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Rural electrification planning based on graph theory and geospatial data: A realistic topology oriented approach
Sustainable Energy Grids & Networks  (IF3.899),  Pub Date : 2021-08-05, DOI: 10.1016/j.segan.2021.100525
Gadelha T. Filho Vinicius, Corigliano Silvia, Dimovski Aleksandar, Bolognesi Massimo, Merlo Marco

By the year 2019, the number of people without access to electricity was 770 million, most of which lived in rural areas. The currently models for rural electrification are often limited in their electrical analysis, or focus on a idealistic optimal solution whilst ignoring the real hierarchical topology of power systems. This work proposes a rural electrification strategy that makes use of Geographic Information System (GIS), graph theory and terrain analysis to create the best electric network topology. It uses the GIS for Electrification (GISEle) tool while improving the former topological-focused analysis, to one that considers other aspects of electric network planning such as cable sizing based on current-carrying capacity constraints and the sharing of substations connections. By considering these factors, a better and more realistic topology is achieved. Furthermore, an electrical analysis was performed to endorse the topology found, by executing load flow analysis in critical parts of the grid, in order to investigate steady-state voltages, as well as substation and line loading. The strategy proposed was applied to a real case study of grid expansion in the municipality of Cavalcante, in a rural area in Brazil. The goal is to find the most cost-efficient network topology reaching up to 100% of the local population through an expansion of the MV distribution network. The results show that by using the new approach proposed, a reduction of up to 47% (compared to a standard minimum spanning tree procedure) of the total investment cost in line deployment was achieved. This reduction is possible through a proper sizing, based on the power supplied, of the MV cables used in the electrification planning.