Two methodologies are presented for analyzing the choice between centralized and decentralized energy infrastructures from a least-cost perspective. The first of these develops a novel minimum spanning tree network algorithm to approximate the shortest-length network that connects a given fraction of total system population. This algorithm is used to identify high priority locations for decentralized electrification in 150 countries. The second methodology utilizes a mixed-integer programming framework to determine the least-cost combination of centralized and decentralized electricity infrastructure that is capable of serving demand throughout a given system. This methodology is demonstrated through a case study of Rwanda. The centralized-decentralized electrification paradigm is also approached from an energy security perspective, incorporating stochastic events and probabilistic parameters into a simulation model that is used to compare different development paths. The impact of explicitly modeling stochastic events as opposed to utilizing a conventional formulation is also considered Finally, a subsidy-free lighting cost curve is developed and a model is presented to compare the costs and benefits of three different financial mechanisms that can be employed to make capital intensive energy systems more accessible to rural populations. The optimal contract is determined on the basis of utility-maximization for a range of costs to the providing agency and a comprehensive single and multi-factor sensitivity analysis is performed.