizing the system operational cost. The proposed methods stand out as an alternative for the solution of problems that are not feasible solely through classic methods by ensuring its feasibility and the global optimum of part of the solution. Both methods proved to be effective showing similar results, reducing the operational cost by, approximately, 13.10% and 13.11%, respectively, when compared to the original system cost.