A new techno-economic assessment published in Discover Energy evaluates the potential of wind power to provide cost-competitive electricity in Burundi. The study, led by Gatoto Placide and Cartland Richard, analyzes four wind-prone locations using three specific Enercon wind turbine models. By integrating high-resolution meteorological data with detailed financial modeling, the research offers a granular look at how wind energy could diversify the country’s heavily hydropower-dependent grid.
Burundi’s electricity sector is currently dominated by hydropower, which accounts for approximately 95% of total domestic electricity production. This heavy reliance on a single renewable source exposes the power system to significant climate variability. Since hydropower output is directly linked to river flows and precipitation patterns, the country faces risks of reduced generation during drought periods and increased supply instability. In 2020, only 11.73% of the population had access to electricity services, highlighting a critical gap between energy demand and available supply. The study aims to address this vulnerability by exploring wind power as a complementary energy source that can operate independently of hydrological conditions.
To assess the wind potential, the researchers utilized wind speed data generated by the Weather Research and Forecasting (WRF) model. This numerical weather prediction tool was configured to reflect Burundi’s specific climatic and topographic conditions, providing hourly wind speeds at a reference height. The data was spatially processed using ArcGIS to create a national wind speed distribution map. From this map, four wind-prone sites, labeled S1 through S4, were selected based on high annual mean wind speeds and land suitability constraints. The WRF model was chosen for its ability to provide high-resolution atmospheric fields in data-scarce regions, although the authors note that model outputs may introduce biases in absolute wind speed estimation due to boundary layer parameterization uncertainties.
The study evaluated three commercial wind turbines manufactured by the German company Enercon: the E-40/5.4 (500 kW), the E-58/10.58 (1 MW), and the E-70 E4 (2 MW). These models were selected to represent technologies suitable for Burundi’s current energy infrastructure and moderate wind regimes. Larger utility-scale turbines, which can exceed 5 to 8 MW, were excluded because they generally require extensive grid infrastructure and substantial investment capacity that may not be feasible for near-term deployment in the country. The selected turbines allow for an evaluation of how increasing hub height and turbine size influence both Annual Energy Production (AEP) and the resulting cost of electricity.
Wind speeds at the turbine hub heights were estimated using the power-law wind shear model, which extrapolates data from the reference height to the specific hub height of each turbine model. The Annual Energy Production for each site and turbine combination was calculated by integrating the manufacturer’s power curve over the wind-speed probability distribution. This process accounts for the turbine’s efficiency and availability, providing a realistic estimate of the energy yield over a year. The capacity factor, which compares the average power output to the maximum rated power, was derived from these calculations to assess the operational efficiency of the turbines at each location.
The economic analysis employed the Present Value of Cost (PVC) method to estimate the unit cost of electricity. This approach calculates the total discounted cost of the project over its 20-year lifetime, including capital expenditures (CAPEX), operation and maintenance costs (OPEX), inflation, discount rates, and salvage value. The study assumed that CAPEX represents 75–80% of the total project investment, while OPEX is estimated at 25% of the annual turbine cost, adjusted for inflation. The average annual inflation rate in Burundi was set at 9.9%, and the interest rate at 7.54%. By dividing the total PVC by the AEP, the researchers determined the unit cost of energy in US dollars per kilowatt-hour for each site and turbine model.
The results indicate that Site S1 exhibits the highest wind potential among the four locations evaluated. At this site, the 2 MW E-70 E4 turbine produced approximately 29.5 GWh per year, while the 500 kW E-40/5.4 turbine generated about 19.5 GWh per year. In contrast, Site S4 showed the lowest energy production, with the 2 MW turbine yielding only 3.4 GWh per year. The 500 kW E-40/5.4 turbine consistently provided the lowest electricity cost across all sites due to its lower capital investment and favorable performance under local wind conditions. This smaller turbine is particularly suited for residential consumption and decentralized electrification, where large-scale grid connections are not available.
The estimated unit cost of wind-generated electricity ranged from US$0.025 to US$0.22 per kWh, depending on the specific site characteristics and turbine size. At the most favorable sites, such as S1, the cost of wind electricity is comparable to or lower than the current national electricity tariffs in Burundi, which range from US$0.0288 to US$0.2267 per kWh. This cost competitiveness suggests that wind power could serve as a viable alternative to existing energy sources, particularly in regions with limited electricity infrastructure. The study highlights that while larger turbines offer higher energy yields, their economic advantage is highly dependent on the quality of the local wind resource and the cost of grid infrastructure.
The findings are consistent with similar techno-economic assessments conducted in other regions, such as Ghana, Thailand, and Djibouti. In these studies, unit costs of wind electricity generally fell between US$0.0469 and US$0.093 per kWh, depending on the specific wind regimes and project designs. The Burundi study’s cost range falls within this international benchmark, confirming the plausibility of the results. The comparison underscores that economic viability is primarily site-dependent, with strong wind resources and appropriate turbine sizing leading to more favorable cost outcomes. This consistency across different geographic and economic contexts supports the general applicability of wind energy economics in developing regions.
Despite the promising results, the study acknowledges several limitations. The wind data were derived from WRF simulations rather than long-term in-situ measurements, which may introduce biases in the absolute energy yield values. The authors recommend that future work incorporate measured long-term wind mast data to strengthen the accuracy of the estimates. Additionally, the study focuses on turbine-level costs and does not fully account for the potential expenses associated with transmission expansion required for large-scale grid integration. In regions with limited electricity infrastructure, such costs could increase the overall system price beyond the turbine-level levelized cost of electricity.
The research suggests that decentralized small and medium-scale wind systems could play a crucial role in improving electricity access in underserved areas of Burundi. By supporting rural electrification, mini-grids, and isolated communities, these systems can reduce the need for extensive transmission networks and enhance energy system resilience. The diversification of the national energy mix through wind power can also reduce vulnerability to seasonal hydrological variability, ensuring a more stable electricity supply. This approach aligns with broader goals of improving energy security and supporting socioeconomic development through reliable and affordable power.
In conclusion, the study demonstrates that wind power has significant potential to contribute to Burundi’s electricity generation, particularly at sites with strong wind resources like S1. The 500 kW Enercon E-40/5.4 turbine emerges as the most economical option for many locations, offering a cost-competitive solution for localized energy needs. By integrating wind energy into the national grid, Burundi can mitigate the risks associated with its heavy reliance on hydropower and work towards universal electricity access. The findings provide valuable insights for policymakers and investors seeking to diversify the country’s energy mix and enhance long-term climate resilience.
Subject of Research: Techno-economic assessment of wind power generation costs in Burundi
Article Title: Analysis of the electricity generation cost per unit from wind resources in Burundi
Article References: Placide, G., & Richard, C. (2026). Analysis of the electricity generation cost per unit from wind resources in Burundi. Discover Energy, 6(1), Article 55. https://doi.org/10.1007/s43937-026-00182-z
Image Credits: AI Generated
DOI: 10.1007/s43937-026-00182-z
Keywords: wind energy, Burundi, electricity cost, renewable energy, techno-economic analysis, hydropower, energy security, Enercon turbines, Analysis, electricity, generation, cost
News Source: Faith Mcneil. (November 8, 2026). Burundi Wind Study Finds Competitive Electricity Costs. Scienmag.
