Modelagem para tomada de decisão da migração do ambiente cativo para o mercado livre de energia

• Ano de defesa: 2021
• Autor(a) principal: Burin, Heloísa Pereira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Engenharia de Produção; UFSM; Programa de Pós-Graduação em Engenharia de Produção; Centro de Tecnologia
• Palavras-chave em Português: Setor elétrico brasileiro; Mercado livre de energia; Indicadores de desempenho; Tomada de decisão; Mensuração de desempenho; Brazilian electrical sector; Free energy market; Performance indicators; Decision-making; Performance measurement; CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO
• Link de acesso: http://repositorio.ufsm.br/handle/1/22330

Due to the constant evolution of the markets for contracting electric energy worldwide, new possibilities for purchasing energy are emerging. In Brazil, there are two environments available, the Regulated Contracting Environment (ACR) and the Free Contracting Environment (ACL). In ACR, energy is supplied to consumers by the local distribution concessionaire with tariffs regulated by the government. In the ACL, the consumer company makes the purchase directly with generators or traders, with free contract negotiations that include prices, energy volumes, and supply terms. Given these contracting options, where both present benefits, but also risks and uncertainties, it is important that consumers identify their performance given the possibility of migration, whether the choice will be an advantage or not for their business. In this sense, the general objective of this research was to build a mathematical model to support the decision-making of the migration of consumers from the captive environment to the free energy market. For this, 18 Critical Success Factors (FCS) were identified for this decision making, grouped into 4 Fundamental Points of View (PVF) that were measured by 32 performance indicators (KPI). Besides, 3 scenarios that can influence these factors and affect decision making were also investigated. The modeling was structured using the Hierarchical Process Analysis (AHP) weighting method, with judgments being made by 15 experts with experience in the energy sector. The modeling was applied in 6 companies, obtaining as a result that all have a potentially satisfactory level of performance for migration, with 69.90% the highest performance and 59.59% the lowest. However, it was found that companies can work about some indicators, managing to evolve to fully satisfactory performance.