Context matters and electrification models should reflect itOn 18.10.2022 by Churchill Omondi Agutu, Florian Manuel Egli
By Churchill Agutu and Florian Egli
Churchill Agutu is a PhD candidate carrying out research with the Energy and Technology Policy Group in collaboration with the Kigali Collaborative Research Centre (KCRC) under ETH Zurich’s ETH for Development (ETH4D) Scholarship. His research focuses on energy policy and finance and its influence on electricity access in sub-Saharan Africa.
Florian Egli is a Senior Researcher and a Lecturer at ETH Zurich. His research focuses on climate and renewable energy finance and the political economy of transitions with a particular emphasis on the role of public policy. He is also affiliated with the Institute for Innovation and Public Purpose at the University College London (UCL) and he is a member of the executive board of the Swiss Young Academy.
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.
Figure 1: Electrification outcomes showing share of new connections in 2030 for different electrification modes (Agutu et al. 2022).
The effect of this shortcoming is already evident in policy implementation
While the results above look at an electrified continent in 2030, some of the effects of unrealistic modelling are evident in electricity access policy implementation today. Private sector MG have very high costs of capital and are subject to the risks faced by infrastructure based investments including political risk, subjection to regulation and dependence on the institutional quality at a national/subnational level. These risks have slackened their ability to scale up. In Rwanda, the National Electrification Plan 2019 was promulgated as a policy to designate for off-grid electrification zones; the initial plan allocated 10% of the total to-be-electrified population in 2024 to be electrified through MG. The plan was developed based on a GIS electrification model that applied a uniform financing cost of 8% across all electrification approaches (our analysis estimated the financing cost for MG to be 22%). A look into the number of MG deployed between 2019 – 2022 shows that there have been very few connections. This can be attributed to financial and regulatory bottlenecks. Further, the plan was updated in 2022, with the new plan allocating, only about a tenth of the original target to MGs. As such in this particular context, private sector financed MG will likely contribute to a miniscule share of the total electrification shares, a key outcome that could likely have been teased out if realistic financing costs were reflected in the initial electrification model.
Realistic financing costs can avoid advice for ineffective policies
In addition, one key finding of our analysis is the high financing cost for privately financed MG, ranging between 16% – 32% depending on the country. It is important to note that if private sector mini-grids are to play a role in electrifying the continent, significant efforts need to be directed towards reducing financing costs i.e. derisking through policy. For instance governments can clearly designate MG electrification zones for a defined period and clearly stipulate expected compensatory measures in the event that the grid arrives an already MG electrified region. This can foster investor confidence against the risk of stranded assets in the event that the grid arrives, especially considering that grid extension remains largely subsidized in many countries in sub-Saharan Africa.
Policymakers and international organizations can be the champions of change
For policymakers and international organizations, reflecting realistic financing cost assumptions could provide effectual country and technology specific insights such as on the extent to which private sector MG should be prioritized in the country’s electrification pathway. These simple and informed approaches to electrification modelling could circumvent the development of ineffective policies and possibly minimize wasted capital. International organizations such as Sustainable Energy for All, the World Bank, the World Resources Institute and the International Renewable Energy Agency (IRENA) can be champions for change by encouraging member countries, energy modellers and policy actors to incorporate realistic financing costs into their electrification models.
1. Standalone systems here refer to both Solar Home Systems (link: https://energypedia.info/wiki/Solar_Home_Systems_(SHS)), as well as Standalone systems which power productive uses e.g. irrigation and food processing e.g. milling.
2. The results presented reflect one of two electrification pathways – the existing grid pathway- where electrification is carried out through grid extension in already electrified areas, while electrification of fully unelectrified areas is done using off-grid options . See Agutu et al. for further details (https://www.nature.com/articles/s41560-022-01041-6)
Cover picture is provided by James Wiseman on Unsplash.
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Suggested citation: Agutu, Churchill and Egli, Florian. “Context matters and electrification models should reflect it”, Energy Blog @ ETH Zurich, ETH Zurich, October 18, 2022, https://blogs.ethz.ch/energy/sub-saharan-electrification/
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