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In Nigeria, 86 million people lack electricity access, the highest number worldwide, predominantly in rural areas. Despite government efforts, constrained budgets necessitate private investors, who, without adequate incentives, are hesitant to commit capital due to perceived high risks. This study identifies three existing incentive policies—concessionary loans, capital subsidy, and financing productive use equipment—aimed at promoting rural electrification in Nigeria. Employing geospatial and regulatory analyses, we evaluate their impact on electrification planning across 22,696 population clusters. While all incentives encourage mini-grids and stand-alone systems, results show varied impacts, predominantly favouring mini-grids. Under the baseline, grid extension is optimal for 66% of clusters, followed by mini-grids (27%) and stand-alone systems (7%). Concessionary loans boost mini-grid and Stand-Alone Systems shares by 10% and 5%, respectively. Capital subsidies increase the mini-grid share to 41%, surpassing concessional loans (37%). Financing productive equipment enhances Stand-Alone Systems and mini-grid shares to 15% and 43%. Incentives impact LCOE, CAPEX, and OPEX, with average LCOE reducing to 0.31 EUR/kWh (concessionary loans), 0.30 EUR/kWh (capital subsidy), and 0.27 EUR/kWh (financing productive use). Financing productive uses proves decisively more effective in lowering costs for mini-grids and stand-alone systems than loans or capital subsidies. The important policy implications of this study reinforce the need for tailored incentives for distinct electrification options.

Access to reliable and sustainable electricity is still precluded for a large share of global population living in rural areas of developing countries, especially in sub-Saharan Africa. Hybrid microgrids are considered a suitable solution for providing affordable and reliable access to electricity to isolated communities. Properly planning and sizing such systems is although an aspect that can greatly influence the sustainability of the intervention, and the arrival to the market of the third generation minigrids poses new challenges to the process. Three main challenges are identified as pivotal for the proper sizing of new generation microgrids: arrival of the main grid, inappropriateness of Net Present Cost as only objective function in the strategy selection process, and necessity to operate on already existing minigrids. Such aspects are addressed in this work by proposing a methodological advancement to an existing open-source microgrid sizing model: a grid outage model alongside the definition of new constraints and variables for the optimization problem with grid-connected microgrids, a multi-objective optimization option, and a brown-field optimization option. The new version of the model is tested on real life case studies in rural Rwanda (greenfield) and Mozambique (brownfield), proving the profitability of grid-connected and grid-extension solutions for sufficiently low connection distances. Sensitivity analyses are performed to assess variations in system size, cost and CO2 emissions with respect to microgrid and grid connection parameters.

Access to affordable, reliable, and modern energy remains a critical goal under the Agenda 2030 for Sustainable Development, especially in remote areas of developing countries. Based on traditional engineering approaches, many energy solution planning tools have been developed to identify the optimal solution in these areas to assess the competition across different technological options. Nevertheless, these approaches, based on an economic optimum, do not necessarily grant long-term sustainability of the solution in specific local contexts, since they are not able to capture the social implications within the Energy-Development nexus. Moreover, also in light of the 2030 Agenda, scientific and grey literature on energy access highlights how energy solutions planning methodologies developed in the last decades need to be complemented by a more comprehensive view, able to integrate evidence from various disciplines, especially engineering and social sciences. Based on the above considerations, this paper introduces a novel framework under the name of CESP, where three social sciences-based phases complement three engineering phases, each one characterized by specific tools, to offer an informed decision framework for the local planner. CESP encompasses a set of techno-economic and socio-technical actions to prevent potential failure as evidenced by a counterfactual analysis used to identify the reasons behind past project failures. The CESP framework presents a sequential and iterative structure that underlines the cyclic perspective of a holistic decision process where social sciences feed the engineering analysis and vice versa. Finally, CESP emerges as a practical and applicable framework for supporting energy access planning in critical areas.

In rural sub-Saharan Africa – the global poverty hotspot – the vast majority of cropland is rainfed only, resulting in reduced and unstable yields. Smallholder farmers account for 80% of agricultural production but they have limited access to relevant services to support both commercial operations and their livelihoods: more than two-thirds of rural dwellers have no access to electricity (crucial for crop irrigation, processing, and storage) and about 40% have no access to clean water. Previous research has analysed integrated technological and resource management approaches to tackle these overlapping development gaps. To finance and implement such transformations in resource-constrained settings, it is now crucial to understand the business and investment implications, also considering the strong regional population growth and the increasing frequency and intensity of climate extremes. Here, we lay out a research agenda that promotes the integration of multi-scale modelling excellence along the climate-water-renewable energy-agriculture-development Nexus and the creation of robust business models for private companies that can sustainably support private smallholder farmers of SSAin their effort to eradicate poverty and inequality. The proposed agenda is a cornerstone of the EC-H2020 project LEAP-RE RE4AFAGRI (“Renewable Energy for African Agriculture: Integrating Modelling Excellence and Robust Business Models”). In proposing the agenda, we highlight the importance of integrating energy access into the Nexus framework from both research and investment perspectives.

In state-of-the-art energy systems modelling, reservoir hydropower is represented as any other thermal power plant: energy production is constrained by the plant's installed capacity and a capacity factor calibrated on the energy produced in previous years. Natural water resource variability across different temporal scales and the subsequent filtering effect of water storage mass balances are not accounted for, leading to biased optimal power dispatch strategies. In this work, we aim at introducing a novelty in the field by advancing the representation of reservoir hydropower generation in energy systems modelling by explicitly including the most relevant hydrological constraints, such as time-dependent water availability, hydraulic head, evaporation losses, and cascade releases. This advanced characterization is implemented in an open-source energy modelling framework. The improved model is then demonstrated on the Zambezi River Basin in the South Africa Power Pool. The basin has an estimated hydropower potential of 20,000 megawatts (MW) of which about 5,000 MW has been already developed. Results show a better alignment of electricity production with observed data, with a reduction of estimated hydropower production up to 35% with respect to the baseline Calliope implementation. These improvements are useful to support hydropower management and planning capacity expansion in countries richly endowed with water resource or that are already strongly relying on hydropower for electricity production.

The coexistence of the need to improve economic conditions and the conscious use of environmental resources plays a central role in today’s sustainable development challenge. In this study, a novel integrated framework to evaluate the impact of new technological interventions is presented and an application to smallholder coffee farms and their supply chains in Kenya is proposed. This methodology is able to combine multiple information through the joint use of three approaches: supply chain analysis, input-output analysis, and energy system modeling. Application to the context of the Kenyan coffee sector enables framework validation: shading management measures, the introduction of eco-pulpers, and the exploitation of coffee waste biomass for power generation were compared within a holistic high-level perspective. The implementation of shading practices, carried out with fruit trees, shows the most relevant effects from the economic point of view, providing farmers with an additional source of income and generating $903 of work for every million of local currency (about $9k) invested in this solution. The same investment would save up to 1.46 M m3 of water per year with the eco-pulpers technology. Investing the same amount in coffee-biomass power plants would displace a small portion of production from heavy-duty oil and avoid importing a portion of fertilizer, saving up to 11 tons of CO2 and around $4k per year. The results suggest the optimal allocation of a $100m budget, which can be affected by adding additional constraints on minimum environmental or social targets in line with sustainable development goals.

Globally about 800 million people live without electricity at home, over two thirds of which are in sub-Saharan Africa. Planning electricity access infrastructure and allocating resources efficiently requires a careful assessment of the diverse energy needs across space, time, and sectors. Because of data scarcity, most country or regional-scale electrification planning studies have however assumed a spatio-temporally homogeneous (top-down) potential electricity demand. Poorly representing the heterogeneity in the potential electricity demand across space, time, and energy sectors can lead to inappropriate energy planning, inaccurate energy system sizing, and misleading cost assessments. Here we introduce M-LED, a Multi-sectoral Latent Electricity Demand geospatial data processing platform to estimate electricity demand in communities that live in energy poverty. The platform shows how big data and bottom-up energy modelling can be leveraged together to represent the potential electricity demand with high spatio-temporal and sectoral granularity. We apply the methodology to Kenya as a country-study and devote specific attention to the implications for water-energy-agriculture-development interlinkages. A more detailed representation of the demand-side in large-scale electrification planning tools bears a potential for improving energy planning and policy.

Energy access is a key need for socio-economic growth. Proven to be a key enabler of development and progress, access to electricity has been prioritized by governments using grid extension actions and off-grid solutions, namely microgrids and home systems technologies, fed by renewable sources. However, achieving universal access to energy remains a huge challenge given the lack of resources and the large population currently unserved. The lack of adequate socio-economic data at granular scale and of a good understanding of demand uptake led by economic growth is a barrier for efficient energy planning. Access to conjoint demand and socio-economic data at local level is crucial, yet hard to obtain: often such data are unavailable or very difficult to collect, and current data platforms often lack the ability to conjointly store variegated socio-economic and time series data. For these reasons, in this paper, we present a comprehensive methodology that, based on an extensive literature review, draws guidelines for developing data-sharing platforms in energy access, develops a proposed architecture to support the data collection of conjoint socio-economic and time-series data, and proposes a prototype of the final application. The methodology leverages on a novel extensive literature review to identify the major determinants of demand uptake and the corresponding consuming entities: villages, households, and appliances. The proposed architecture is able to capture numeric, categorical, and time series information for all consuming entities, based on state-of-the-art NoSQL databases. Finally, a prototype implementation with a web-based interface developed with Angular and Spring is proposed and discussed.

Energy system models for off-grid systems usually tend to focus solely on the provision of electricity for powering simple appliances, thus neglecting more energy-intensive and critical needs, such as water heating. The adoption of a Multi-Energy System (MES) perspective would allow us not only to provide comprehensive solutions addressing all types of energy demand, but also to exploit synergies between the electric and thermal sectors. To this end, we expand an existing open-source micro-grid optimization model with a complementary thermal model. Results show how the latter achieves optimal solutions that are otherwise restricted, allowing for a reduction in the Levelized Cost of Energy (LCOE) of 59% compared to a conventional microgrid, and an increase of reliance on renewable sources of 70%.