A scalable framework to measure the impact of spatial heterogeneity on electrification

We propose a scalable computational framework to examine the effects of settlement patterns on the electrification of an entire country. We first propose a data processing strategy to convert structure locations, identified from satellite imagery, to estimated household locations using census data. Then, we present a computational framework that involves a two-level network design algorithm to find an abstract representation of the power distribution system at a national scale involving low voltage (LV) wires, medium voltage (MV) wires, and the transformers between the two levels of the system. Given the system components, we introduce three metrics for per-household connectivity requirements of LV and MV wires, and transformers to interpret our results at the administrative and the sub-administrative unit levels. With our administrative level analysis provided for 9.2 million structures in Kenya, we show that traditional rural/urban classification based on population density may not be enough and is often deceiving in estimating the cost of electrification and a new categorization based on our metrics provides more relevant estimates on the total cost. Moreover, our metrics can help determine the least-cost electrification option (e.g.,grid, mini-grid, or stand-alone systems) for expanding access in the sub-administrative unit level and create a platform to perform sensitivity analysis based on different cost components. Our work demonstrates the potential for improvements in universal electrification combining new and more detailed data sources with a scalable planning framework and helps governments achieve Sustainable Development Goal 7 (SDG7) more quickly and at lower cost.