The cost of electrifying all households in 40 Sub-Saharan African countries by 2030

The study addresses the cost and affordability of achieving universal electricity access (SDG7) in Sub-Saharan Africa by 2030. Despite steady progress, 581 million people in SSA lacked electricity in 2020, with setbacks from COVID-19 and constrained government budgets. Electricity demand is projected to grow substantially, and the emissions intensity of electrification pathways has broader climate and development implications. Existing analyses often evaluate levelized costs of electricity (LCOE) at aggregate levels, offering limited insight into geographically granular consumer costs and affordability. Least-cost technology choices are highly context dependent, and without understanding local ability to pay, policymakers risk expanding infrastructure that is unaffordable to users, creating an affordability gap. The authors aim to provide spatially explicit, least-cost electrification pathways and associated costs per person per day across 40 SSA countries, to inform tailored, feasible planning toward SDG7.

Prior work highlights that least-cost electrification choices vary with local context, institutional quality, and risk, and that ability and willingness to pay are critical for sustainable access. Research documents sharp cost differences across access tiers in the World Bank’s Multi-Tier Framework, and that off-grid technologies (notably solar PV with batteries) have experienced major cost declines alongside business model innovations such as pay-as-you-go. Geospatial models like OnSSET are widely used for planning by researchers and international organizations, but many studies emphasize LCOE and technology choice rather than consumer affordability and subnational heterogeneity. The literature also notes challenges in fee collection for off-grid systems, the importance of financing conditions, and the potential for productive use of electricity to improve utilization and reduce costs per kWh.

The authors model 100% electrification by 2030 for 40 Sub-Saharan African countries using a modified version of the open-source geospatial model OnSSET v1.0. The analysis selects the least-cost approach among: (1) Grid extension (GE), including densification in partially electrified clusters and extension up to 50 km from existing or planned grids (per World Bank datasets); (2) Mini-grids (MG), including solar PV+battery and hydro; (3) Standalone systems (SAS), solar PV+battery sized by local irradiation and household size. Diesel options are omitted as new private off-grid deployments are predominantly renewable. Key inputs and assumptions: - Spatial resolution: population raster 100 m × 100 m; clusters formed from cells; includes medium-rate population growth to 2030. - Demand: fixed per tier (WB Multi-Tier Framework) and country; tiers modeled are Tier 2, Tier 3, Tier 4. - Financing conditions: Implement realistic, approach-specific cost of capital reflecting institutional risk (“niche” scenario). GE is assumed publicly financed (lowest cost of capital); SAS financed by private sector with better access to debt than MG (SAS ~50% debt vs. MG ~0%), making MG highest cost of capital. - LCOE calculation: For GE, LCOE combines generation plus transmission and distribution costs. For MG and SAS, LCOE includes generation, distribution (for MG), O&M, and salvage value. Standard discounted cash flow formulation is used. - CAPEX projections: For MG and SAS, use historical component costs (IRENA) and apply conservative cost reduction factors (UNDP). GE generation costs assumed roughly constant near term. - Model modifications: Improved SAS sizing to allocate capacity only to 2030 new connections rather than all cluster population. Cost curve construction: - Clusters are ordered by LCOE to assemble electrification cost curves at SSA and country levels (ventiles/deciles of population to be electrified). Weighted LCOE and cost per person per day (pp/d) are computed using energy consumption and new connections. Scenario comparisons and sensitivities: - Analyze three demand tiers (2–4) and mix of GE, MG, SAS across cost curves. - Sensitivity analysis on key inputs (CAPEX, cost of capital for SAS, MG, and grid/T&D) showing influence on average LCOE, with higher sensitivities in high-cost (remote) segments. - Demand shortfall assessments: (Error 1) misguided planning—technology chosen for higher tier but realized demand is lower; (Error 2) oversizing—systems deployed at Tier 3 but actual demand is 50% of projected, estimating LCOE penalties for MG and SAS. Geographical scope: - 40 SSA countries; some countries excluded due to incomplete inputs (e.g., Cape Verde, Comoros, Djibouti, Mali, Mauritius, Ivory Coast, Seychelles, Sao Tome & Principe, Sierra Leone). Somalia and Somaliland not split due to data limitations.

- Off-grid options, particularly solar standalone systems (SAS), substantially reduce the cost of electrifying remote and high-cost areas; SAS act as a cost-leveller across rural, low-density, high-risk contexts. - Average costs: Least-cost Tier 3 electrification across SSA can be provided at about 0.14 USD/kWh or 0.07 USD per person per day (pp/d). For Tier 2 and Tier 4, average costs are approximately 0.20 USD/kWh (0.03 USD pp/d) and 0.11 USD/kWh (0.15 USD pp/d), respectively (Supplementary Table S7). - Investment needs by tier: Tier 2: USD 87 bn; Tier 3: USD 203 bn; Tier 4: USD 408 bn. - SAS availability reduces total Tier 3 investment by USD 46 bn (≈18%) compared to scenarios without SAS (e.g., grid+MG), and keeps LCOE < 0.50 USD/kWh for the entire unelectrified population when all three options are available. - Maximum consumer costs: Up to USD 0.05 pp/d (Tier 2), USD 0.16 pp/d (Tier 3), USD 0.40 pp/d (Tier 4). - Technology mix by tier and cost curve position: For the first ~30% of the unelectrified population (urban/peri-urban), grid densification dominates with similar LCOEs across tiers. Grid extension remains least-cost to roughly the 50th percentile. SAS becomes dominant beyond the median for Tier 2 and after ~75th percentile for Tier 3; MGs play a notable role primarily in high-demand Tier 4 in the last decile (MG+SAS ~90%). - Demand risk impacts: If planned for Tier 3 but actual demand is Tier 2, average LCOE increases by 32% (and by 86% if planned Tier 4 but realized Tier 2). Oversizing at 50% of projected Tier 3 demand raises LCOE by 128% for MG and 121% for SAS (representative clusters). - Country variation (Tier 3, pp/d): A five-fold range from USD 0.03 pp/d (Gabon) to USD 0.16 pp/d (Eswatini). Eight countries exceed USD 0.10 pp/d (Burkina Faso, Central African Republic, Chad, Madagascar, Mozambique, Republic of Congo, Eswatini, Zimbabwe). The largest populations to be electrified are concentrated in DRC, Ethiopia, Nigeria, Tanzania, and Uganda (≈475 million combined), with average costs 5–7c pp/d (annual USD 18–26 per person). - Institutional quality correlation: Strong positive correlation between average pp/d cost and public cost of debt (proxy for institutional quality). Notable exceptions include Sudan (lower costs despite high cost of debt due to large households, favorable irradiation, and high SAS share) and Eswatini/Madagascar (higher costs due to small households and grid-heavy shares). - Within-country heterogeneity: Four archetypal country cost-curve shapes identified (flat→increasing; constant increasing; increasing→flat; flat→increasing→flat), with policy implications for targeted support and tier selection. - Uncertainty grows toward high-cost, remote segments; results are robust overall with modest shifts in curves under input sensitivities.

The analysis demonstrates that integrating affordability metrics (cost per person per day) and spatial heterogeneity into least-cost electrification planning offers a more actionable path to achieving SDG7. Findings show that low-cost off-grid technologies, especially SAS, significantly lower consumer-level costs in remote, institutionally challenging contexts, thereby improving feasibility of universal access. Countries with the largest unelectrified populations generally have lower-than-average pp/d costs, indicating strong opportunities for cost-effective scale-up. However, affordability challenges persist—particularly at higher tiers and for the last mile—where costs and uncertainties rise. Demand realization is pivotal: overestimation can lead to misguided technology choices and oversizing, driving large LCOE increases. Cost curves by tier and geography enable planners to tailor strategies: prioritize grid densification/extension in dense, near-grid areas; deploy SAS broadly in later segments; and reserve MGs for high-demand clusters where shared infrastructure offsets financing costs. The strong correlation between costs and institutional risk underscores the role of policies to improve governance and reduce the cost of capital. Overall, the study suggests that using electrification cost curves and demand-tier strategies can align technology choices with local ability to pay and reduce the need for unsustainably high subsidies.

The paper contributes a spatially explicit, consumer-cost-focused assessment of least-cost electrification pathways for 40 SSA countries, introducing electrification cost curves (LCOE and pp/d) as planning tools. It quantifies how off-grid options—particularly SAS—reduce costs and investment needs, highlights substantial between- and within-country heterogeneity, and shows sensitivity to demand assumptions. On average, Tier 3 access can be achieved at about 0.14 USD/kWh or 0.07 USD pp/d, with total investment needs around USD 203 bn. Policy implications include: swiftly executing grid densification/extension where least-cost; enabling private off-grid deployment at scale; tailoring support schemes by region and tier; and considering lower tiers where affordability is constrained. Future research directions include region-specific cost-of-capital estimation, integrating productive-use demand into geospatial models, improving demand forecasting to mitigate oversizing, and assessing operational cost changes as electrification extends to more remote, lower-income areas.

- Demand profiles are assumed per tier and country; deviations (higher or lower realized demand) will change pp/d costs and can cause suboptimal technology selection or oversizing. - Market factors and regulation may lead private off-grid providers to charge margins above modeled costs; real tariffs may differ from LCOE-based estimates. - The model assumes governments implement planned grid buildout with public financing and relatively low cost of capital; in practice, many utilities face debt burdens, poor cost recovery, and implementation challenges, so reported costs represent potential rather than forecasts. - MG costing assumes a representative 100 kW system without detailed battery sizing, likely optimistic; despite this, MG shares remain modest in results. - Diesel technologies are excluded; while consistent with recent renewable off-grid trends, this omission may affect options in certain contexts. - Uncertainties increase for high-cost, remote segments; sensitivity analyses show robustness overall but higher variance toward the right tail of cost curves. - Geospatial inputs omit certain local nuances (household-level load profiles, incomes/preferences) and topography effects; some countries are excluded due to missing inputs (and Somalia/Somaliland not split).