Metodologia para análise de impactos associados a elevada inserção de microgeração fotovoltaica em redes secundárias de distribuição

• Ano de defesa: 2020
• Autor(a) principal: Lima, Roger Hatwig de
• Tipo de documento: Dissertação
• 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: Microgeração fotovoltaica conectada à rede de distribuição secundária; Capacidade de hospedagem; PRODIST; Qualidade do produto; Sobretensão; Desequilíbrio de tensão; Photovoltaic microgeneration connected to the secondary distribution network; Hosting capacity; Product quality; Overvoltage; Voltage imbalance; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://repositorio.ufsm.br/handle/1/22630

In recent years, there has been an exponential growth in the number of photovoltaic installations, mainly in secondary distribution systems, as a result of the beginning of the modernization of the electricity sector, based on new regulations, government incentives and more accessible technologies. However, the vast majority of existing low voltage networks were not designed to absorb a large volume of distributed generation, mainly residential feeders, which have relatively low demand during the day, a period when photovoltaic generation reaches its peak, which can cause reversal of the energy flow in the feeder, resulting in a series of technical challenges for the energy distributors, mainly overvoltage and increase in the voltage imbalance index. Thus, there is a need to conduct studies on the hosting capacity of photovoltaic in Brazilian distribution networks, with the objective of guaranteeing quality electrical energy, without limiting the expansion of photovoltaic energy. This dissertation presents a method using Monte Carlo simulations (MMC) to analyze the main impacts associated with high photovoltaic insertion in secondary distribution networks, in order to estimate its hosting capacity. In short, the MMC consists of the generation of thousands of random scenarios, where the position of each photovoltaic system is established based on a pseudo-random number generated by the program. For each scenario generated, the power flow is calculated and the results of the impacts are collected, for later probabilistic analysis. The algorithm is implemented in Python programming language, which uses the OpenDSS COM interface for power flow calculations. A comprehensive case study is performed, using data from a typical Brazilian secondary distribution network, where different rates of photovoltaic insertion are applied to analyze the main associated impacts. Following the study, some network parameters, such as cable length and section, and loads, such as load and demand profile, are changed to assess their relationship with impacts.