Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

# Planning sustainable electricity solutions for refugee settlements in sub-Saharan Africa

## Alternative electricity demand scenarios and sensitivities

To enrich the understanding of the scope of the efforts associated with providing electricity access to refugees in SSA, the RSEA DB also includes the outcomes of the same techno-economic analysis presented above, under two alternative demand scenarios. The so-called tier 1 scenario is estimated assuming an average consumption for residential loads equal to the one measured in Kalobeyei (a minimum of 0.074 kWh per household per day). The tier 3 scenario (with a minimum of 1 kWh per household per day) reflects, instead, an increase of residential electricity demand to tier 3 (ref. 38) access which includes small appliances (for example, general food processing and washing machine). As visible from Fig. 3 (Supplementary Table 4), by almost doubling residential electricity demand from tier 1 to tier 2 (the number of loads remains the same) the techno-economic indicators show a twofold increase. Increasing the residential demand by five times, from tier 2 to tier 3, results in a large increment in the total up-front costs (from US$1.3 to 5 billion). In the same way, the solar PV and battery capacity increases dramatically and this is reflected in a similar increase in avoided GHG emissions. The fixed ratios between indicators in alternative demand scenarios derive from having common assumptions about the costs of the technical elements in the mini-grids and from the scaling of the same load profiles over all settlements. Additional field data collections would be essential to capture the variety of costs and energy needs both across refugee sites and within them and to design mini-grids that are more closely tailored to the actual situation in each settlement8. Other opportunities to design more specific interventions would derive from integrating the energy demand specifically associated with water services (boreholes) and, potentially, cooking needs. Figure 4 considers, instead, the effects on LCOE values (under tier 2) of changes in economic and technical parameters. Figure 4a focuses on changes in the discount rate from the reference value of 10%. The latter is consistent with the observed practice in Kalobeyei: a foreign energy company assessing a project to be realized in a displacement setting operated under the UNHCR mandate (rather than, generically, in the hosting country) and considering all cost elements in the LCOE directly in foreign currency. Although cross-country differences in the cost of capital39,40 can be significant, the realization of electrification projects in refugee sites is habitually part of a concerted strategy between national and international organizations10. These strategies include measures to reduce investment risk, in the form of grants to cover up-front costs and favour projects with positive externalities (for their overall societal benefits), rather than more favourable financing conditions. All these considerations motivate the use of a single discount rate for all settlements in the RSEA DB. Nevertheless, it is important to note that even the smaller variation in Fig. 4a (from 10% to 12%; Supplementary Table 5a) corresponds to a 13% change in the median LCOE (from US$0.30 kWh–1 to US$0.34 kWh–1). Furthermore, Fig. 4b compares, first, the LCOE values of the fully renewable systems discussed so far, with the LCOE values of an alternative technological option (also optimally sized for each settlement), that is a PV mini-grid using diesel generators as backup (current diesel cost are differentiated per country, thus include existing national diesel subsidies Supplementary Table 1b). Although the median difference is US$0.05 kWh–1, the data are significantly dispersed. Figure 4b shows also the difference in LCOE values when calculated with country-specific discount rates rather than a uniform one (Supplementary Table 5b). As expected, these differences are higher for the fully renewables systems (median value US$0.11 kWh–1) than for the hybrid ones (median value US$0.05 kWh–1) and more dispersed.

## Integration with earth observations and dissemination

The data collected in the RSEA DB are suitable to support research in further directions, as well as output dissemination. To illustrate the advantage of having direct access to the RSEA DB, recall that one of the crucial aspects to consider in planning electricity access interventions in refugee settlements is the interaction with the hosting communities9,41,42. Indeed, night-lights in Fig. 1 suggest that numerous refugee settlements in SSA might be located in areas where the host communities also lack access to electricity. The map in Fig. 5a analyses the matter more closely by combining the distribution of population without access to electricity18 and the boundaries of the refugee settlements (both derived from earth observations). The integration of such data can provide a first indication of where it would be sensible for electricity access projects to consider the joint needs of displaced and local populations12.

Furthermore, Fig. 5b–f combine accessibility data43,44 and the geographical boundaries of the refugee settlements providing a way to capture the potential level of integration with the host community (Methods). Proximity, in terms of travel time to the refugee sites illustrates, for instance, the opportunity for the local population41 (without access to electricity) to use public services, such as health facilities, or to access businesses located in the settlement. Sharing services, not for absence of equivalent ones within the host community but, indeed, for reasons of integration, might be beneficial but, clearly, further investigation would be necessary to establish the feasibility and realism of this proposition on a case-by-case basis. With these caveats in mind, Fig. 5e compares, for 13 illustrative cases, the cumulative population curves, capturing the number of people within a given motorized travel time to the refugee settlement. Considering that private ownership of motorized vehicles is not common in these regions, this information is mainly relevant for accessing emergency healthcare. Differences across sites are apparent. For instance, a larger number of people can more rapidly access Kyangwali in Uganda than Kakuma in Kenya, primarily due to the density of the local population (sparser in the case of Kakuma), the existing transport infrastructure and the local geographical conditions. Figure 5b–d further illustrate this concept for three sites: El Daein in Sudan, Kalobeyei in Kenya and Kouchaguine in Chad (the increase in travel time is expressed with a colour change). The maximum motorized travel time (dotted line) captures where the local population reaches the same number as the refugees in the settlement. The latter represents a theoretical upper-bound estimation of the level of integration via shared services (a maximum catchment area for the facilities). For the population without electricity within this area, Fig. 5f further illustrates the distribution of the motorized travel time per settlement. For example, the travel time to Kakuma ranges from 70 min for the nearest population to 160 min for the further population (with an average travel time of 2 h).

As for the second point, output dissemination, a selection of the RSEA data contributes to the datasets included in the interactive open-access Clean Energy Access Tool. The Clean Energy Access Tool was developed for visualizing and analysing information on electricity access in Africa and the overall energy outlook (Fig. 6). Specifically, it allows the visualization of the refugee sites, enables the analysis of the related indicators (based on user-defined polygons or at national and/or subnational levels) and the downloading of the datasets (RSEA DB can be downloaded from https://data.jrc.ec.europa.eu/collection/id-0076). Ultimately, it is expected to inform the wider public and to support institutional stakeholders’ decision-making processes.

## Discussion

This work sheds light on the potential of decentralized energy systems to offer a fast, reliable and sustainable way to increase access to electricity in refugee settlements in SSA. The continental scope of the data collection and techno-economic analysis conducted in this work enable an estimation of the scale of the overall effort (US\$1.34 billion in up-front costs) and environmental benefit (0.14 MtCO2e avoided per year) of providing clean electricity access in all the observed settlements. Furthermore, by facilitating access to aggregated and settlement-specific quality data, the RSEA DB and the associated web tool (https://africa-knowledge-platform.ec.europa.eu/energy_tool) can support humanitarian and development organizations in eliciting an effective response across countries and actors (for example, local government and donors) to deliver sustainable energy solutions on a large scale. Energy data in humanitarian contexts are also useful for the growing academic community studying technical solutions for evidence-based interventions. As shown, new data sources are extremely helpful to address the fragmented and incomplete nature of currently available information; and the opportunities to use the present information set to pursue other research avenues are potentially numerous as well as to tailor the information to the evolving needs and realities.

There are two important directions for future investigation. A first one regards the completeness and representativeness of the RSEA DB over space and time. The data-intensiveness of the analysis implies growing uncertainty over the reliability of the information, as some sources such as existing grid infrastructure, site boundaries and number of refugees per sites become outdated or are deemed incomplete—tracking the fast evolution of population data and energy demand is a key aspect in the humanitarian context. Also, it is important to continue to address the uncertainty regarding the quality of the collected publicly available and open-source data. Ongoing feedback from the field is the most obvious way to improve quality but also other approaches such as validation by experts, peer review, comparison with other collected data and use of standardized and calibrated measurement for mini-grid performance.

Second, while expansions to other geographical areas using a similar approach would be useful, energy interventions in humanitarian contexts should be informed by more than only techno-economic models. Complementary methodologies include social impact assessment studies, institutional analyses, economic and ethnographic studies and community engagement processes42,45,46. Particularly in protracted situations, electricity is an investment for the future. Hence, social cohesion between the settlement and the host community, current and future generations’ well-being, potential impacts on the most vulnerable groups among the refugees are fundamental concerns to be addressed, together with any other competing basic needs.

## Methods

### Building a comprehensive database

The RSEA DB was designed and developed to gather the multidimensional factors47 that determine the energy access dimension for refugee settlements. These factors were chiefly environmental (such as variability of solar radiation and avoided GHG emissions), technical (electrification status, distance to grid), social (population, electricity demand, social infrastructure) and economic (PV mini-grid component prices, discount factors, operation and maintenance costs). The main indicators capturing these factors are listed in Supplementary Table 6.

To retrace the steps behind the creation of the RSEA DB, note the following.

1. (1)

The holistic structure of the RSEA DB was informed by an extensive review of the existing literature including whitepaper reports1,10,11,12,13,14,21,32,38,48,49 and academic papers3,4,5,6,8,9,19,25,34,35,36,41,42,45,46,50,51 which focus on energy access and refugee settlements. The analysed documents were used to gather demographic figures from UNHCR statistics, to obtain field data from humanitarian contexts and other relevant data from studies on solar and hybrid (solar-diesel) mini-grids installed in refugee camps or rural communities in Africa. Studies on mini-grids in similar contexts were analysed in depth and technical and financial information, such as generation component prices, battery costs and discount rates were retrieved, as well as documentation on the institutions operating in the camps, number of businesses, health facilities and so on. Additional information regarding the hosting countries was also collected from literature (for example, inflation rates, currency exchange, electricity tariffs).

2. (2)

Field data on electricity needs were collected and retrieved from the Kalobeyei settlement in Kenya in 2020. Specifically, current electricity consumption was measured from the existing electricity supply: diesel generators, an existing PV mini-grid (metered data), solar home systems (PV and battery storage) and PV panels7 already owned by refugees (second-hand panels were mostly found to be in poor conditions). The data collection was conducted via a questionnaire administered to the refugee population of the settlement. The semistructured survey reached 325 interviewees, properly selected among type of end-users (households, businesses and institutions) and their geographic location inside the camp (the settlement is divided into three villages).

3. (3)

The data collection was complemented by stakeholder interviews. The surveyed experts were selected from various humanitarian organizations (Danish Refugee Council, Norwegian Refugee Council and UNHCR), development actors (Deutsche Gesellschaft fur Internationale Zusammenarbeit, Global Plan of Action, Netherlands Development Organisation and United Nations Institute for Training and Research), research institutions (École Polytechnique Fédérale de Lausanne and Imperial College London) and civil society organizations (Renewvia Energy Kenya Limited) involved in the coordination and organization of refugee settlements in SSA. A complete list of the stakeholders interviewed is stored in Supplementary Table 7 (the response rate was around 10%). Selection of the questions used for the interviews was mainly based on the background, expertise and agency-provenience of the interviewees. Qualitative discussions with the experts were aimed to cross-check and validate the information from the literature, understand the socio-economic framework in refugee settlements in Africa (for example, electrification conditions, social context and refugee energy routines), to obtain verifiable data sources inherent to energy-related humanitarian operations and to complete the technical data input for actual energy operations based on field data from the settlements (system losses, efficiency, lifetime of the system and so on).

After completing the data collection and the techno-economic analysis described below, the following parameters were integrated in the RSEA DB for each of the settlements.

1. (1)

Descriptive parameters: name and coordinates of the site location, name and type of the refugee settlement, number of refugee population, actual or estimated number of households, businesses and public institutions. Additional parameters are distance to the national electric grid and the closest national border.

2. (2)

Energy demand parameters: residential, business and institutional daily electricity demand at settlement level (kWh d–1), estimated at year 5 of the lifetime of the project (20 yr). Daily electricity demand estimated at year 0 for a single household, business and public institution (kWh per connection per day).

3. (3)

Techno-economic parameters: optimized PV and battery size. Up-front cost of generation (PV, battery and balance of system). Up-front cost of distribution (customer connection fee, overhead lines and substation). Soft cost for logistics and project management and for contingencies. Replacement cost (inverter and battery). LCOE values with three discount rates (5%, 10% and 12%). LCOE sensitivity analyses for operation and maintenance costs (2% and 3% of up-front costs) and four levels of grant coverage of the customer connection fee (25%, 50%, 75% and 100%).

4. (4)

Climate parameters: daily and yearly avoided GHG emissions at year 5 of the lifetime of the project.

5. (5)

Geospatial datasets: PVGIS15, GHSL17, NOOA52, OSM53 and national grid54 were integrated in the RSEA DB to geographically contextualize the refugee settlements (distribution of population, solar radiation, accessibility maps, existence of electricity grid and night-light imagery).

The RSEA DB tracks 288 settlements organized in 203 refugee sites. The settlement coincides, in most cases, with the site. However, for some sites hosting a large number of refugees, population data were publicly available for smaller administrative units (for example, Bidibidi in Uganda, is organized in five units or ‘zones’ and Kakuma, in Kenya, in four ‘villages’). Hence, each subadministrative unit within the same site was traced as a different settlement. Conversely, in the absence of information regarding the subadministrative units, some large refugee sites had to be considered as a single settlement (for example, Nyarugusu in Tanzania).

### Estimating electricity demand and daily load profiles

The electricity demand and load profile per refugee settlement were calculated by considering the three types of users below.

1. (1)

Residential users: the electricity demand at the household level corresponds to a small-scale power of needs (for example, for tier 1, task light and phone charging; and, for tier 2, adding general lighting, television, fan or computing).

2. (2)

Productive/ business users: the electricity demand for income-generating activities includes energy requirements for commercial usage and industrial light (small businesses).

3. (3)

Public/ institutional users: this category consists of demand for facilities such as street lighting, back-office administration, communication services, healthcare and education services.

Note that only users located inside the settlement were considered. For instance, humanitarian compounds located outside the camps are not included. The number of household users in each settlement was retrieved from UNHCR statistics or estimated from the average number of people per household of each country. The number of businesses and institutions were retrieved from the literature or expert interviews (nine settlements only) or estimated using a fixed ratio of business and institutional users to residential ones (respectively, 0.0507 and 0.0059, computed as average values across the nine observed settlements)—in fact, the number of businesses and institutions will vary (perhaps significantly) across settlements.

Settlement-specific daily load profiles for households, businesses and institutions were derived from the corresponding daily load profiles estimated for Kalobeyei. The latter process involved assessing, during the field visit, the hourly electricity demand of the respondents having access to any source of electricity in each village and adjusting these figures to the actual number of users. The available sources of electricity were an operational mini-grid (7% of the respondents), several diesel generators (9%) and solar lamps or second-hand PV solar panels purchased from the market (45%)—the remaining 61% of the respondents had no access to electricity. The estimated daily load profiles per type of user and per village in Kalobeyei are shown in Supplementary Table 8b.

To calculate, for each user type, a settlement-specific daily load profile, five corrective factors (CFs) were defined and applied to the load profiles in Supplementary Table 8b. These factors adjust the load profiles estimated for Kalobeyei, to the characteristics of each settlement and account for: the number of connections per type of user (CF1); the presence of users opting to remain with a pre-existing electricity provider (CF2); the fact that part of the users in Kalobeyei were already connected to a mini-grid (CF3); and the different tiers (CF4 and CF5 for, respectively, tier 2 and tier 3). A full description of the CFs can be found in Supplementary Table 8a. The aggregated daily load profile for each settlement is the sum, over all user types, of the settlement-specific daily load profiles per type of user—for an example see Supplementary Fig. 1.

Note that for the purpose of sizing the mini-grid capacity, the hourly power demand which composes the settlement-specific daily load profile per user type, is assumed to grow annually by 10%, starting from the initial values estimated for year 0 with the CFs and until year 5. After year 5, the hourly power demand remains constant for the lifetime of the project. These choices mimic the assumptions on potential demand growth made by mini-grid developers in Kenya. Expert interviews confirmed that the limited available experience and the uncertainty regarding the number of refugees living in each settlement can strongly affect the reliability of long-term forecasting for these projects.

The aggregate settlement-specific electricity demand and the settlement-specific electricity demand per user type, were calculated from the load profiles. Hence, the daily electricity demand for a single household depends on the year and on the scenario:

1. (1)

Tier 1—this scenario was based on the data registered in Kalobeyei (0.07 kWh d–1 per household in year 0).

2. (2)

Tier 2—this scenario is coherent with the SDG 7 framework20,24 and with the goal set by UNHCR21,51 for refugee settlements (0.20 kWh d–1 per household in year 0).

3. (3)

Tier 3—this scenario captures a potential increase from the current goal (tier 2) to the equivalent of tier 3 (ref. 38) access (1 kWh d–1 per household in year 0).

The daily electricity demand for a single business and for a single institution depend only on the year and were also derived from the field data collected in Kalobeyei (0.26 kWh d–1 and 3.14 kWh d–1, respectively, in year 0).

### Optimizing the PV and hybrid PV/diesel mini-grid

The total amount of solar irradiation and the intermittency of the solar irradiation depend strongly on the geographical location. On the basis of these data and the specific load characteristics of each refugee settlement, the PV mini-grid was sized with 100% renewable energy and with PV plus diesel generator as backup. The optimal size of the PV array and battery storage for a given settlement was computed to ensure loss of power supply probability37 at 10%.

The annual electricity generated yearly by the PV mini-grid was calculated for each settlement using a combination of models20: (1) a model for effective irradiance with hourly solar radiation measured from satellites (combined with temperature and wind speed data from re-analysis); (2) a PV output power model with measured data on module performance; and (3) a model for battery performance based on measured battery data. This methodology has been described in detail and validated19,20,55. As for the hybrid system56,57, current diesel cost were used, differentiated per country, thus they include national diesel subsidies.

The distribution network consists of low-voltage overhead feeders sustained by poles and end-user connections. Settlements with a number of households above 2,000 (60% of the observed ones) include a substation as an additional distribution element.

### Assessing electrification costs

The cost of providing electricity to each settlement via a PV plus battery storage mini-grid was analysed in two ways: (1) estimating an up-front capacity cost on the basis of the system specifications; and (2) computing the corresponding LCOE. The up-front capacity cost was calculated on the basis of the PV plus battery storage mini-grid optimization outputs (PV size and battery size) and three main cost categories: generation, distribution and soft costs. Field data collected in Kenya were used to set these three cost categories7 (Supplementary Table 1).

For each refugee settlement (n), the LCOE was calculated using a 20 years lifetime (T) and a discount rate (r) of 10% (refs. 15,58). The up-front capacity costs (Up_cost0 in year t = 0), the annual operation and maintenance cost (O&Mt), the asset replacement (Rt, battery and PV inverter at year 10) and financial costs (Ft) were taken into consideration by the following equation:

$${{{\mathrm{LCOE}}}}_{{{n}}} = \frac{{{{{\mathrm{Up}}}}\_{{{\mathrm{cost}}}}_0 + \mathop {\sum}\nolimits_{{{{t}}} = 1}^T {\left\{ {\left( {{{{\mathrm{O}}}}\& {{{\mathrm{M}}}}_{{{t}}} + {{{R}}}_{{{t}}} + {{{F}}}_{{{t}}}} \right)/\left( {1 + {{{r}}}_{{{n}}}} \right)^{{{t}}}} \right\}} }}{{\mathop {\sum}\nolimits_{{{{t}}} = 1}^{{{T}}} {\left\{ {\left( {{{{\mathrm{ES}}}}_{{{n}}}} \right)/\left( {1 + {{{r}}}_{{{n}}}} \right)^{{{t}}}} \right\}} }}$$
(1)

where O&Mt costs are 1% of the up-front capacity costs, financial expenditures include VAT payments, insurance, interest expenses and $$\mathrm{ES}_n$$ is the annual electricity production from the PV mini-grid. The same financing structure used in Kalobeyei was assumed, with 90% grant, 8% equity and 2% debt.

The indicator LCOEn includes the entire up-front capacity costs. The indicator LCOE_genn (also reported in the database) is the part of the LCOE for the generation components only. The reference values and variations of the parameters used in the sensitivity analyses (discount rate, O&M costs and customer connection fee) are shown in Supplementary Table 5a. Supplementary Fig. 2 illustrates (for the reference tier 2 demand scenario) that, starting from O&M costs equal to 1% of the up-front costs59, an increase to 2% and 3% corresponds to a LCOE increases of 1.5% and 1.7%, respectively. In a similar manner, assuming that the customer connection fee might be covered partially (25%) or entirely (100%) by a grant also lead to relatively small decrease in the median value of the LCOE (comprising between –0.1% and –1.3%).

The effects on the LCOE values (under tier 2) of varying the discount rate were illustrated in Fig. 4a. To study the effect of a country-specific discount rate on the LCOE (Fig. 4b), weighted average capital cost values were calculated47 with recent input data on equity rate of return and debt interest rate in each country (World Bank/ IMF lending interest rate, 2021)60 using the methodology described previously40. The country-specific weighted average capital cost values (Supplementary Table 5b) were then included in the LCOE location-specific estimates for both the fully renewable and the hybrid PV/ diesel mini-grids56,57.

### Estimating avoided emissions

The estimation of the carbon mitigation potential of using fully renewable mini-grids is based on the avoided GHG emissions in CO2e (ref. 59). The avoided GHG emissions were calculated by computing the annual emissions of a stand-alone diesel generator supplying the settlements’ electricity demand in year 5, when demand becomes stable and the system is operating at full capacity. Supplementary Table 9 summarizes the relevant parameters and sources used to compute the emission factor (0.93 tCO2 MWh–1).

### Observing the host communities

The lack of harmonized geodata concerning the African population, together with inconsistent demographic information produced through census campaigns, has prompted the scientific community to extract information from the available satellite remote sensing archives61. Consistently, a multicriteria site-selection algorithm was designed to identify the populated areas at a given motorized travelling distance from 13 selected refugee settlements and without access to electricity. The criteria used to delineate these areas are:

1. (1)

Identify the borders of the selected refugee settlements by combining cartographical (OSM) and earth observation data. Missing boundaries were added through visual interpretation of satellite images (Microsoft Bing aerial images).

2. (2)

Estimate the cumulative travel distance by motorized44 means for the selected refugee settlements. The calculation considers the travel distance starting from the settlement borders until reaching the maximum distance of 10 km. The areas inside each site were not considered in this analysis.

3. (3)

Extract the catchment area considering the population without access to electricity18 around the refugee settlements that corresponds to the total population living inside these settlements.

The requirement of data extraction for obtaining missing settlement boundaries limited the analysis to a few settlement areas. This limitation can be overcome in future analyses if cartographic information becomes available or a specific algorithm is developed to automate the process of settlement boundary delimitation and eliminate the need for visual interpretation techniques.

## Data availability

The RSEA DB generated during this study is available in two file formats (csv and geopackage) at https://data.jrc.ec.europa.eu/dataset/4261bf3c-7e8e-4b16-925b-68cfd4eade37. Visualization interactive Clean Energy Access Tool is available at https://africa-knowledge-platform.ec.europa.eu/energy_tool. Source data are provided with this paper.

## References

1. Lahn, G. & Grafham, O. Heat, Light and Power for Refugees: Saving Lives, Reducing Costs (Chatham House, 2015).

2. Boodhna, A., Sissons, C. & Fullwood-Thomas, J. A systems thinking approach for energy markets in fragile places. Nat. Energy 4, 997–999 (2019).

3. Huber, S. & Mach, E. Policies for increased sustainable energy access in displacement settings. Nat. Energy 4, 1000–1002 (2019).

4. Rosenberg-Jansen, S., Tunge, T. & Kayumba, T. Inclusive energy solutions in refugee camps. Nat. Energy 4, 990–992 (2019).

5. Baranda Alonso, J., Sandwell, P. & Nelson, J. The potential for solar–diesel hybrid mini-grids in refugee camps: a case study of Nyabiheke camp, Rwanda. Sustain. Energy Technol. Assess. 44, 101095 (2021).

6. Thomas, P. J. M., Sandwell, P., Williamson, S. J. & Harper, P. W. A PESTLE analysis of solar home systems in refugee camps in Rwanda. Renew. Sustain. Energy Rev.143, 110872 (2021).

7. Baldi, D. Electricity Access in Displacement Settings—Electrification Planning in Refugee Camps in Africa (Utrecht Univ., 2021).

8. Grafham, O. & Sandwell, P. Harness better data to improve provision of humanitarian energy. Nat. Energy 4, 993–996 (2019).

9. Grafham, O. (ed.) Energy Access and Forced Migration (Routledge, 2019).

10. The Global Plan of Action for Sustainable Energy Solutions in Situations of Displacement: Framework for Action (UNITAR, 2019).

11. Kalobeyei Integrated Socio-Economic Development Plan in Turkana West (UNHCR, 2018).

12. Renewable Electrification of Refugee Camps—Phase 1 (SWECO, 2021).

13. Corbyn, D. & Vianello, M. Prices, Products and Priorities: Meeting Refugees’ Energy Needs in Burkina Faso and Kenya (Moving Energy Initiative, 2018).

14. Access to More: Creating Energy Choices for Refugees (Vivid Economics, 2020).

15. Photovoltaic Geographical Information System (PVGIS) (European Commission—JRC, accessed 1 September 2019); https://ec.europa.eu/jrc/en/pvgis

16. Geo-Information System for Renewables (European Commission—JRC, accessed 15 October 2021); https://data.jrc.ec.europa.eu/collection/id-0076

17. Schiavina, M., Freire, S. & MacManus, K. GHS-POP R2019A—GHS Population Grid Multitemporal (1975-1990-2000-2015) (European Commission—JRC, 2019); http://data.europa.eu/89h/0c6b9751-a71f-4062-830b-43c9f432370f

18. Falchetta, G., Pachauri, S., Parkinson, S. & Byers, E. A high-resolution gridded dataset to assess electrification in sub-Saharan Africa. Sci. Data 6, 110 (2019). https://doi.org/10.1038/s41597-019-0122-6

19. Moner-Girona, M. et al. Decentralized rural electrification in Kenya: speeding up universal energy access. Energy Sustain. Dev. 52, 128–146 (2019). https://doi.org/10.1016/j.esd.2019.07.009

20. Huld, T., Moner-Girona, M. & Kriston, A. Geospatial analysis of photovoltaic mini-grid system performance. Energies 10, 218 (2017).

21. UNHCR Global Strategy for Sustainable Energy 2020–2025 (UNHCR, 2019).

22. Beyond Connections: Energy Access Redefined (SE4All, Energy Sector Management Assistance Program, 2015).

23. Puig, D. et al. An action agenda for Africa’s electricity sector. Science 373, 616–619 (2021).

24. Tracking SDG 7: The Energy Progress Report (World Bank Publications, 2020).

25. Neves, D., Baptista, P. & Pires, J. M. Sustainable and inclusive energy solutions in refugee camps: developing a modelling approach for energy demand and alternative renewable power supply. J. Clean. Prod. 298, 126745 (2021).

26. Africa Energy Outlook (IEA, 2019).

27. Come Zebra, E. I., van der Windt, H. J., Nhumaio, G. & Faaij, A. P. C. A review of hybrid renewable energy systems in mini-grids for off-grid electrification in developing countries. Renew. Sustain. Energy Rev. 144, 111036 (2021).

28. Renewable Energy in Hybrid Mini-Grids and Isolated Grids: Economic Benefits and Business Cases (Frankfurt School—UNEP Collaborating Centre for Climate and Sustainable Energy Finance, 2015).

29. Safdar, T. Business Models for Mini-Grids (Smart Villages, 2017).

30. Innovation Outlook: Renewable Mini-Grids (IRENA, 2016).

31. Comello, S. D., Reichelstein, S. J., Sahoo, A. & Schmidt, T. S. Enabling mini-grid development in rural India. World Dev. 93, 94–107 (2017).

32. Update on Budgets and Funding (2019, 2020–2021) (UNHCR, 2020); https://www.unhcr.org/5e6a3c497.pdf

33. Krupa, J., Poudineh, R. & Harvey, L. D. D. Renewable electricity finance in the resource-rich countries of the Middle East and North Africa: a case study on the Gulf Cooperation Council. Energy 166, 1047–1062 (2019).

34. Irene Feria Cerrada, M. & Thomson, A. PV microgrid business models for energy-delivery services in camps for displaced peoples. J. Humanit. Eng. https://doi.org/10.36479/jhe.v5i2.93 (2017).

35. Bellanca, R. in Energy Access and Forced Migration (ed. Grafham, O.) Ch. 6 (Routledge, 2019).

36. Sandwell, P. et al. Ensuring Refugee Camps in Rwanda have Access to Sustainable Energy (Practical Action, 2020).

37. Georgilakis, P. S. & Katsigiannis, Y. A. Reliability and economic evaluation of small autonomous power systems containing only renewable energy sources. Renew. Energy 34, 65–70 (2009).

38. Mini Grids for Half a Billion People: Market Outlook and Handbook for Decision Makers (ESMAP, 2019).

39. Egli, F., Steffen, B. & Schmidt, T. S. Bias in energy system models with uniform cost of capital assumption. Nat. Commun. 10, 4588 (2019). https://doi.org/10.1038/s41467-019-12468-z

40. Ondraczek, J., Komendantova, N. & Patt, A. WACC the dog: the effect of financing costs on the levelized cost of solar PV power. Renew. Energy 75, 888–898 (2015).

41. Taylor, J. E. et al. Economic impact of refugees. Proc. Natl. Acad. Sci. USA 113, 7449–7453 (2016).

42. Miller, R. L. & Ulfstjerne, M. A. Trees, tensions, and transactional communities: problematizing frameworks for energy poverty alleviation in the Rhino Camp refugee settlement, Uganda. Energy Res. Soc. Sci. 63, 101404 (2020).

43. Weiss, D. J. et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553, 333–336 (2018).

44. Nelson, A. et al. A suite of global accessibility indicators. Sci. Data 6, 266 (2019).

45. Demir, E. Improving Energy Access for Displaced Populations: An Institutional Analysis for the Potential of Community Solar Mini-Grids in Refugee Camps (Delft Univ. Technology, 2020).

46. Verme, P. & Schuettler, K. The impact of forced displacement on host communities: a review of the empirical literature in economics. J. Dev. Econ. 150, 102606 (2021).

47. Moner-Girona, M. et al. A multidimensional high-resolution assessment approach to boost decentralised energy investments in Sub-Saharan Africa. Renew. Sustain. Energy Rev. 148, 111282 (2021).

48. Energy for Displaced People: A Global Plan of Action for Sustainable Energy Solutions in Situations of Displacement (UNITAR, 2018).

49. Data Transformation Strategy 2020–2025: Supporting Protection and Solutions (UNHCR, 2019).

50. Lehne, J., Blyth, W., Lahn, G., Bazilian, M. & Grafham, O. Energy services for refugees and displaced people. Energy Strategy Rev. 13–24, 134–146 (2016).

51. Dean, N. Strategies to empower. Nat. Energy 4, 1003–1005 (2019).

53. Planet OpenStreetMap (Planet OSM, accessed 1 June 2021); https://planet.osm.org

54. Moner-Girona, M. et al. Achieving universal electrification of rural healthcare facilities in sub-Saharan Africa with decentralized renewable energy technologies. Joule 5, 2687–2714 (2021).

55. Moner-Girona, M. et al. Mapping the Least-Cost Option for Rural Electrification in Burkina Faso: Scaling-up Renewable Energies (Publications Office of the European Union, 2017).

56. Husein, M., Hau, V. B., Chung, I. Y., Chae, W. K. & Lee, H. J. Design and dynamic performance analysis of a stand-alone microgrid: a case study of Gasa island, south Korea. J. Electr. Eng. Technol. 12, 1777–1788 (2017).

57. Husein, M. & Chung, I. Y. Optimal design and financial feasibility of a university campus microgrid considering renewable energy incentives. Appl. Energy 225, 273–289 (2018).

58. PV LCOE in Europe 2014–30 (EU PV Platform, 2015).

59. Moner-Girona, M. et al. Electrification of sub-Saharan Africa through PV/hybrid mini-grids: reducing the gap between current business models and on-site experience. Renew. Sustain. Energy Rev. 91, 1148–1161 (2018).

60. World Bank Open Data (World Bank, accessed 1 March 2021); https://data.worldbank.org

61. Paresi, M., Melchiorri, M., Siragusa, A. & Kemper, T. Atlas of the Human Planet: Mapping Human Presence on Earth with the Global Human Settlement Layer (Publications Office of the European Union, 2017).

62. Szabó, S., Bódis, K., Huld, T. & Moner-Girona, M. Energy solutions in rural Africa: mapping electrification costs of distributed solar and diesel generation versus grid extension. Environ. Res. Lett. 6, 034002 (2011).

## Acknowledgements

We thank N. Taylor (Joint Research Centre (JRC)—European Commission), F. Coloni and V. Lallini (UNHCR) for insightful comments on earlier versions of this article. We also acknowledge M. A. Husein (Abuja University) for his valuable insights in the PV mini-grid optimization modelling, P. R. Ristol (JRC—European Commission) for the development of the Clean Energy Access Tool, N. G. Bleier for proofreading the article and M. Ten (Trama TecnoAmbiental) for her valuable comments. F.F. is a consultant for JRC—European Commission. The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission.

## Author information

Authors

### Contributions

D.B., M.M.G. and E.F. conceived and designed the experiments. D.B. performed the experiments. D.B., M.M.G., E.F. and F.F. analysed the data. D.B., M.M.G., E.F. and F.F. contributed materials/ analysis tools. D.B., M.M.G. and E.F. wrote the paper.

### Corresponding author

Correspondence to Magda Moner-Girona.

## Ethics declarations

### Competing interests

The authors declare no competing interests.

### Ethics approval and consent to participate

According to standard procedures for energy assessment surveys in refugee settlements, the UNHCR office in Kakuma, first, and the Refugee Settlement Committee in Kalobeyei, second, were asked for and granted permission to conduct the survey. A member of the Committee accompanied the interviewers for the entire survey duration. Each of the individual respondents granted informed consent. Respondents were selected on a geographical basis to include all three administrative units that compose the settlement and the various countries of origin of the refugees (different areas of the settlement host different nationalities). Out of 325 in-person interviews, 234 were conducted with households, 68 with small businesses and 23 with institutions. The survey questions are available in the Supplementary Information.

## Peer review

### Peer review information

Nature Energy thanks Owen Grafham, Philip Sandwell and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

## Supplementary information

### Supplementary Information

Supplementary Figs. 1 and 2 and Kalobeyei Settlement Household Businesses and Institution Demand Survey.

### Supplementary Tables

Supplementary Tables 1–9: 1, Mini-grid components; 2, Moyo settlement; 3, Descriptive statistics; 4, Demand scenarios; 5, Sensitivity; 6, Main indicators; 7, Stakeholders; 8, Corrective factors; 9, Emission factors.

### Supplementary Data

Source data for Supplementary Figs. 1 and 2.

## Source data

### Source Data Fig. 2

Own elaboration of aggregated data per country included in RSEA DB.

### Source Data Fig. 3

Own elaboration with settlement data from RSEA DB.

### Source Data Fig. 4

Own elaboration with settlement data from of RSEA DB.

## Rights and permissions

Reprints and Permissions

Baldi, D., Moner-Girona, M., Fumagalli, E. et al. Planning sustainable electricity solutions for refugee settlements in sub-Saharan Africa. Nat Energy 7, 369–379 (2022). https://doi.org/10.1038/s41560-022-01006-9

• Accepted:

• Published:

• Issue Date:

• DOI: https://doi.org/10.1038/s41560-022-01006-9