Seleção ótima de baterias armazenadoras de energia em redes de distribuição com geração distribuída considerando modelagem da operação por redes neurais

• Ano de defesa: 2019
• Autor(a) principal: Rangel, Camilo Alberto Sepúlveda
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Engenharia Elétrica; UFSM; Programa de Pós-Graduação em Engenharia Elétrica; Centro de Tecnologia
• Palavras-chave em Português: Sistemas de baterias de armazenamento de energia; Sistemas de distribuição; Geração distribuída; Redes neurais; Simulação de Monte Carlo; Perdas de energia; Custos de energia; Distribution systems; Battery energy storage systems; Distributed generation; Neural networks; Monte Carlo simulation; Energy losses; Energy cost; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://repositorio.ufsm.br/handle/1/18509

This thesis presents a methodology for optimal determination of type, bar, and capacity of Battery Energy Storage Systems (BESS) in distribution systems with distributed generation (DG) where the battery optimal operation is approximated by an input/output model created with neural networks. A genetic algorithm selects the storage by a fitness function defined with the annual operation costs of the distribution system, the voltage limits, and batteries costs. The model allows to compare different types of batteries technologies, considering its technical and economical characteristics. Lifetime of the battery is based on the depth of discharge (DOD) impact to the life cycle. The database for the input/output model is obtained by a Monte Carlo simulation of the optimal daily operation of the battery for a representative sample from a yearly real data. This approach allows to consider the stochastic behavior of the distributed generation, the load and the energy prices. The daily operation of the battery is optimized by a nonlinear optimization model, considering a load flow by OpenDSS proprietary software from the Electric Power System Research Institute (EPRI). The neural network was based on the Group Method of Data Handling (GMDH). The neural network implementation allows to reduce the yearly simulation time, where the possible selection alternatives are chosen by the genetic algorithm. This methodology is tested in a distribution system of 33 nodes, and the generation, demand, and prices curves are taken from data of the Independent Electricity System Operator IESO relative to the Canadian distribution system, considering solar and wind as renewable sources. The studied case shows a good approximation of the neural network with the obtained data for the daily load flow and allows to identify the critic cases of the systems, as bar location not allowed and probability of risk of the results. The results compare the use of the batteries in the distribution network, reducing losses and operational costs along the day in the system and selecting the best type. Also, the storage systems can reduce the final energy cost of the system (limited by the proposed constraints) and the loses, with the possibility to determine the best alternative.