Sistema de gerenciamento de energia orientado à arbitragem energética e recomendações para operação isolada de microrredes

• Ano de defesa: 2023
• Autor(a) principal: Alves, Guilherme Henrique
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Engenharia Elétrica
• Palavras-chave em Português: Gerenciamento de energia; Incerteza; Mercados de energia; Microrredes; Interoperabilidade; Ilhamento intencional; Tarifa dinâmica de eletricidade; Energy management; Uncertainty; Power markets; Microgrids; Interoperability; Intentional islanding; Dynamic electricity pricing; Engenharia elétrica; Guimarães, Geraldo Caixeta, 1954-, (Orient.); CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA::SISTEMAS ELETRICOS DE POTENCIA; Energia elétrica - Distribuição - Alta tensão; Redes elétricas inteligentes; Redes elétricas - Analisadores
• Link de acesso: https://repositorio.ufu.br/handle/123456789/39042; http://doi.org/10.14393/ufu.te.2023.457

In order to optimize the operation and planning of microgrids (MG), it is necessary to carry out a set of analyzes of technical and econometric data. In this aspect, decision-making metrics oriented to energy arbitrage can fit, among others. This approach is important for the development of the modern electrical system, as it allows better integration of distributed generation (DG) and battery energy storage systems (BESS). The use of algorithms based on artificial intelligence (AI) for the energy management system (EMS) can help to improve the MG operation to achieve the lowest possible cost in the process of buying and selling electricity, and consequently, increase sustainability levels. Thus, in the first stage of the Thesis, two strategies are proposed for energy management in the MG to determine the instants of charge and discharge of the SAEB. A simple heuristic method is used to serve as a reference for comparison with the fuzzy logic (FL) operation developed. Furthermore, other algorithms based on artificial neural networks (ANNs) are proposed using the nonlinear autoregressive technique to predict MG variables. During the research, the developed algorithms were evaluated through extensive case studies, with simulations that used data from the photovoltaic (PV) system, load demands, and electricity prices. For all cases, the AI algorithms for predictions and actions managed to reduce the cost and daily consumption of electricity from the main network, compared with the heuristic method or the MG without using BESS. This indicates that the developed power management strategies can be applied to reduce the costs of grid-connected MG operations. In the other stage of this research, an alternative is proposed to keep the MG operating through intentional islanding, in case of failures that cause the disconnection of the energy supply, aiming to guarantee the continuity of the service of priority loads through a contract between the utility and the manager of GD-MG, according to the IEEE 1547 technical guidelines. The results obtained by the simulations have shown excellent levels in the quality of electric energy supply for the input situation of the stabilized MG loads, after the intentional islanding, thus confirming that this proposal is within the technical guidelines of IEEE 1547. The simulations results confirm that this proposal comply with the technical guidelines of IEEE 1547, guaranteeing high quality in supplying electrical energy after the intentional islanding of the stabilized MG loads.