Global energy systems have started a transition from polluting fossil-based energy sources to low-carbon alternatives. At the same time, there are ~700 million people without access to electricity and 2.3 billion people without clean cooking facilities. During the energy transition, policymakers overseeing energy systems in low to middle-income countries typically have three main priorities:
Providing universal access to energy. Electricity access rates in many low-income countries remain low, so the provision of electricity services will have to expand so that universal access to electricity can be provided.
Transitioning to low carbon generation. Most countries have committed to transitioning towards a low-carbon economy. This will have significant implications for the energy sector, requiring a rapid shift towards cleaner generation sources.
Charging affordable tariffs. Many households have limited financial resources, and affordability can be a major constraint on demand for energy.
Kuungana has developed a least-cost planning model, which can be used to inform sector planning decisions. Least cost modelling, tailored to the context of a given country or region, can help governments and regulators to understand the options available in balancing these often competing priorities. In this blog post, Kuungana’s planning model is applied to a low-income country in Sub-Saharan Africa to illustrate some of the challenges that the model can help policymakers to tackle.
Five scenarios were modelled until 2040 that explore the impact of technology and emissions reduction policies. These scenarios are simplified in that they do not explicitly measure required grid upgrades and assume that the grid will be expanded to accommodate the increase in demand and generation. The five scenarios were:
No cost reductions: Capital costs of generation and storage remain as they are today, and no emissions reduction policies are applied.
RE cost reductions: Solar and wind prices fall in line with expectations over the modelled scenario. The capital cost assumptions are informed by the IEAs Announced Pledges scenario (link).
RE and storage cost reductions: RE and battery prices fall in line with the IEA’s Announced Pledges scenario.
Cost reductions + 100 $/tCO2 carbon cost: RE and battery prices fall, and proactive emissions reduction policies are applied. These emission reduction policies could be implemented via a carbon tax or other measures such as government procurement of low carbon power. The policy ambition is modelled by applying a social cost of carbon that increases from 0 $/tCO2 today to 100 $/tCO2 in 2040.
Cost reductions + 200 $/tCO2 carbon cost: Similar to the previous scenario but the social cost of carbon applied increases from 0 $/tCO2 today to 200 $/tCO2 in 2040.
The below figure shows the cumulative capacity addition to 2040 and emissions intensity of the grid in 2040 under the different scenarios. The key takeaways from the figure are:
In future, most capacity additions should come from renewable technologies. This is the case even without any further cost reduction on renewable generation capacity; this conclusion is accentuated if costs fall further.
Policy interventions are required to achieve the largest emissions reductions. Cost reduction alone will result in emission reductions, but policy leavers such as carbon prices or renewable energy auctions will be necessary to unlock the most significant emissions reductions.
Storage has a key role to play in the bulk shifting of electricity, but costs need to fall for this role to become significant. Emission reductions are possible without storage, but more flexibility (such as that provided by storage) is required to increase these emissions reductions further. Battery storage projects may also play a bigger role if other value streams are recognised (e.g., providing frequency response and other ancillary services).
Without seasonal energy storage capability, some gas-fired generation is still required in the medium term. Overbuilding renewable generation capacity can reduce the amount this gas-fired capacity is used, lowering emissions. Hydrogen-based generation or fitting carbon capture and storage to the gas-fired capacity provide alternatives to unabated gas but will likely remain expensive in the short to medium term. The simplified scenario presented here does not consider these alternatives.
Limited battery storage capacity is built in the short term, but as costs fall the rate of battery storage capacity build accelerates, and the duration of the storage built increases. The below figure shows the different duration of storage installed in the “Cost reductions + 100 $/tCO2 carbon cost” scenario. Until 2031, only 300 MW of battery storage capacity is built. After 2032, the bult out of storage capacity accelerates, including several 6 hr battery projects. The model has the option to build longer-duration storage units (e.g., 8h, 12hr, 16hr, 32hr), but chooses not to do so.
Applying a higher carbon tax results in more renewable energy capacity being added but does not result in more battery storage being installed; instead, excess RE generation is curtailed. The below figure shows the amount of renewable generation curtailed by the model in the various scenarios. The model suggests it is sometimes more cost effective to displace thermal generation with additional renewable generation (even with large increases in curtailment) than to use additional BESS capacity. This finding is sensitive to the relative cost reduction in renewable energy technologies vs. batteries vs. other longer duration storage or flex options that might emerge. Furthermore, this model is optimising a single country in isolation, and interconnection may allow the excess generation to be exported.
The trade-offs illustrated by the discussion above highlight the importance of careful analysis to inform power sector plans. This is especially so at a time of dramatic change, with the policy imperative of reducing emissions and the emergence of new technologies to deliver those emissions reductions. Kuungana’s modelling tools can help to inform policy discussions regarding these trade-offs and can be used to explore the impact of alternative technology and cost pathways on planning.
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