Optimizing Spatial Input Data for Techno-Economic Modeling of Least-Cost Electrification Pathways in Zambia
Abstract
Zambia’s current population is approximately 19.4 million, growing at an annual rate of about 2.9%. Only 40% of Zambia’s population has access to electricity, leaving roughly 11.7 million people without modern energy services. Since 2008, the Rural Electrification Authority of Zambia embarked on an ambitious program to increase the rural electrification rate from 11% to about 51% by 2030. However, this goal may not be realized at the current electrification rate.
This article presents an analysis of techno-economic electrification pathways for Zambia using the Open-Source Spatial Electrification Toolkit. While the Rural Electrification Authority of Zambia aims to increase electrification from 11% to 51%, this research targets achieving 100% access to electricity by 2030 to meet the United Nations Sustainable Development Goal 7.1. The study involves national-scale modeling of lowest-cost technology options for 750,000 distinct population settlement clusters, based on the least cost of electricity to supply electricity to settlements using a range of technologies while benchmarking the study with the Global Electrification Platform. In this study, data for Zambia was improved and validated by enhancing the accuracy of population clusters and the electricity grid network. This was achieved by combining several new datasets from gridfinder.org, Geo-Referenced Infrastructure, and Demographic Data for Development - three, WorldPop, the United States Agency for International Development Demographic and Health Surveys and engaging with the national Zambian Electricity Supply Company. The results are benchmarked against the latest publicly available scenarios in the Global Electrification Platform using the same version of the Open-Source Spatial Electrification code, thus only testing the effects of the population and grid datasets on the results. Remarkably great similarity of about 99.95% of the total investment cost requirement between the standalone solar system and the grid extension was realized by 2030 but with a 10% decrease in investments in grid extension. Standalone solar systems have seen a corresponding rise, primarily because the gridfinder.org dataset identified numerous false positive grids which refer to instances where a dataset incorrectly identifies areas as having existing electrical grid infrastructure when, in reality, they do not. Standalone solar systems are found to be less costly than grid extensions by a factor of 10, saving almost US$33 million.