A High-Resolution Analysis of Electricity Demand for Informed Electrification Planning
Electrification planning necessitates a nuanced understanding of electricity demand. However, previous studies have largely overlooked regional differences and relied on a one-size-fits-all approach to demand estimation. This study aims to examine the spatial distribution of electricity demand across households and community institutions in a developing country context, using Ethiopia as a case for the study. The study employed a multifaceted approach: household demand was estimated using the Multi-Tier Framework (MTF), while the demand for community institutions (healthcare, education facilities, places of worship, and government offices) was estimated using the Remote-Areas Multi-energy systems load Profiles (RAMP) stochastic model. The spatial distribution was visualized using the OnSSET methodology. The findings reveal significant demand heterogeneity, with 64% of households falling within Tier 1 and Tier 2 demand levels assuming all households have electricity access, but 61.3% of currently electrified households exhibit Tier 3 in 2020. Additionally, community institutions exhibited electricity demand patterns, with health facilities requiring the highest average demand (around 2,100 kWh/year), followed by education facilities (400 kWh/year). Insights from these high-resolution demand assessments can inform the selection of cost-optimal electrification technologies (e.g., grid extension, mini-grids, standalone systems) based on the demand patterns of specific communities at local level in Ethiopia.
Authors
- Adugnaw Lake Temesgen (ORCID: https://orcid.org/0009-0009-8105-8020)
- Erik O. Ahlgren (ORCID: https://orcid.org/0000-0002-1164-0850)
- Basilio Zeloso S. Tamele
Institutions
- Eduardo Mondlane University (MZ)
- Addis Ababa University (ET)
- Chalmers University of Technology (SE)
Publication Details
- Published
- 2024-10-07
- DOI
- https://doi.org/10.1109/powerafrica61624.2024.10759429
- Citations
- 2
- Primary Topic
- Electric Vehicles and Infrastructure
- Type
- article
- Field-Weighted Citation Impact
- 0.42