Long-Term Least-Cost Electrification Pathways For Ethiopia: A Geospatial Modeling Approach High-Resolution Spatial Analysis to Bridge the Electricity Access Gap
| dc.contributor.advisor | Getachew Bekele (Assoc. Prof.) | |
| dc.contributor.advisor | Erik O. Ahlgren (Prof.) | |
| dc.contributor.advisor | Yibeltal T. Wassie (PhD) | |
| dc.contributor.author | Adugnaw Lake | |
| dc.date.accessioned | 2026-03-03T06:21:03Z | |
| dc.date.available | 2026-03-03T06:21:03Z | |
| dc.date.issued | 2025-11 | |
| dc.description.abstract | In 2015, the United Nations adopted the Sustainable Development Goals (SDGs) to guide global development efforts towards 2030. Among these, SDG 7 aims to "ensure access to affordable, reliable, sustainable, and modern energy for all by 2030." Electricity is essential for the development of many sectors, including households, health and education facilities, and productive enterprises. Yet, as of 2022, approximately 685 million people worldwide remained without electricity access, nearly 83% of whom lived in Sub-Saharan Africa, where only about half the population was electrified. In this region, rapid population growth and dispersed settlements further complicate the challenge. Achieving universal electricity access necessitates strategies tailored to the unique context of each population settlement, accounting for settlement distribution, economic activities, and resource availability. To this end, policymakers and planners increasingly employ geospatial and techno-economic assessments to inform energy policies and national electrification targets. However, geospatial electrification models require large volumes of reliable georeferenced data, from infrastructure locations to electricity consumers, which are often limited or unavailable in many developing countries. This paucity of spatial information, combined with several methodological limitations, can undermine model outcomes. These limitations include low-resolution data that mask settlement-level variations; simplified demand assumptions that overlook local socio-economic realities; and short-term planning horizons that fail to capture dynamic, long-term investment pathways. In response, this thesis develops and applies a geospatial framework using the Open-Source Spatial Electrification Tool (OnSSET) to produce phased, least-cost electrification pathways for Ethiopia through 2050. The research is guided by three specific objectives: (1) To analyze how and to what extent geospatial factors affect the feasibility of extending the national power grid to unelectrified settlements. This objective is pursued by conducting a geospatial analysis that quantifies the spatial constraints of grid extension based on factors such as distance from road and substation, terrain slope, elevation, and land cover. The results indicate that geospatial factors may increase grid extension costs by 2.3% to 29% across Ethiopia.The second specific objective is (2) To develop long-term, spatially disaggregated electricity demand projections for rural electrification planning. Existing literature offers limited insights into spatial heterogeneity in electricity demand, reducing its applicability to spatial electrification planning. This objective is pursued by projecting the electricity demand of households, productive users, and community institutions. Alternative scenarios are developed by considering electricity demand growth in rural areas of developing countries under different drivers of demand, such as population growth, urbanization, rural electricity access, and economic growth, using a multiple regression model. OnSSET is employed to spatially disaggregate electricity demand, using high-resolution data on mean gridded GDP and the International Wealth Index to classify settlements by economic status. The scenario results generate aggregate national electricity demand projections and also show how demand is expected to evolve over time for each consumer group within each settlement. The results show that electricity demand is spatially heterogeneous, with projected household demand ranging from Tier 1 to Tier 4. The final objective is (3) To explore long-term, least-cost electrification technology mix dynamics and evaluate investment needs to enhance electricity access in rural areas. A geospatial optimization model is developed using OnSSET to determine the optimal electricity supply option that provides electricity at the lowest levelized cost of electricity (LCOE) under varying demand and grid generation cost scenarios. The model integrates spatially disaggregated electricity demand, georeferenced existing grid infrastructures, renewable energy resources (solar, wind, and hydro), geospatial cost penalty factors, and techno-economic parameters. The model identifies the least-cost option for each settlement by comparing the LCOE of grid extension, mini-grid (solar, wind, and hydro), and standalone photovoltaic systems. The results reveal a dynamic technology mix that shifts over time: grid extension can be the least-cost solution for over 82% of the population planned to be electrified by 2030, with its share declining by 2050, while mini-grids become the least-cost option for about 26% of the population. This integrated, geospatial modeling, data-driven approach informs cost-effective, sustainable, and equitable electrification strategies tailored to Ethiopia’s diverse regional contexts. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/7808 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Electrification pathways | |
| dc.subject | electricity demand projections | |
| dc.subject | Ethiopia | |
| dc.subject | geospatial modeling | |
| dc.subject | OnSSET | |
| dc.subject | SDG 7 | |
| dc.subject | techno-economic assessment. | |
| dc.title | Long-Term Least-Cost Electrification Pathways For Ethiopia: A Geospatial Modeling Approach High-Resolution Spatial Analysis to Bridge the Electricity Access Gap | |
| dc.type | Dissertation |