Previsão espacial da densidade de carga nos sistemas de distribuição de energia elétrica considerando a geometria fractal da zona urbana

• Ano de defesa: 2014
• Autor(a) principal: Melo Trujillo, Joel David [UNESP]
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual Paulista (Unesp)
• Palavras-chave em Português: Energia elétrica - Distribuição; Sistemas de energia eletrica - Planejamento; Carga e distribuição elétrica; Fractais; Electric power distribution
• Link de acesso: http://hdl.handle.net/11449/111139

This thesis presents a grid-based simulation method for spatial load forecasting studies in the electric distribution systems in the urban area. This approach can be considered as a useful tool to aid planning engineers in the process of distribution systems expansion planning in the mid- and long-term. The fractal geometry concepts are used to improve the characterization of the spatial pattern of the load density in the urban area considering the available data on the electrical distribution utilities, using a data georeferenced of network elements and maps of the urban area. One of the contributions of this work is a tool to jointly simulate: the expansion of the city, the free decision of inhabitants to populate regions, and the influence of urban infrastructure to attract or repel new users. Thus, this model can help creating future scenarios in the process of distribution systems expansion planning considering the dynamic growth of the urban area. The proposed method was tested in a real distribution system of a medium-sized city. The result is a map of the spatial distribution of the load density simulated in the study area, which shows the subareas with high growth in their load density. This map allows the verification of the distribution system capacity in order to meet the load growth. The proposed model is evaluated using a spatial analysis of the loads allocation error in the simulation of the actual load density; it shows an efficacy to characterize the spatial pattern of consumption of electricity with a simulation global error inferior to 3% of the total load installed in the city.