Aplicação de Algoritmos Inteligentes na Previsão do Montante de Uso do Sistema de Transmissão para Otimização do Contrato de Uso do Sistema de Transmissão em Distribuidoras de Energia Elétrica.

• Ano de defesa: 2024
• Autor(a) principal: Cysne, Karol Damasceno
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: MUST; Previsão de Energia Elétrica; Algoritmos Inteligentes; Modelagem de Equações Estruturais; CUST; Structural Equation Modeling; Electricity Forecast; Intelligent Algorithms
• Link de acesso: http://repositorio.ufc.br/handle/riufc/76984

(ONS). CUST allows the ONS to manage and promote the optimization of the operation of the electroenergetic system and quantify the costs incurred when using the basic network so that they can be apportioned among the system’s users. In CUST, generating agents report their maximum electrical power injectable into the system while distributors and consumers report annually their MUST, defined according to the maximum demand values at each connection point with the core network and hiring schedule for the subsequent four calendar years. In order to establish the MUST value to be contracted, most electricity concessionaires rely on estimates derived from simplified and/or statistical methods based on historical electricity demand data. This work proposes the application of intelligent algorithms to predict MUST, in order to obtain values closer to reality and that support an optimization in CUST in order to obtain the lowest associated financial value. To this end, climatic, economic, temporal and operational variables are taken into account, highlighted as variables that influence the demand for electrical energy, through the use of MEE. After carrying out a correlation study between variables, the application of intelligent algorithms was proposed to predict electrical energy demand data and, based on this, optimize the CUST contracting values. The results indicated that the