  In many developing countries, access to electricity remains a significant
challenge. Electrification planners in these countries often have to make
important decisions on the mode of electrification and the planning of
electrical networks for those without access, while under resource constraints.
An integrated approach to electrification planning in which traditional grid
electrification is complemented off-the-grid technologies such as off-grid
microgrids and stand-alone systems can enable the economic provision of
electricity access in these regions. This integrated planning approach can be
facilitated by determining the least-cost mode of electrification - i.e by
electric grid extension or off-grid systems - for non-electrified consumers in
a region under analysis, while considering technical, economic and
environmental constraints. Computational clustering methods the identification
of consumer clusters (either as clusters of off-grid microgrids, stand-alone
systems or grid-extension projects) can be undertaken using computational
clustering methods. This paper presents a novel computational approach to
achieve this purpose. This methodology involves exploiting the grid network
that connects all consumers, by greedily partitioning the network to identify
clusters of consumers to be electrified by grid-extension and off-grid
microgrid systems. Using test cases and sensitivity analyses, we implement and
benchmark this top-down approach with those obtained from a bottom-up
clustering methodology used by the Reference Electrification Model, a model
obtainable in literature. Results presented show that the alternative top-down
methodology proposed can compare favorably, in terms of global electrification
costs, with a bottom-up approach to rural electrification planning.
