On the role of interconnected mini-grids in net-zero emissions electricity system: insights from Nigeria

Nigeria's baseline future electricity demand was recently evaluated in a comprehensive bottom-up study—NREmap—by the International Renewable Energy Agency (IRENA) in collaboration with the Energy Commission of Nigeria. The study showed that Nigeria's electricity demand could increase from 113 TWh in 2020–520 TWh by 2050 (IRENA 2023). The study projected electricity demand across various demand sectors, including the residential, industry, commercial, transport, and agriculture sectors. A linear extrapolation of the IRENA's electricity demand projection has been done to 2070. Thus, future electricity demand is expected to increase to 876 TWh by 2070 at a compound annual growth rate (CAGR) of 4.2%. Figure 3 shows the projected baseline sectoral electricity demand for the modeling period (2020–2070).

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IRENA's electricity demand projection shows that by 2050, Nigeria's per capita electricity demand could reach 1296 kWh (IRENA 2023). This value is less than half of the current global average of 3152 kWh and is even more insignificant when compared to OECD and US current consumption levels (7992 kWh and 12 573 kWh, respectively) (Blimpo et al 2023). However, this electricity consumption level of Nigeria largely undermines Nigeria's developmental needs and the central role of electricity as a precursor for socioeconomic development in the modern era. For this reason, the Energy for Growth Hub proposed a modern energy minimum (MEM) of 1000 kWh per capita as a minimum threshold to spur competitive economic growth (Moss et al 2021).

To reflect Nigeria's development aspirations for industrialization, employment, higher incomes, prosperity, and economic transformation, we developed a second electricity demand forecast that envisages ambitious growth driven by relatively higher per capita electricity consumption. Per capita, electricity consumption generally grows as a function of per capita income (Wiedenhofer et al 2013, Wu et al 2019). To estimate the future electricity demand under this ambitious demand (AD) case, we apply similar understandings and assume that there is a relationship between the distribution of income and electricity consumption for a given country (Kemp-Benedict et al 2013, Fouquet 2014, Oxfam 2015, Csereklyei 2020). Given the paucity of national-level income data, we used per capita GDP as a proxy for per capita income (Diacon and Maha 2015, Dioha and Kumar 2020a). Nigeria's GDP data was obtained from the World Bank. The Nigerian population data in this study was obtained from the United Nations' world population prospects (United Nations 2019). The percentile-level national income distribution data were obtained from OXFAM's study of global carbon inequality (Oxfam 2015).

Using the OXFAM's national income percentiles and methods (Oxfam 2015), we calculate the GDP of each percentile. Using the GDP of each percentile, we calculate the fractional share of each percentile's contribution to the national GDP. Next, we calculate the cumulative population of each percentile and their corresponding per capita GDP. To convert the national income distribution data into electricity consumption distributions, we calculate the index value of each percentile distribution (equation (2)). To allow an elastic relationship to exist between electricity consumption and income, we assigned Nigeria income elasticity of electricity demand of 0.513 (Babatunde and Shuaibu 2011), which is then used to determine an elastic income distribution index (equation (3)). The elastic income distribution index is then converted into a synthetic distribution of electricity consumption as per equation (4),

$$\begin{equation}i{P_y} = \left[ {\frac{{\left( {GDG/cap} \right)\,of\,Px}}{{\left( {GDP/cap} \right)\,of\,P1}}} \right] \times 100\,\end{equation} \tag{ 2 }$$

where $i{P_y}$ is the index value for percentile $y$

$$\begin{equation}E{P_y} = e \times 100 \times \left[ {\frac{{i{P_y}}}{{100}}} \right]\end{equation} \tag{ 3 }$$

where $E{P_y}$ is the elastic distribution index value for percentile $y$, and $e$ is the income elasticity of electricity demand,

$$\begin{equation}EUD = \left( {TES/cap} \right) \times \left[ {\frac{{EPy}}{{EPy\mu }}} \right]\end{equation} \tag{ 4 }$$

where $EUD$ is the per capita electricity consumption distribution, $TES/cap$ the per capita electricity consumption by each percentile's elastic index, and $EPy$ the average value of the elastic distribution index.

From equation (4), we derive the distribution of electricity across each national percentile and the corresponding per capita electricity demand. From this electricity consumption distribution, we calculate the total number of people living below the MEM and then quantify the implications of the additional energy needed to lift them to the MEM's value (1000 kWh) by 2030 and the median for high-income countries (6720 kWh) by 2050 (Moss et al 2021). The projected electricity demand is split across sectors using IRENA's study's percentage share across each sector (IRENA 2023). Figure 3 shows the projected ambitious sectoral electricity demand for the modeling period (2020–2070).

In figure 4, the total capacity requirements for all four-BD scenarios are presented, showcasing the contrasting outcomes when considering the interconnection or not of mini-grids. As electricity demand is projected to rise sharply in the modeled years, this will necessitate a substantial increase in both installed capacity and electricity generation, far exceeding current levels. To meet this demand, Nigeria's electricity system is projected to undergo a significant expansion: from an initial capacity of 35 GW in 2020 to an estimated 203 GW by 2050 in the BD2050 scenario. In the BD2070 scenario, the required capacity is projected to reach 157 GW by 2050. Further, by the year 2070, the total installed capacity is expected to increase to 268 GW in the BD2050 scenario and to 282 GW in the BD2070 scenario.

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To align with the net-zero goal by 2050, Nigeria needs to increase electricity generation by 4.7 GW annually, a figure that rises slightly to 4.9 GW for a 2070 target. The BD2050 scenario requires a more rapid build out due to the higher deployment of solar PV with low availability factors, necessitating greater capacity and faster battery storage deployment.

The inclusion of IMGs yields higher cumulative capacity expansions, reaching 308 GW and 326 GW by 2070 in the BD2050-IMG and BD2070-IMG scenarios, respectively. This capacity differential relative to the scenarios without IMGs is predominantly attributable to substantially larger additions of battery storage and utility-scale solar PV. The rise in capacity investment in all scenarios relative to the base year presents notable implications in a context like Nigeria where constraints on capacity or capital investments are prevalent. The additional capacities required imply higher initial capital outlay and increased complexity in managing a more diverse energy system, challenging Nigeria's financial and infrastructural capabilities. Strategic planning and support mechanisms may be required to manage these increased investments without hampering the overall goal of decarbonization.

The model projects a near doubling of annual battery storage and utility-scale solar PV additions in scenarios with IMGs, significantly reducing curtailment and enhancing the efficiency of renewable energy utilization. Furthermore, the oil (diesel and petrol) backup generator phaseout is expected to be completed by 2040 in all scenarios, with a faster retirement rate in scenarios with IMGs. Similar insights were derived from Sachiko Graber et al (2020), and Green Energy Investment (2020), who opined that IMG customers could benefit from better, more reliable, cost-effective electricity service.

In all BD scenarios, a rise in thermal power generation, particularly from combined cycle gas turbines (CCGTs), is expected, peaking at 13.8 GW around 2030–2035. Meeting the net-zero target by 2050 would require the early decommissioning of CCGT and biomass power plants. To offset this, the model suggests deploying 5.2 GW of CCGT with carbon capture and storage (CCS) in the BD2050 scenario. However, in the BD2050-IMG scenario, which integrates solar PV and battery storage, the need for CCGT + CCS is significantly reduced to only 0.3 GW. This is due to the flexibility and reliability provided by IMGs with solar PV and battery storage. As the goal shifts to achieving net-zero by 2070 in the BD2070 and BD2070-IMG scenarios, renewable energy options, thanks to decreasing costs, become more feasible, reducing the dependence on CCS technology. The model also finds feasibility to deploy at least 10 GW of nuclear capacity by mid-century in all four BD scenarios. This capacity grows rapidly, with an average of 0.5 GW per year, resulting in a minimum installed capacity of 24 GW by 2070 in the BD2070-IMG scenario. Nigeria has the ambition to embrace nuclear energy through the addition of 4 GW of nuclear capacity in Geragu, Kogi State (Bolodeoku 2021, Ewim et al 2022). Despite its strong supportive role in decarbonizing Nigeria's electricity system, the successful implementation and contributions of nuclear technology heavily rely on the level of domestic and international policy support (Dioha 2020, Dioha and Kumar 2020b).

To meet the Nigeria's electricity demand in the BD scenarios, total electricity generation is projected to increase eightfold from 121 TWh in 2020 to around 956–961 TWh in 2070 in the BD2050 and BD2070 scenarios (figure 5). The BD2050 scenario shows a quicker rise in electricity generation until 2050, after which a decrease is observed. By 2050, non-fossil sources like wind, solar PV, hydro, and nuclear will constitute about 87% of total electricity generation in the BD2050 scenario, compared to 78% in the BD2070 scenario. Fossil fuels, which accounted for 76% of the energy mix in 2020, are phased out by 2050 in the BD2050 scenario, while they still hold an 11% share in the BD2070 scenario. In both IMG scenarios, the share of non-fossil energy is pushed to 92% by 2050, driven by the accelerated deployment of utility-scale solar.

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Figure 6 shows annual CO₂ emissions for various scenarios, with electricity sector emissions peaking at 154 million tCO₂ between 2020–2030, then declining. The net-zero by 2050 scenarios, BD2050 and BD2050-IMG, see significant CCGT + CCS deployment after 2050 for CO₂ removal, with lower CCS use in BD2050-IMG due to mini-grids replacing CCS needs. In the 2070 scenarios, emissions consistently decrease at a 6% CAGR. Figure 7 outlines fuel requirements for natural gas, oil, biomass, and nuclear. Natural gas use decreases over time, peaking at 746 PJ between 2030–2040, then dropping as cleaner technologies are adopted. Even with CCGT + CCS, the BD2050 scenario would use over 250 PJ of natural gas by 2070, suggesting potential continued use of Nigeria's gas reserves with effective CCS implementation. Oil usage sharply declines in all scenarios, peaking at 1860–1866 PJ in 2025. Biomass use increases, peaking at 232 PJ in 2050 in the BD2070 and BD2070-IMG scenarios, but all biomass capacity retires by 2070 across all scenarios.

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Table 6 presents the total discounted cost of the electricity system for the four BD scenarios. In general, achieving net zero will require very large investments, underlining the need for low-cost, long-term international climate finance, in the absence of which these investments would have to come at the cost of other sectors and public services, therefore, obstructing much-needed development. The total discounted system cost for both BD2050-IMG and BD2070-IMG scenarios are 510 and 509 billion USD, respectively, both ∼1% lower than their non-IMG counterparts. The total discounted cost for both net zero scenarios by 2050 (BD2050 and BD2050-IMG) experiences a minimal increment of only 1.1% in comparison to their 2070 counterparts (BD2070 and BD2070-IMG). As mentioned in section 3.1.1, this cost differential can be attributed to the faster deployment of new capacity of clean generating assets and early retirement of fossil assets in the BD2050 and BD2050-IMG scenarios. Similar cost differentials were found by (Panos et al 2023) who showed that postponing mitigation measures by a decade could lead to mitigation expenses for achieving a 2 °C target that closely resemble the costs needed to attain the 1.5 °C goal if initiated now.

It is important to note that the choice of a discount rate is pivotal in such long-term investment analyses. The 10% rate used in our study is indicative of the current economic climate and risk profile in Nigeria. A lower discount rate, which might be justifiable should Nigeria's economic prospects improve, would significantly alter these findings. Specifically, it would increase the present value of future investment costs, making upfront investments in clean generating assets and IMGs appear more economically attractive.

To meet the median per capita electricity consumption of high-income countries by 2050, Nigeria requires a dramatic expansion in capacity, adding 27–32 GW annually—nearly six times higher than BD scenarios (figure 8). By 2070, under the AD2050 and AD2070 scenarios, total installed capacity would reach 1392 GW and 1538 GW, respectively. In these scenarios, IMG capacities are 8% higher than their counterparts, mainly due to substantial solar PV installations.

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Petrol and diesel generators are phased out earlier in all AD scenarios, consistent with the BD scenarios. For scenarios targeting net-zero emissions by 2050, CCGT capacity peaks at around 44 GW by 2035, contrasting with a delayed peak of about 107 GW for 2070 targets. In both AD2050 and AD2050-IMG scenarios, CCGT and biomass generation assets are abandoned by 2050.

In all four AD scenarios, a rapid deployment of CCGT equipped with CCS begins in 2050, in line with the higher renewable energy mix. The need for CCS diminishes when extending the net-zero target to 2070. As with the BD scenarios, IMG scenarios further reduce CCS deployment due to the economic advantages of solar power and battery storage.

Renewable energy technologies, particularly utility-scale solar PV, solar PV mini-grids, and battery storage, are pivotal in attaining the ambitious energy consumption target. Notably, utility-scale solar PV and battery storage demand expedited and substantial annual deployments of 9–10 GW and 4–7 GW, respectively. Similar to the BD scenario clusters, the model consistently prioritizes the accelerated deployment of battery storage in both IMG scenarios (AD2050-IMG and AD2070-IMG). This emphasis is particularly notable during the period from 2020 to 2050, with an average annual growth rate of approximately 7 GW of new battery storage capacity, a rate nearly double that observed in AD scenarios without IMGs. Investments in other non-fossil sources like CSP and nuclear are also significant, reaching 70 GW and 80 GW by 2070, respectively.

Nigeria's electricity generation must grow three-fold by 2030 to approximately 317 TWh, and by 2070, a thirty-four-fold increase to over 4000 TWh is necessary (figure 9). The AD scenarios heavily rely on CCGT + CCS from 2050 onwards, balancing ambitious electricity demand with emission constraints.

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In table 6, we show the total discounted cost of the system over the modeling period for the four AD scenarios. Notably, despite the considerable increase in capacity additions in the AD scenarios compared to the BD scenarios, the total discounted cost is only twofold higher, between 1001–1022 billion USD. This outcome can be attributed to the substantial incorporation of cost-effective generation sources such as solar PV. Furthermore, it is important to note that, similar to the BD scenarios, the presence or absence of IMGs does not significantly impact the total investment costs in the AD scenarios.

It is, however, crucial to recognize that these investment costs may not accurately reflect the full range of system requirements and associated costs to achieve the ambitious energy transition goals outlined in the AD scenarios. This would require a more granular spatial and co-optimization modeling approach to account for the spatial distribution of renewable resources, transmission and distribution constraints, and the interplay between generation, storage, and grid infrastructure.