Posicionamento e Dimensionamento de Geração Distribuída em Rede de Distribuição de Energia Elétrica

• Ano de defesa: 2020
• Autor(a) principal: Belmino, Lucas Martins
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Evolução diferencial; Sistema de distribuição radial; Posicionamento de geração distribuída; Dimensionamento de geração distribuída; Geração distribuída de energia elétrica - Uso
• Link de acesso: http://www.repositorio.ufc.br/handle/riufc/53323

The increase in electricity consumption has driven advances in the use of small and medium sized generators connected to the distribution system in contradiction to the previous paradigm, where the generation centers are concentrated and far from the loads. Long feeders responsible for connecting supply substations to load centers are associated with losses inherent in electrical components. In this scenario the use of Distributed Generation (DG) can contribute to the supply of energy consumption and provide improvements in power supply reducing system losses and improving voltage profile. However, in order to ensure the benefits of using DGs, positioning and sizing studies are required. Positioning and sizing are mixed integer nonlinear mathematical problems that have their solution set susceptible to accelerated growth as the number of bars in the system or generators increases. In this work we propose an algorithm to evaluate the behavior of losses as a function of the number, capacity and position of DGs allowing the analysis of the number of generating units that provides the greatest reduction of the electrical losses. In the proposed algorithm uses the meta-heuristic Differential Evolution (DE). In addition, for the representation of the electric network is used the concept of list of adjacencies, belonging to the study of graph theory. Once the representation of the distribution network is chosen, Direct-Inverse Sweep Method via Sum of Powers (DMSP) is used to calculate the power flow and the losses calculated by the method are used as an aptitude function to evaluate the possible solutions represented by the individuals. The algorithm then performs the positioning and sizing of the DGs and restarts its execution by updating the number of DGs that must be positioned. The results obtained are used to plot the loss curve and to evaluate its behavior. The algorithm is implemented and, to compare the results, simulations are performed in networks widely used in the literature, IEEE 33 bars and IEEE 69 bars. The positioning performed by DE obtained good results in reducing losses, which are close to the values found in other studies. The curve plotted by the algorithm, however, showed an accelerated reduction of initial value losses, which stabilized with the increase in the number of DGs.