Pathways to 100% Electrification in East Africa by 2030.

AuthorFalchetta, Giacomo

    As of 2019, East Africa--here defined as the macro-region that includes Burundi, Kenya, Malawi, Mozambique, Rwanda, Tanzania, and Uganda--hosts 2.9% of world's population (The World Bank, 2019), but only accounts for 0.14% of global gross electricity consumption (CIA, 2017). While the share of regional population without access to electricity has fallen from 90% in 2000 to 64% in 2017 (IEA, 2018), the absolute number of people without access has instead increased by 8 million as electrification efforts have been outpaced by rapid population growth. 141 million people are estimated to live without access in the region, and high rural-urban inequality prevails in all countries. The regional final electricity consumption stood at 35 TWh in 2016 (CIA, 2017). In the same year a high-income country like Italy consumed 310 TWh of electricity, despite having less than 20% of EA's population. Table 1 reports statistics characterising the power sector and electricity access situation in the EA countries considered.

    EA countries are endowed with substantial untapped energy resources and generation potential (BP, 2017; ENI, 2017; IRENA, 2014), which is technically enough to guarantee energy security and self-sufficiency in the region: solar PV maximal technical generation potential is abundant throughout EA, and overall it stands at 219,500 TWh/year (1). Solar CSP (176,000 TWh/year) is mostly feasible in Kenya. Untapped hydropower, both at large and small scale, is found to varying degrees in all countries (The International Journal on Hydropower and Dams, 2017). The same is true for geothermal, and in particular in the northern part of EA, in the Rift Valley, between Kenya and Malawi. Wind potential stands at 16,600 TWh/year (2). Bioenergy for power generation purposes is a further viable option, mostly in Kenya, Uganda, Mozambique and Tanzania (IRENA, 2014). Hydrocarbon resources are also abundant, but their distribution is highly skewed: only Uganda has substantial oil reserves (2.5 billion barrels), while those of Kenya are less prominent or accessible; natural gas (NG) endowments of Mozambique and Tanzania are large (together they sum to 4,200 bcm), while those of Rwanda are limited and only partially viable (BP, 2017; ENI, 2017). Finally, coal reserves and associated mining activity is taking place in Mozambique (according to estimates more than 20,000 Mt of reserves could exist in the country), and Tanzania (297 Mt) (BP, 2017).

    Bottom-up analysis for electrification exploiting geospatial data has proved a robust methodology to assess cost-efficient strategies to achieve energy access objectives in developing countries. Modelling efforts have culminated in the release and application of a number of tools (among others, OnSSET, GEOSIM, Network Planner; refer to van Ruijven et al., 2012; Columbia University, 2017; Parshall et al., 2009; Ellman, 2015; Mentis et al., 2017; Szabo et al., 2011). However, for the case of EA--a heterogeneous and yet highly interdependent region--a paucity of integrated, region-wide, quantitative studies on electrification and energy development is witnessed. Given the tight interdependencies which already exist and will intensify in the power sector of the region and within the EAPP--it is crucial to elaborate the electrification process as an integrated, transboundary one.

    Here, spatially-explicit least-cost 100% electrification scenarios by 2030 for EA (in compliance with the UN's Sustainable Development Goals' target 7.1.1) are modelled using OnSSET (Open Source Spatial Electrification Tool, Mentis et al., 2017), an open-source model allowing for high-resolution bottom-up assessment of access technologies and investment requirements. The output of the model provides insights on the changes in the level of penetration of different technologies and sources when key variables in the electrification equations are altered, as well as on the capacity additions required and on the total investments necessary to achieve universal access to electricity by year 2030 in each country of the region. Calibrated model input data are hosted in an online repository to allow reproduction of results and substitution of parameters and assumptions, also thanks to the open nature of the tool.

    OnSSET is not an energy system-wide optimisation model, and thus it does not represent the evolution of the grid-based electricity generation mix. Exogenous input parameters for the average LCOE of grid-based electricity in the regional market and for the average investment requirement to add a utility-scale kW of new capacity to the system must be determined. Thus, we design three baseline scenarios (refer to Section 4 and the Appendix) as plausible pathways for the evolution of the regional grid-based power generation mix given current and planned capacity addition and energy resource endowments in the region. Furthermore, beyond new connections, we also quantify capacity additions and corresponding investment requirements to satisfy baseline power demand growth (i.e. increase in the demand from already electrified consumers and sectors other than the residential one). The relative C[O.sub.2] emission pathways are therefrom derived. The analysis is not limited to a modelling exercise, as specific attention is paid to the main policy and financing-related issues faced in the accomplishment of a sustainable and cost-effective full electrification in EA.

    The remainder of the paper is structured as follows. In Section 2 a literature review of electrification planning and challenges in sub-Saharan Africa, including previous spatial least-cost electrification modelling, is presented. Section 3 illustrates the methodology and the datasets used as inputs by the model and it describes its functioning and the key aspects represented. The design of generation mix scenarios and the modeled pathways is detailed in Section 4. Section 5 presents the results of the analysis for both newly electrified consumers and the additional demand for power from already electrified customers, while also highlighting the main results of the sensitivity analysis. The numbers are then put in perspective with socio-economic and finance flows metrics for the region. Section 6 discusses the key policy implications deriving from the analysis for both national governments and international institutions to enable the fulfilment of investment requirements. Section 6 concludes the paper.


    Electrification planning, optimal technology mix choice, and long-term demand forecasting in developing countries present multiple challenges because of data scarcity, including the lack of a current demand to anchor on and uncertainty over its future development (Mandelli et al., 2016; Lombardi et al., 2019). At the same time, electricity access planning can itself be performed with a number of different approaches (Trotter et al., 2017). These include e.g. top-down (Pachauri et al., 2013), bottom-up (Mentis et al., 2017), or system dynamics-based methodologies (Riva et al., 2018c). It also requires accounting--either in the modelling, or in the interpretation of results, or in both--for crucial aspects including: incentives and barriers to private sector participation (Rafique et al., 2019; Williams et al., 2015; Eberhard et al., 2017; Malgas and Eberhard, 2011) and public incentives (Lucas et al., 2017; Kruger and Eberhard, 2018), institutions and accountability (Ahlborg et al., 2015), barriers on the household-side (Golumbeanu and Barnes, 2013), productive uses of energy (Riva et al., 2018a), energy efficiency (Adom, 2019; Diawuo et al., 2018), and synergies with other development goals (McCollum et al., 2018; Bos et al., 2018).

    Different macro-regional electrification projections have been carried out on SSA as a whole or on specific countries. van Ruijven et al. (2012) developed and applied a global model for rural electrification. They showed that the gap between the projected level of access and 100% access by 2030 is largest in sub-Saharan Africa. They estimate that $131-226 billion are necessary to close the gap in the continent. For the specific regional case of EA, they show that--under a low-demand and both high and low-cost scenarios--a combination of SA PV and wind and diesel MG is the most economically feasible in virtually all regions. Conversely, under a scenario of high demand, investment costs have a big impact between the decision of shifting from decentralised solutions to grid connection, wich determine a broad uncertainty range (between 14.5%-61.5% for wind/diesel MG and 11.5% and 47.5% for SA solar-PV).

    Dagnachew et al. (2017) extended the model of van Ruijven et al. (2012) to account for further decentralised electrification solutions, such as mini-hydro. The authors confirm that the optimal mix for providing 100% access to electricity strongly depends on the aimed per-capita yearly consumption tier, with 65% penetration of SA or MG systems at low consumption levels and 95% of on-grid electricity penetration at 3,000 kWh/household/year. For the entire SSA continent, they project a very large range of variation in required investment (between $22 billion and $2,500 billion), again depending on the consumption level to be achieved. Szabo et al. (2011) developed an LCOE-minimisation-based electrification model based on the demand-side, i.e. on household ability-to-pay (ATP). They find that a difference of $0.05/kWh (from $0.25 to $0.30/kWh) in the ATP of rural households would greatly expand the fraction of locations where solar PV can be the key technology in achieving electrification vis-a-vis standalone diesel generators. They thus stress on the significance of diesel price in determining electrification solutions, and therefrom derive policy inputs on incentives and feed-in-tariffs. Bertheau et al. (2017) carried out a similar GIS-based modelling exercise and found a high relevance of...

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