Sub-Saharan Africa (SSA) faces a significant challenge in achieving Sustainable Development Goal 7 (SDG7): ensuring access to affordable, reliable, sustainable, and modern energy for all by 2030. In 2020, 581 million people in SSA lacked access to electricity, highlighting the urgency of addressing this issue. While progress has been made, the COVID-19 pandemic and population growth have reversed some gains. Constrained government budgets and unsustainable debt levels further hinder public investment. However, recent cost reductions in off-grid technologies, particularly solar photovoltaic (PV) systems and batteries, coupled with business model innovations, offer new opportunities for achieving universal electrification at a lower cost than traditional grid expansion. Existing analyses often focus on aggregate levelized cost of electricity (LCOE), neglecting the granular, country-specific feasibility and affordability of electrification. This study aims to address this gap by providing spatially explicit cost estimates for different electrification pathways in 40 SSA countries, considering the affordability gap and the context-dependency of least-cost solutions. This information is crucial for policymakers to make informed decisions and avoid building inaccessible infrastructure due to affordability issues.

Previous research highlights the context-dependent nature of least-cost electrification choices. Studies have shown that the optimal approach varies significantly depending on factors such as population density, geographic location, and the quality of the investment environment. However, there is a lack of granular, geographically explicit data on the cost of electrification to the consumer in SSA. This information is critical because the affordability gap, the difference between the cost of electrification and the consumer's ability to pay, can jeopardize electrification targets and hinder development outcomes. Existing models often fail to adequately incorporate realistic financing conditions and country-specific risks, leading to inaccurate cost estimations. This research builds on previous work by incorporating these crucial factors into a spatially explicit model, providing a more comprehensive and realistic assessment of electrification costs.

This study employs the open-source electrification model OnSSET to estimate the levelized cost of electricity (LCOE) and cost per person per day (pp/d) for 40 SSA countries. The model considers three electrification approaches: grid extension (GE), mini-grids (MG), and standalone solar systems (SAS). GE includes both grid densification and extension, limited to areas within 50 km of existing or planned grids. MGs incorporate both solar PV and hydro-powered options. SAS utilizes solar PV and batteries, with system sizing dependent on solar irradiation and household size. The model uses a 100m x 100m resolution population raster layer to achieve 100% electrification by 2030, accounting for population growth and realistic financing conditions. Different levels of energy access are considered using the World Bank's Multi-Tier Framework (MTF), classifying annual household electricity consumption based on service quality. The model calculates the least-cost electrification option for each area (cluster) and generates electrification cost curves to illustrate cost variations. The study acknowledges uncertainties in cost estimations, especially for SAS input parameters, and conducts sensitivity analyses to assess the robustness of the results. Equations are provided to calculate LCOE and cost pp/d, incorporating investment costs, operation and maintenance costs, salvage value, electricity generated, and the discount rate. The model also accounts for country-specific risks in cost of capital through differentiation between public sector financing for grid and private sector financing for off-grid solutions. Modifications to the original OnSSET code are detailed, including an updated SAS algorithm for accurate capacity estimation based on new connections. Data sources include the World Bank, IRENA, and UNDP.

The study's key findings reveal that the availability of low-cost off-grid electrification options (MGs and SAS) drastically reduces the cost of electrifying remote and high-cost areas, particularly for lower tiers of electrification. Least-cost electrification at Tier 3 is achievable at an average of 14c USD/kWh or 7c USD pp/d for all of SSA. However, this cost varies significantly between and within countries, ranging from 3c USD pp/d in Gabon to 16c USD pp/d in Eswatini. The cost curves show that off-grid solutions play a more significant role in higher cost regions. SAS acts as a cost leveller in rural areas due to lower investment costs per kW. The analysis of different demand tiers (Tier 2-4) shows increasing costs with higher consumption levels. Maximum costs pp/d reach 5c USD for Tier 2, 16c USD for Tier 3, and 40c USD for Tier 4. Affordability remains a challenge, especially at higher tiers, given the poverty levels in many SSA countries. Misguided planning or oversizing due to inaccurate demand projections can lead to substantial cost increases (32% increase in average LCOE for Tier 3 if demand is actually Tier 2). Substantial within-country variations are observed. Four different types of cost curves are identified at the country level: flat-then-increasing, constantly increasing, increasing-then-flat, and stepwise curves, each with policy implications. Countries with large populations to be electrified (DRC, Ethiopia, Nigeria, Tanzania, and Uganda) generally exhibit lower-than-average costs.

The findings demonstrate that achieving SDG7 in SSA is feasible at reasonable cost if policymakers leverage low-cost off-grid technologies effectively. The study emphasizes the importance of considering the ability to pay, acknowledging substantial between and within country cost variations. The results highlight the importance of spatially disaggregated electrification planning. The cost curves offer a useful tool for tailoring electrification strategies to country-specific contexts and optimizing both LCOE and affordability. The sensitivity of the results to demand assumptions underscores the importance of accurate demand forecasting to avoid cost overruns. The study's limitations are acknowledged, including assumptions about household demand profiles, the role of private sector companies in off-grid deployment, and the assumed public financing of grid expansion. The authors suggest policy implications tailored to country-specific electrification cost curve shapes, such as improved utility efficiency, regionally differentiated support schemes for off-grid companies, and demand subsidies for high-cost areas.

This study provides a comprehensive and granular assessment of the cost of electrifying all households in 40 SSA countries by 2030. The results show that achieving SDG7 is feasible at a relatively low cost with a strategic approach that accounts for diverse country contexts. The study emphasizes the critical role of off-grid solutions in reducing costs, the importance of accurate demand projections, and the need for country-specific policies tailored to cost curves and local affordability. Future research could focus on refining demand projections, integrating different types of productive use into macro-oriented models, and developing region-specific cost of capital estimations. Policies that improve institutional quality, political stability, and the rule of law are critical in driving down the overall cost of electrification in the region.

The study acknowledges several limitations. Firstly, the model relies on specific household demand profiles which may not accurately reflect actual consumption patterns. Secondly, while the model estimates the cost of off-grid solutions from private companies, it doesn't factor in market dynamics and potential additional margins added by these companies. Thirdly, the assumption of public financing for grid expansion may not reflect real-world scenarios, where governments often face financial constraints and challenges in executing grid extension plans effectively. This could affect cost of capital estimations and the overall cost calculation. The study also notes that it does not explicitly consider topographic variations which could impact electrification choices, and that household-level load profiles and individual preferences are not accounted for.