The lack of access to electricity in developing countries necessitates spatial electricity planning for guiding sustainable electrification projects that evaluate the costs of centralized systems vis-a-vis decentralized approaches. Heuristic approaches have been widely used in such electrification problems to find feasible, cost effective solutions; however, most of the time global optimality of these solutions is not guaranteed. Our thesis through its modeling approach provides a new methodology to find the least cost solution to this electrification problem. We model the spatial network planning problem as Prize Collecting Steiner Tree problem which would be base for a decision support tool for rural electrification. This new method is systematically assessed using both randomly generated data and real data from rural regions across SubSaharan Africa. Comparative results for the proposed approach and a widely used heuristic method are presented based on computational experiments. Additionally, a bi-objective approach that permits to take carbon emission level into the account is implemented and experimented with numerical data.
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