Context matters and electrification models should reflect it

This blogpost discusses a key shortcoming in electrification models used by policymakers and international organizations to chart electricity access pathways in sub-Saharan Africa –  unrealistic financing cost assumptions. We highlight why this often overlooked flaw in electrification models could slacken efforts to reach universal electricity access by 2030 and explain why it matters.

Electrification models are shaping electricity access policy in sub-Saharan African countries

Electrification models have become a key tool in shaping electricity access policy in many sub-Saharan African countries. Many governments including Rwanda, Nigeria, Ethiopia and Kenya are using these models to inform electrification plans. These plans inform key policy decisions, such as the extent to which privately financed off-grid electrification technologies – Mini-grids (MG) and Standalone Systems (SAS¹) – will contribute to electricity access targets. Behind everything is the intent to support reaching the UN SDG #7 on universal access to modern, reliable and affordable energy services by 2030 in light of the current electricity access gap where ca. 600 million people still lack access to electricity on the continent.

The models determine a cost efficient and feasible electrification approach: grid extension, MG and SAS for electrifying a particular region. These models are very sophisticated on technical assumptions, but they often make simplified assumptions on financing costs – a key metric for determining the most cost-effective technology. They typically assume a uniform financing cost of ca. 8% across countries and electrification approach. This shortcoming can have far reaching effects on electrification outcomes and the consequent electrification policy decisions (as you will see in this blog), especially because technologies powered by solar and batteries are capital intensive. This makes their Levelized Cost of Electricity (LCOE) sensitive to the financing costs. In the worst case it could slow down efforts to electrify most of the continent in the remaining 8 years we have to reach universal energy access targets.

Country and technology specific risks drastically influence electrification modelling outcomes

In our recent publication, we estimated realistic financing costs for different electrification approaches – grid extension, MG, and SAS –  and across different countries in sub-Saharan Africa. Our analysis found that depending on the country, the financing cost could range  between 3% – 19% for grid extension, 16% – 32% for MG and  10% – 26% for SAS, a much broader range than the previously assumed 8% uniform financing cost. The financing costs reflect the country and technology specific risks. Grid extension is typically financed by the state, while MG and SAS are typically privately financed. Further, MG are infrastructure based systems with long payback periods (ca. 8 – 15 years), as opposed to SAS (typically Solar Home Systems) which are more product-like and have shorter payback periods (ca 3 – 4 years).  As such the financing cost would be higher for MG in contrast with SASs depending on the financiers, countries (context) and electrification approach. Therefore, the Levelized Cost of Electricity (LCOE) would differ.

In order to understand the importance of accounting for these factors, take a look at the results²  from two financing scenarios in Figure 1. We calculated optimal electrification approaches with currently common financing assumptions and with realistic assumptions. We see from Figure 1, that realistically, MG are cost-optimal in far fewer locations. Electrification planning may thus plan for too many MGs if current models are used, potentially jeopardizing SDG7. If current modelling advice were implemented, it would result in a 20% increase in the LCOE casting further doubt on the affordability of the provided electricity in a region where most unelectrified customers come from the lower income bracket earning less than USD 2 per day.