Techno-Economic Potential of Wind-Based Green Hydrogen Production in Djibouti: Literature Review and Case Studies

Abstract

Disputed supply chains, inappropriate weather and low investment, followed by the Russian invasion of Ukraine, has led to a phenomenal energy crisis, especially in the Horn of Africa. Accordingly, proposing eco-friendly and sustainable solutions to diversify the access of electricity in the Republic of Djibouti, which has no conventional energy resources and is completely energy-dependent on its neighboring countries, has become a must. Therefore, the implementation of sustainable renewable and energy storage systems is nationally prioritized. This paper deals, for the first time, with the exploitation of such an affordable and carbon-free resource to produce hydrogen from wind energy in the rural areas of Nagad and Bara Wein in Djibouti. The production of hydrogen and the relevant CO₂ emission reduction using different De Wind D6, Vestas and Nordex wind turbines are displayed while using Alkaline and Proton Exchange Membrane (PEM) electrolyzers. The Bara Wein and Nagad sites had a monthly wind speed above 7 m/s. From the results, the Nordex turbine accompanied with the alkaline electrolyzer provides the most affordable electricity production, approximately 0.0032 $/kWh for both sites; this cost is about one per hundred the actual imported hydroelectric energy price. Through the ecological analysis, the Nordex turbine is the most suitable wind turbine, with a CO₂ emission reduction of 363.58 tons for Bara Wein compared to 228.76 tons for Nagad. While integrating the initial cost of wind turbine implementation in the capital investment, the mass and the levelized cost of the produced green hydrogen are estimated as (29.68 tons and 11.48 $/kg) for Bara Wein with corresponding values of (18.68 tons and 18.25 $/kg) for Nagad.

1. Introduction

Countries around the globe have to develop their energy transformation plans due to different primary obstacles, including market pressures, rising hydrocarbon costs, long-term energy security requirements and climate change, with growing local implications. The situation is more crucial in Djibouti and similar countries, where there is a lack of conventional energy resources and consequently full reliance on foreign energy sources. The first Ethiopia-Djibouti interconnection project from hydropower has been in operation since 2011. The electricity trade between the two countries has increased from 155 GWh in 2011 to 568 GWh in 2022 [1]. As a result, the cost of electricity remains very high in Djibouti, at around 0.32 $/kWh, compared to 0.006 $/kWh in Ethiopia and approximately 0.18 $/kWh in other neighbor countries, such as both Uganda and Kenya [1]. The global average electricity tariff is 0.182 $/kWh for household consumers [1]. To face the escalating demand for electricity and rapid urbanization, the Djibouti government has decided to focus on its energy policy for the development of renewable energy. In addition to the use of renewable energies in order to reduce negative environmental impacts, developing economic activities with less energy dissipation, energy sobriety and efficiency has become one of the fundamental objectives. Energy access in the subregion is generally quite low; the situation in Djibouti (55%) in this regard is better than other countries in the Horn of Africa [2]. However, Djibouti is experiencing sustained economic growth with a doubled population projected by 2050 [3]. About 60% of this population will be concentrated in urban areas, which is seven times the region’s GDP [3]. This situation requires more efficient energy production. It is estimated that electricity demand in Djibouti urban areas will grow with an annual average rate of 4% until 2050 [4]. In this regard, Djibouti has started several national-scale projects to introduce more access to energy and ensure the security of the nation’s energy supply through the expansion of renewable energy sources, like solar, wind and geothermal, for the generation of power. Recently, along the frontier of the Tadjourah region (Northern region of country), in the Arta region (located about 25 miles west of the city), a large wind farm with a capacity of 60 MW has been built to reduce Djibouti’s dependence on heavy fuel oil for electricity generation. This wind project is also part of the country’s climate ambitions to reduce its CO₂ emissions by 40% by 2030. In 2022, different feasibility studies have been conducted in the northern region of Djibouti to produce hydrogen through water electrolysis using wind energy, allowing for a zero-carbon footprint and to enhance the electricity production [5]. The results have shown the suitability of the northern parts of Djibouti to produce green hydrogen. However, the lack of clean water resources in the region and in the country remains a real problem [6].

The country’s clean water resources have been severely reduced by successive periods of drought. With an average yearly temperature of 30 °C and 160 mm of precipitation, which can reach up to 43 °C during the hot season (April–September), Djibouti is a very arid country. The availability of water resources per capita has declined sharply in recent decades, from 1673 m³/capita/year in 1972 to 320 m³/capita/year in 2020 [7]. In order to valorize the utility of the existing water quantities in the dam, a techno-economical study of green hydrogen production from the wind energy at the Nagad and Bara Wein locations in the Arta and Dikhil regions, respectively, is introduced considering the different electrolyzer technologies (alkaline and PEM). In the paper, an impartial and comprehensive analysis of the different technologies of electrolysis for both sites is demonstrated without an investigation of the treatment cost of the water in the dam, which will be the subject of a separate study.

1.1. Problem Formulation

As described in Figure 1, the Republic of Djibouti is located halfway between the equator and the Tropic of Cancer, between latitudes 10°9′ and 12°7′ N and longitudes 41°8′ and 43°4′ E. Bordered by Ethiopia along the north, west and south-west, and to the south-east by Somalia (250 km of land borders), Djibouti has about 370 km of coast, from Ras Doumeira in the north to Loyada in the south. The total area is about 23,000 km². The Foehn effect of the winds—which, in summer, descend from the Ethiopian highlands towards the sea—make Djibouti one of the hottest regions in the world. Its climate is tropical arid, characterized by low (average annual rainfall of 132 mm) and irregular (23 days per year) rainfall that can sometimes cause catastrophic floods. Djibouti has a cold season, from October to March, and a hot season, from April to September, marked by the dry and burning sandy wind of the Khamsin from the northwest or the Sabo from the southwest. The average daytime temperatures range between 17 °C and 42 °C, with annual averages between 25 °C and 35 °C. The energy sector plays an essential role in the national economy. Djibouti’s electricity production is 568 GWh, with an almost equal consumption of electricity.

Djibouti’s electricity resources are not very diversified, relying solely on two fossil-fired power plants that account for 100% of the country’s electricity generation, for a contribution of only providing, on average, 20% of the country’s electricity needs. Since 2011, the remaining 80% has been imported from the interconnection transmission line connecting Djibouti to Ethiopia. This allows for the transmission of a maximum of 95 MW, creating a high level of energy dependence for the country. As a result, the price paid by consumers remains relatively high, depending on their consumption at the beginning of 2020. To meet the country’s energy needs and to obtain competitively priced energy, Djibouti’s 2035 strategy relies on the development of renewable energies such as geothermal, solar and wind. In order to guarantee the twin objectives of energy access and security, the implementation of new renewable energy generation on the basis of the available Djiboutian geothermal, solar and wind resources should be accounted for. In addition, stepping forward to find green and sustainable solutions through converting the captured renewable energy into the hydrogen vector can be a feasible and viable option to reduce dependence on imported fossil fuels.

1.2. Fundamental Contributions and Research Originality

In the last five years, Djibouti has experienced several tropical cyclones, such as Sagar (in 2018) and Gati (in 2020), causing severe flooding and flash floods due to heavy rains. In 2019, a 270 m long and 35 m high hydraulic dam with a total capacity of 17 mln m³ was built in the Arta region to protect the capital of Djibouti from frequent floods and recover the upstream huge quantity of water that flowed into the sea [8]. In addition to that, Bara Wein is a dry lake where a maximum amount of water accumulates during the rainy seasons. The main contributions of this paper can be summarized as:

• Estimating the green hydrogen production using wind energy in Nagad (Arta) and Bara Wein (Dikhil) owing to their proximity to the water dam and windy locations;

• Illustrating the performance of the different wind turbines accompanied with various alkaline and PEM electrolyzers;

• Studying how the environment will be ecologically impacted while producing the green hydrogen solutions;

• Analyzing the levelized cost of hydrogen production for the diverse case studies.

The rest of this paper is structured as follows: a comprehensive literature review that highlights the importance of the study, the problem formulation and fundamental contributions and novelty of the paper are presented in Section 2. In Section 3, the two studied sites, Nagad and Bara Wein, are introduced with the due theoretical backgrounds. Furthermore, in Section 4, the results of the techno-economic studies have been demonstrated and fairly analyzed. Finally, the main conclusions, recommendations and fundamental perspectives are summarized in Section 5.

2. Literature Review

Hydrogen production is considered a topic of significant interest, particularly when CO₂ emissions are reduced. The different sustainable challenges of hydrogen production, accompanied with an advanced bibliometric analysis for project classification by continent, country, topic, size and others, have been introduced in [9,10,11,12]. In Chile, the techno-economic viability and the CO₂ emission reduction for producing green hydrogen from solar/wind energy have been assessed [13]. The competitivity of H₂ production has been indicated to be between 2.09 $/kWh and 3.28 $/kWh with adequate reduced CO₂ emissions between 1.06 and 1.57 tons CO₂ e/tons H₂. In China, Yang et al. studied the capacity optimization and presented economic analysis for generating Hydrogen via PV panels [14]. From the results, the attained LCOE was 0.03 $/kWh with a levelized cost of hydrogen (LCOH) of 2.9 $/kg in an acceptable payback period of about 11 years.

Kudria et al. [15] investigated the assessment of wind energy potential for hydrogen production in Ukraine. The obtained average potential of wind energy and the annual production of hydrogen were 688 GW and 43 mln tons, respectively. Karayel et al. [16] illustrated using on/offshore wind-based hydrogen production in Turkey using a PEM electrolyzer for producing 248.56 mln tons. In Iraq, Hasan and Genç [17] analyzed the techno-economic feasibility of a hybrid solar/wind system to produce hydrogen. According to [17], Basrah city showed an adequate potential, with an annual production value of hydrogen of 49,150 m³ H₂. In addition, Hassan et al. [18] provided a techno-economical study for large-scale green hydrogen production using wind/solar energies with alkaline water electrolysis. With a 1.5 MW wind turbine and a 2 MW solar plant, the hydrogen production rates have been estimated as (11,963 kg/year at 8.87 $/kg) and (94 432 kg/year at 6.33 $/kg), respectively. In Egypt, Al-Orabi et al. [19] employed the HOMER^TM software for demonstrating the green hydrogen production techno-economic feasibility of integrating the solar and/or wind technologies for three different sites. For the wind scenario, the levelized cost of electricity (LCOE) attained a value between 0.308–0.353 $/kWh, with the cost of hydrogen being up to approximately 4.13 $/kg. Moreover, Nasser et al. [20] evaluated the performance of wind/PV hybrid energies for generating and storing green hydrogen in Egypt. The study focused on energy, exergy, ecology and economics. The annual electricity and hydrogen production were 108.4 MWh and 1.912 tons, respectively, where the electrolyzer consumption and the relevant oxygen generated were approximately 97.4 MWh and 16.493 tons. With a payback period of up to 13.85 years, the CO₂ emission saving reached 689.4 tons. In addition, Ahshan et al. [21] demonstrated the significant techno-economical potential of Wind-H₂ production considering a comprehensive 18-location study in Sultanate of Oman. The LCOE range was estimated to be between 0.0349 and 0.0801 $/kWh with and without degradation. The LCOH was calculated to be between 3.37 and 6.15 $/kg.

With the electricity generated by renewable energies, high carbon emission can be greatly reduced and has become an important topic in different countries [22,23,24,25,26]. Koholé et al. [27] assessed the wind energy potential and the hydrogen production required to satisfy the load demand in Cameroon. The maximum CO₂ emission minimization value reached 641.60 tons with the GE 1.5SL wind turbine in the Kousseri region. The effect of the cost of the hydrogen from the solar energy and the environmental benefits have been investigated in Algeria [28]. Renewable hydrogen production with the environmental issue is playing a key role for African traditional cooking systems [25]. In Afghanistan, Almutairi et al. [29] carried out a techno-economic and carbon footprint study for hydrogen production using 22 turbines in the power range of 600 kW–2.3 MW. The results have shown the excellent wind potential in the Badakhshan region in terms of the LCOH and the carbon footprint. According to the performance of the studied wind turbines, the Gamesa G80 wind turbine provided the lowest LCOH value of 3.887 $/kg with a CO₂ emission reduction of 1518.41 tons. Rezaei et al. [30] introduced the economic aspect of hydrogen production to estimate the LCOH, the energy efficiency and the payback period in Fayzabad, Afghanistan. When utilizing the WES 100 GENERAL wind turbine, the results indicated that the cost of renewable electricity, the amount of CO₂ emissions and the LCOH attained 0.063 $/kWh, 72.19 tons and 2.118 $/kg, respectively. In addition, the payback period was less than 5 years.

Furthermore, a vast array of literature research has been published across the world in the domain of the feasibility of hydrogen generation through electrolysis, as well as the technical and economic analysis of wind energy generation. In Table 1, it is observed that the countries that manage the cost of generation and the storage of hydrogen will have a considerable advantage. The cost of renewable hydrogen is 5.30 $/kg to 5.80 $/kg in Pakistan using Polymer Electrolyte Membrane (PEM) electrolysis [31]. A techno-economic study of hydrogen production has been conducted in Algeria using hybrid CSP-electrolysis [28]. The results have shown that the direct normal solar irradiance and/or quality of the measured wind data together with the electrolysis technology play an important factor in hydrogen cost production [32,33]. In [34], electrification in Egypt has been investigated based on the affordability of using the hybrid solar PV/Wind/Fuel Cell combination for hydrogen production with a LCOE of about 0.47 $/kWh. The ability to use hydrogen production for energy storage in Benin has been introduced in [35], with a LCOH of about 13.29 €/kg. The techno-economic viability based on green hydrogen production from solar/wind-based energy using an alkaline electrolyzer in different countries has been illustrated [36,37,38]. In [39], hydrogen production from solar and wind energy using different technologies of water electrolyzers and their working principles has been comprehensively reviewed.

The Alkaline electrolyzer has been presented as the most mature and stable technology, with cheap components that provide low capital cost while, the PEM is famous for its compact and simple design, high speed response and startup, in addition to its high hydrogen purity output. However, the main disadvantage of the PEM technology is its high membrane cost with low durability. Javaid et al. [40] evaluated the feasibility of hydrogen production using a PEM electrolyzer, extracting it from wind energy in a suburban environment through artificial intelligence/machine leaning algorithms. The PEM electrolyzer enabled splitting the water into hydrogen and oxygen. The long short-term memory (LSTM) model provided an average daily hydrogen production of 6.76 kg/day at the studied site. The different types of electrolyzers used to analyze the LCOH using wind or solar power has been introduced in [41]. The PEM and AEM technologies have been compared in terms of their environmental impacts while producing hydrogen from wind energy systems in the Netherlands [42].

After reviewing the references in Table 1, it has been concluded that none of the previous research has integrated CO₂ emissions reduction with green hydrogen production. The goal of this paper is to investigate the production of green hydrogen produced from wind energy in the new location of the Republic of Djibouti using three wind turbines and their CO₂ reduction emission. The study also presents comparisons of alkaline and PEM electrolyzers to determine the LCOH and the produced hydrogen quantity.

3. Sites Description and Theoretical Background

3.1. Site Description

It is important to map and collect the country’s climate data in order to develop renewable energy. Over the past five years, several projects have been conducted on the techno-economic analysis and energy potential of renewable energy systems to meet the needs of the population [43,44,45,46,47]. Therefore, in this paper, the choice of the sites of Nagad and Bara Wein is in favor of their wind characteristics [47]. For the first time in these regions, an investigation will be conducted to study the techno-economic analysis and feasibility of green hydrogen production using the turbines available in the Djiboutian market. Nagad is located in the region of Arta, a city in the southeast of the Republic of Djibouti. Nagad lies between latitudes 11°31′ N and longitudes 43°07′ E. Bara Wein is located between latitudes 11°24′ N and longitudes 42°60′ E. The meteorological data were collected and analyzed for a period of five years, between 2015 and 2019, using Vantage Pro2 equipment. It includes a mast, an anemometer, a wind vane, a thermometer and a barometer. The equipment was installed in July 2014. The daily average wind speed, wind directions and temperatures were measured in 10-min time intervals, at a height of 10 m. For Bara Wein, the wind speeds were collected at a 20 m height for the period of one year between January 2015 and December 2015. The flowchart diagram of the proposed methodology is illustrated in Figure 2; this process gives the stages to assess the economic, technical and environmental investigation of the green hydrogen production produced with the wind power available in the sites.

3.2. Theoretical Background

Table 2 presents a summary of the common equations and definitions used in the wind resource assessment and energy potential investigations for hydrogen production and cost assessment. In Weibull probability and cumulative distribution functions, the energy pattern factor method (EPFM), the extrapolation of wind speed parameters at different hub heights is used for wind assessments. In addition, other equations are performed to investigate the performance of wind turbines, these include the capacity factor, the energy output, the levelized cost of electricity and the carbon emission reduction in the studied locations. The economic effects and amount of hydrogen production from the wind turbines are estimated using the energy consumption and the rectifier efficiency for the selected electrolyzer.

4. Results and Discussion

The analysis of the wind data and Weibull parameters calculated using Equations (1)–(8) generally indicates that the average wind speeds are between 3.5 m/s and 10 m/s (as depicted in Table A1). A study compared five numerical methods for estimating the Weibull parameters and concluded that the EPFM is the most accurate method for Djibouti [47]. For both locations, the two parameters of Weibull are estimated using the EPFM method using the Equations (5) and (6).

For the Nagad site, at 80 m, the highest mean wind speeds values were 7.61 m/s (with c value of 10.85 m/s) and 7.63 m/s (c value of 10.87 m/s), in July and August, respectively. At the same height, in the hot season, the mean values of the k parameters were 2.38 in July and 2.39 in August. For the Bara Wein site, the opposite behavior to Nagad was obtained for the mean wind speed and Weibull parameters values. In the hot season, the values of the mean wind speed are the lowest. At 80 m, the highest mean wind speed was obtained in the cool months, which are March-April and October–December, with a maximum value of the mean wind speed of 9.13 m/s in April, while the c and k values were 14.71 m/s and 4.77. For both sites, similar results and an identical profile of the values obtained for the mean wind speed and the Weibull parameters were extrapolated for the different heights.

Table 3 depicts the characteristics of the most commercial horizontal wind turbines with three bladed rotors in Djibouti.

Technical data are important for efficiently evaluating the performance of wind turbines. The capacity factor, the energy output and the cost assessment for the selected sites can be estimated using Equations (10)–(14). The power curves of the three commercialized and selected horizontal wind turbines are plotted in Figure 3 [53].

For the cost assessment of wind energy, some crucial factors must be taken into account, such as the investment cost, the interest and inflation rates, the lifetime of wind turbines, the maintenance/repair cost and the scrap value. In this paper, according to the cost assessment, these factors are estimated as:

• Investment cost = 20% [53,54];

• Interest rate (r) = 11.2% [52];

• Inflation rate (i) = 2% [52];

• lifetime of turbine (lt) = 20 years [53,54];

• Operation, maintenance and repair cost (C_omr) = 25% [53,54];

• Scrap value (S) = 10 [53,54].

Figure 4 presents the estimated monthly capacity factor and energy output for the De Wind D6 and Nordex N90 wind turbines. It is noted that the latter parameters were very high compared to the Vestas V44 turbine for both sites. For Nagad, the maximum capacity factor and energy output values of 48.28% and 448.97 MWh were obtained for the De Wind D6 turbine, while values of 48.26% and 825.85 MWh were obtained for the Nordex turbine in August. In Bara Wein, the maximum capacity factor and energy output values of 75.30% and 700.31 MWh were obtained in March for the De Wind D6 turbine, while values of 76.70% and 1312.6 MWh were obtained for the Nordex turbine for the same month. Therefore, the De Wind D6 and Nordex turbines are the most suitable wind machines for estimating the cost assessment for the studied sites.

These results were predictable because the rated operating power of Vestas V44 is lower than the PeR power of the other turbines. The calculations of the capacity factor and energy output in Figure 4 confirm the results obtained in Figure 5 and show no change in the monthly profile of the monthly calculated value of the LCOE using Equations (13) and (14). To alleviate the budgetary constraints of households in Djibouti, it is important to reduce their energy bills, which, at present, remain the most expensive in East Africa. However, if we consider only electricity, the building sector represents by far the largest share, making up about 90% of the country’s consumption. The measurements carried out by the Djibouti’s Energy Management Agency in a number of public buildings (government offices and hospitals) show that air-conditioning accounts for a large proportion of electricity consumption. Household electricity demand is still dominated by cooling needs, i.e., air conditioning and ventilation, which together account for 62% of household electricity consumption, and it is especially high during the summer.

Figure 5 shows a comparative analysis of the monthly levelized cost of electricity for each turbine obtained by dividing the PVC cost by the total energy output. The highest cost of electricity LCOE calculated is obtained for Vestas V44 (in the cold season) and the lowest value (in the hot season) is obtained for Nordex and De Wind D6. Nordex had the minimum value of LCOE (0.0035 $/kWh) at Nagad in July and August, while the corresponding minimum value of LCOE cost was 0.0021 $/kWh at Bara Wein in March. Comparing the LCOE values obtained by all of the turbines indicates that the estimated tariffs are lower than the local cost price of electricity, which is around 0.32 $/kWh in Djibouti. These sites have an adequate potential for wind energy deployment and the proposed turbines are the most suitable for hydrogen production.

In this study, the cost assessment and production of hydrogen are performed using two electrolyzer technologies: alkaline and polymer electrolyte membrane. Several studies regarding the price and technical data of each technology of electrolyzer have been broadly described [55,56,57,58,59]. In addition to that, the impacts of the environmental benefits are analyzed and calculated using Equation (17), which are plotted in Figure 6. The maximum values of the monthly average of the CO₂ emission reduction were 363.58 tons for the Nordex wind turbine (in March) and 228.76 tons (in August) for Bara Wein and Nagad, respectively. Subsequently, the proposed wind system is cost-effective, eco-friendly and viable for hydrogen production in the studied locations.

In this case, Equation (15) is used to determine the mass of the hydrogen produced by the wind turbine for each electrolyzer technology. The energy consumption and the rectifier efficiency for alkaline are 42 kWh/kg and 0.95, with the unit cost and the efficiency of the electrolyzer being 1300 €/kW and 0.6, respectively. For the PEM electrolyzer, the energy consumption and the rectifier efficiency are 60 kWh/kg and 0.65, with the unit cost and the efficiency of the electrolyzer being equal to 2000 €/kW and 0.48, respectively. The operation life of the two electrolyzers is assumed to be 15 years. Figure 7 describes the comparison of the amount of hydrogen produced by the three turbines for Nagad and Bara Wein. It appears that the hydrogen production is higher for the alkaline electrolyzer than the PEM, and the size of the turbine plays a crucial role (as shown in Table A2). Bara Wein is windier than Nagad, which suggests good hydrogen production for all of the turbines, with values ranging between 29.68 tons H₂ for the Nordex turbine and 15.84 tons H₂ for the De Wind D6 wind turbine. However, for Nagad, using alkaline, the maximum values of the mass of hydrogen are 18.68 and 10.15 tons H₂ for the Nordex and De Wind D6, respectively.

The monthly cost of hydrogen production variations (using alkaline and PEM) depending on the hub height of the turbines for Nagad and Bara Wein according to Equation (16) is shown in Figure 8. The amount of hydrogen produced from the wind turbine reflects and depends on the cost of the hydrogen in the given site. The results show that the minimum LCOH with alkaline was obtained at 80 m (i.e Nordex), with values of 11.48 $/kg (in March) and 18.25 $/kg (in August) for Bara Wein and Nagad, respectively. On the other hand, the results from the PEM electrolyzer illustrate the highest cost of hydrogen, which is caused by the lowest amount of hydrogen produced. Moreover, the nature and the economic indicators of the electrolyzers and turbines strongly impact the hydrogen production system and its cost analysis.

The period from October to March is the cold season. The hot season starts from April and ends in September. The average contribution of the wind speed, the quantity of the hydrogen, LCOE and LCOH, as well as the CO₂ reduction, are obtained for both seasons. The radar charts shown in Figure 9 take into account the different previously calculated parameters normalized between 0 and 1. The normalized value of each parameter for both sites and different case studies are defined by dividing the parameter’s season by the annual sums.

From Figure 9, in the Nagad site, when using the alkaline electrolyzer for hydrogen generation, the hot season wind speed is higher than the cold season. In the former, the quantity of hydrogen is higher than the cold season considering the seasonal 6-month duration. In addition, higher CO₂ reduction is witnessed in the hot season than in the cold season. Concerning the LCOE and LCOH, better results in terms of the economic feasibility are attained in the cold season in the case of using Vestas V44 and Nordex N90. However, the use of the De wind D6 turbine becomes more economic in the hot season. In contrast to Nagad site studied, in the Bara Wein site, using the alkaline electrolyzer, the different turbines provide a higher wind speed, quantity of hydrogen and CO₂ reduction in the cold season than the hot season. Therefore, the LCOH and LCOE show better results in the cold season. To conclude, the wind speed in Bara Wein is much higher than its corresponding monthly values in the cold season than the Nagad site. In the latter site, the wind speed slightly exceeds its corresponding value in the Bara Wein location in hot seasons.

5. Conclusions, Recommendations and Perspectives

In the absence of oil resources or proven gas deposits, the Republic of Djibouti imports 85% of its energy in the form of oil and covers only 15% of its energy needs in the form of wood and coal. Djibouti’s electricity production relies on fossil fuel-based thermal power plants and generators. This study extends the integration of wind energy development in two new sites (Nagad and Bara Wein) in order to produce green hydrogen while reducing greenhouse gas emissions.

The results of the proposed wind energy system showed that Nagad and Bara Wein are suitable for hydrogen production using Nordex N90 compared to the De Wind D6 and Vestas wind turbines. With monthly average velocities above 7 m/s, the sites are adequately classified as having potential for wind energy development. The maximum monthly wind power generation is estimated to be about 825.85 MWh (with capacity factor of 48.26%) and 1312.6 MWh (with capacity factor of 76.70%) for Nagad and Bara Wein, respectively. Nordex gives a cheaper cost value of 0.0035 $/kWh and 0.0021 $/kWh in Nagad and Bara Wein, respectively. This 100-times cheaper price reduces the country’s energy dependence on oil, the price and supply of which are fluctuating. The monthly CO₂ emissions avoided by the Nordex N90 wind turbine amount to 363.58 and 228.76 tons for Bara Wein and Nagad, respectively. According to the performance of the alkaline electrolyzer and the Nordex turbine, the amount of hydrogen and the LCOH costs are estimated at 29.68 tons H₂ and 11.48 $/kg H₂ for Bara Wein, while for Nagad, the values of 18.68 tons H₂ and 18.25 $/kg H₂ are obtained. This economic analysis study provides an important database and has allowed the identification of two new potential sites for hydrogen production using wind energy. It has also shown interesting and promising results for the development of wind farms in the Republic of Djibouti and countries with the same climate conditions.

In the forthcoming research studies, the authors will focus on studying the techno-economic feasibility of producing green hydrogen using the Amitié dam water after treatment. In addition, the integration of solar as an alternative energy resource or as a hybridized source together with the existing solutions is worthy research for both on-grid and off-grid studies. The hydrogen extracted from solar, wind or hybridized sources can store excess green energy as batteries during their peak cycles. This would minimize the intermittency of renewable resources that cannot produce energy continuously. Therefore, a viable, eco-friendly, attractive and permanent electricity supply for the networks will be guaranteed.

Acknowledgments

This study is financially supported by the Centre d’Excellence Africain en Logistique et Transport (CEALT), Université de Djibouti. The researchers are thankful to the National Meteorological Agency of Djibouti (ANM) for providing the meteorological and economic data used in this research.

Conflicts of Interest

The authors declare no conflict of interest.

Nomenclature

$\alpha$	Wind shear coefficient
$\Gamma$	Gamma function
${\tau }_{\mathrm{a}}$	Efficiency of electrolyzer
${\tau }_{\mathrm{conv}}$	Conversion efficiency
$c$	Scale parameter of Weibull distribution
${\mathrm{c}}_0$	Scale parameter at ${\mathrm{h}}_0$
${\mathrm{C}}_{\mathrm{a}}$	Capital cost of the electrolyzers
${\mathrm{C}}_{\mathrm{b}}$	Cost of wind electricity
${\mathrm{C}}_{\mathrm{f}}$	Capacity factor
$C_h\left(\mathrm{h}\right)$	Scale parameter at extrapolated height
Comr	Operation, maintenance and repair cost
Cu	Unit cost of wind energy
${\mathrm{E}}_{\mathrm{a}}$	Electrolyzer energy consumption
${\mathrm{E}}_{\mathrm{o}}$	Energy output
${\mathrm{E}}_{\mathrm{pfm}}$	Energy pattern factor
$f\left(v\right)$	Probability density function (PDF)
$F\left(v\right)$	Cumulative distribution function (CDF)
h	Extrapolated height
${\mathrm{h}}_0$	Initial height
I	Inflation rate
$k$	Shape parameter of Weibull distribution
${\mathrm{k}}_0$	Shape parameter at ${\mathrm{h}}_0$
$k_h\left(\mathrm{h}\right)$	Shape parameter at extrapolated height
Lt	Lifetime of turbine
$mln$	Million
${\mathrm{M}}_{\mathrm{hyd}}$	Amount of hydrogen produced
n	Exponent
${\mathrm{P}}_{ave}$	Average power output
${\mathrm{P}}_{\mathrm{e}\mathrm{R}}$	Rated electrical power
r	Interest rate
S	Scrap value
t	Time
T	The operation life of the electrolyzer
$v$	Wind speed
${\mathrm{v}}_0$	Wind speed at ${\mathrm{h}}_0$
$v_c$	Cut-in wind speeds
$v_f$	Cut-off wind speeds
$v_r$	Rated wind speeds
$\stackrel{-}{v}$	Arithmetic wind speed mean

Footnotes

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Figures and Tables

Figure 1. (a) Map of the Republic of Djibouti and studied sites; (b) Monthly hourly mean temperature contour map; (c) Monthly mean humidity variation.

Figure 2. Proposed flow chart of the methodology.

Figure 3. Power curves of studied wind turbines.

Figure 4. Monthly mean capacity factor and energy output variations for (a) Vestas V44 (b) De Wind D6 and (c) Nordex N90 for the studied locations.

Figure 5. Monthly mean LCOE values for (a) Nagad and (b) Bara Wein.

Figure 6. CO2 emission reduction obtained from the wind turbines for (a) Nagad and (b) Bara Wein.

Figure 7. Monthly hydrogen production variations using alkaline and PEM electrolyzers for (a) Vestas V44 (b) De Wind D6 and (c) Nordex N90.

Figure 8. Monthly cost of hydrogen production for Bara Wein and Nagad sites.

Figure 9. Impartial analyses for hydrogen production in Nagad and Bara Wein sites in hot/cold seasons.

Table 1

Recent research’s comparison in the domain of hydrogen production.

References	[32]	[33]	[31]	[34]	[35]	[36]	[37]	[38]	In this Study
Country	India	Djibouti	Pakistan	Egypt	Benin	Poland	South China	West China	Djibouti
Assessment of wind speed	✔	✔	✔	✔		✔		✔	✔
Wind power investigation	✔	✔	✔	✔		✔		✔	✔
Techno-economic analysis	✔		✔	✔	✔	✔	✔	✔	✔
Electrolyzer Alkaline						**	✔	**	✔
Electrolyzer PEM			✔	✔	✔		✔		✔
Hydrogen			✔	✔	✔	✔	✔	✔	✔
Hydrogen cost			✔	✔	✔		✔	✔	✔
Carbon Dioxide emission reduction									✔

** generic electrolyzer, ✔ realized study in each reference.

Table 2

Summary of the common equations used in the wind resources and energy investigations.

Performances Measures	Equations	No.
Probability density function [28,48]	$\mathrm{f}\left(\mathrm{v}\right)=\left(\mathrm{k}/\mathrm{c}\right){\left(\mathrm{v}\right)/\mathrm{c}}^{\mathrm{k}-1}\exp \left[-{\left(\mathrm{v}/\mathrm{c}\right)}^{\mathrm{k}}\right]$	(1)
Cumulative distribution function [28,32]	$\mathrm{F}\left(\mathrm{v}\right)=1-\exp \left[-{\left(\mathrm{v}/\mathrm{c}\right)}^{\mathrm{k}}\right]$	(2)
Energy pattern factor [33]	${\mathrm{E}}_{\mathrm{pfm}}=\stackrel{-}{{\mathrm{v}}^3}/{\overline{\mathrm{v}}}^3=1/\mathrm{n}{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}}{{\mathrm{v}}_{\mathrm{i}}}^3/{\left(1/\mathrm{n}{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{n}}}{\mathrm{v}}_{\mathrm{i}}\right)}^3$	(3)
Vertical extrapolation of wind speed [32]	$\mathrm{v}\left(\mathrm{h}\right)={\mathrm{v}}_0{\left(\mathrm{h}/{\mathrm{h}}_0\right)}^{\alpha }$ with $\alpha$= 0.14	(4)
Weibull shape parameter [31,33]	$\mathrm{k}=1+3.69/{\left({\mathrm{E}}_{\mathrm{pf}}\right)}^2$	(5)
Weibull scale parameter [31,33]	$\mathrm{c}=\overline{\mathrm{v}}/\Gamma \left(1+1/\mathrm{k}\right)$	(6)
scale parameter at extrapolated height [31,34]	$c_h\left(\mathrm{h}\right)={\mathrm{c}}_0\times {\left(\mathrm{h}/{\mathrm{h}}_0\right)}^{\mathrm{n}}$	(7)
shape parameter at extrapolated height [34,35]	$k_h\left(\mathrm{h}\right)={\mathrm{k}}_0\times \left[1-0.088\mathrm{l}\mathrm{n}\left(\mathrm{h}/{\mathrm{h}}_0\right)\right]/\left[1-0.088\ln \left(\mathrm{h}/10\right)\right]$	(8)
Exponent [31]	$\mathrm{n}=\left[0.37-0.088\mathrm{l}\mathrm{n}\left({\mathrm{c}}_0\right)\right]/\left[1-0.088\ln \left(\mathrm{h}/10\right)\right]$	(9)
Average power output [36,37]	${\mathrm{P}}_{ave}={\mathrm{P}}_{\mathrm{e}\mathrm{R}}\left\{{\mathrm{e}}^{{-\left({\mathrm{v}}_{\mathrm{c}}/\mathrm{c}\right)}^{\mathrm{k}}}-{\mathrm{e}}^{{-\left({\mathrm{v}}_{\mathrm{r}}/\mathrm{c}\right)}^{\mathrm{k}}}/{\left({\mathrm{v}}_{\mathrm{r}}/\mathrm{c}\right)}^{\mathrm{k}}-{\left({\mathrm{v}}_{\mathrm{c}}/\mathrm{c}\right)}^{\mathrm{k}}-{\mathrm{e}}^{{-\left({\mathrm{v}}_{\mathrm{f}}/\mathrm{c}\right)}^{\mathrm{k}}}\right\}$	(10)
Capacity factor [36,37]	${\mathrm{C}}_{\mathrm{f}}={\mathrm{P}}_{\mathrm{a}\mathrm{v}\mathrm{e}}/{\mathrm{P}}_{\mathrm{e}\mathrm{R}}$	(11)
Energy output [38]	${\mathrm{E}}_{\mathrm{o}}={\mathrm{C}}_{\mathrm{f}}\times \mathrm{P}\mathrm{e}\mathrm{R}\times \mathrm{t}$	(12)
Present Value Cost [38]	$\mathrm{P}\mathrm{V}\mathrm{C}=\mathrm{I}+{\mathrm{C}}_{\mathrm{o}\mathrm{m}\mathrm{r}}\left[1+\mathrm{i}/\mathrm{r}-\mathrm{i}\right]\times \left[1-{\left(1+\mathrm{i}/1+\mathrm{r}\right)}^{\mathrm{t}}\right]-\mathrm{S}{\left(1+\mathrm{i}/1+\mathrm{r}\right)}^{\mathrm{t}}$	(13)
Levelized cost of electricity [38,49,50,51]	$\mathrm{L}\mathrm{C}\mathrm{O}\mathrm{E}=\mathrm{P}\mathrm{V}\mathrm{C}/{\mathrm{E}}_{\mathrm{o}}$	(14)
Amount of hydrogen produced by wind turbine [31,35,37]	${\mathrm{M}}_{\mathrm{h}\mathrm{y}\mathrm{d}}={\left({\mathrm{E}}_{\mathrm{o}}/{\mathrm{E}}_{\mathrm{a}}\right)·\tau }_{\mathrm{conv}}$	(15)
Levelized cost of hydrogen [31,35,37,38]	$\mathrm{LCOH}={\mathrm{C}}_{\mathrm{a}}+{\mathrm{C}}_{\mathrm{b}}/{\mathrm{M}}_{\mathrm{hyd}}·\mathrm{T}$  with ${\mathrm{C}}_{\mathrm{a}}={\mathrm{C}}_{\mathrm{u}}·\left({\mathrm{M}}_{\mathrm{hyd}}\times {\mathrm{E}}_{\mathrm{a}}/8760\times {\mathrm{C}}_{\mathrm{f}}\times {\tau }_{\mathrm{a}}\right)$  and ${\mathrm{C}}_b=\mathrm{LCOE}\times {\sum }_{\mathrm{i}=1}^{\mathrm{t}}{\mathrm{E}}_{\mathrm{o}}/\mathrm{T}$	(16)
Carbon dioxide emission reduction from wind energy [5,52]	${\mathrm{CO}}_2={\mathrm{E}}_{\mathrm{o}}\times 0.277$	(17)

Table 3

Characteristics of commercial horizontal wind turbines.

Turbine Model	Vcut-i	Vrated	Vcut-o	Hub	PeR	Lifetime	Price
	(m/s)	(m/s)	(m/s)	(m)	(kW)	(yr)	($)
Vestas V44	5	17	20	50	600	20	37,016
De Wind D6	2.8	12.5	25	65	1250	20	986,000
Nordex N90	4	13	25	80	2300	20	228,000

Appendix A

Table A1

Values of the mean wind speed and Weibull parameters at different heights for the two studied sites.

			January	February	March	April	May	June	July	August	September	October	November	December
NAGAD	h = 10 m	v (m/s)	4.7	4.00	4.00	3.38	3.39	4.00	4. 90	5.29	3.39	3.99	3.89	3.99
		c (m/s)	5.57	4.69	4.58	4.33	4.02	4.59	5.42	6.33	4.30	4.79	4.60	4.79
		k (-)	1.91	1.80	1.80	1.68	1.69	1.83	1.98	1.99	1.69	1.72	1.79	1.79
	h = 50 m	v (m/s)	5.76	5.13	5.13	4.50	4.50	5.13	7.13	7.14	4.51	5.12	5.13	5.12
		c (m/s)	7.89	7.16	7.13	6.38	6.39	7.15	9.45	9.47	6.39	7.16	7.15	7.16
		k (-)	2.12	2.02	2.02	1.90	1.90	2.02	2.27	2.27	1.91	2.02	2.02	2.03
	h = 65 m	v (m/s)	5.97	5.33	5.32	4.66	4.67	5.32	7.40	7.41	4.67	5.33	5.32	5.33
		c (m/s)	8.57	7.80	7.77	6.98	6.99	7.79	10.19	10.21	6.99	7.80	7.79	7.81
		k (-)	2.18	2.08	2.08	1.95	1.95	2.07	2.33	2.33	1.95	2.08	2.07	2.08
	h = 80 m	v (m/s)	6.15	5.48	5.47	4.80	4.80	5.48	7.61	7.63	4.81	5.48	5.47	5.49
		c (m/s)	9.18	8.38	8.35	7.53	7.54	8.37	10.85	10.87	7.54	8.38	8.37	8.39
		k (-)	2.23	2.13	2.13	1.99	1.99	2.12	2.38	2.39	2.00	2.13	2.12	2.13
BARA WEIN	h = 20 m	v (m/s)	7.04	8.05	8.06	6.87	5.46	4.77	5.22	6.00	4.27	7.31	7.06	7.35
		c (m/s)	7.88	8.94	9.50	7.65	6.16	5.39	5.90	6.78	4.82	8.10	7.87	8.17
		k (-)	3.05	3.60	3.90	3.37	2.42	2.03	2.12	2.17	1.98	3.72	3.34	3.46
	h = 50 m	v (m/s)	7.75	8.86	8.87	7.56	6.01	5.25	5.74	6.60	4.70	8.05	7.77	8.09
		c (m/s)	11.21	12.46	13.11	10.94	9.13	8.16	8.80	9.89	7.44	11.47	11.20	11.56
		k (-)	3.55	4.19	4.54	3.92	2.81	2.36	2.46	2.52	2.30	4.33	3.89	4.03
	h = 65 m	v (m/s)	7.87	9.00	9.01	7.68	6.10	5.33	5.84	6.71	4.77	8.17	7.89	8.22
		c (m/s)	12.01	13.29	13.96	11.73	9.86	8.85	9.52	10.65	8.09	12.28	12.00	12.37
		k (-)	3.65	4.30	4.66	4.03	2.89	2.43	2.53	2.59	2.37	4.45	3.99	4.14
	h = 80 m	v (m/s)	7.97	9.11	9.13	7.78	6.18	5.40	5.91	6.79	4.83	8.28	7.99	8.32
		c (m/s)	12.72	14.03	14.71	12.43	10.51	9.47	10.16	11.32	8.69	13.00	12.71	13.08
		k (-)	3.73	4.40	4.77	4.12	2.96	2.48	2.59	2.65	2.42	4.55	4.08	4.23

Table A2

Values of the M_hyd and LCOH with Alkaline and PEM electrolyzers for the two studied sites using different technologies of the wind turbines.

Sites	Electrolyzer	Wind Turbine		January	February	March	April	May	June	July	August	September	October	November	December
NAGA NAGAD	Alkaline	Vestas V44	${\mathrm{M}}_{\mathrm{hyd}}$ (tons)	1.44	1.05	1.15	0.89	0.92	1.13	2.14	2.14	0.89	1.17	1.13	1.16
			LCOH($/kg)	61.61	84.36	77	99.86	96.39	78.54	41.61	41.57	99.74	76.07	78.77	76.25
		De wind 06	${\mathrm{M}}_{\mathrm{hyd}}$ (tons)	7.82	6.10	6.71	5.53	5.73	6.54	10.14	10.15	5.54	6.76	6.53	6.76
			LCOH($/kg)	23.74	30.42	27.65	33.55	32.41	28.37	18.31	18.28	33.50	27.45	28.41	27.46
		Nordex N90	${\mathrm{M}}_{\mathrm{hyd}}$ (tons)	14.23	10.98	12.08	9.78	10.13	11.77	18.64	18.68	9.80	12.17	11.76	12.17
			LCOH($/kg)	23.96	31.05	28.26	34.84	33.66	28.96	18.29	18.25	34.79	28.01	29.00	28.02
	PEM	Vestas V44	${\mathrm{M}}_{\mathrm{hyd}}$ (tons)	0.69	0.50	0.55	0.42	0.44	0.54	1.02	1.02	0.42	0.56	0.54	0.55
			LCOH($/kg)	167.45	229.28	209.27	271.39	261.97	213.47	113.10	112.98	271.07	206.75	214.08	207.24
		De wind 06	${\mathrm{M}}_{\mathrm{hyd}}$ (tons)	3.74	2.92	3.21	2.65	2.74	3.13	4.85	4.86	2.65	3.23	3.13	3.24
			LCOH($/kg)	64.51	82.67	75.14	91.17	88.09	77.11	49.77	49.69	91.05	74.60	77.21	74.64
		Nordex N90	${\mathrm{M}}_{\mathrm{hyd}}$ (tons)	6.81	5.26	5.78	4.68	4.85	5.63	8.92	8.94	4.69	5.82	5.63	5.83
			LCOH($/kg)	65.14	84.42	76.74	94.73	91.52	78.75	49.73	49.63	94.57	76.17	78.85	76.18
BARA WEIN	Alkaline	Vestas V44	${\mathrm{M}}_{\mathrm{hyd}}$ (tons)	2.17	2.37	2.94	1.66	1.49	1.32	1.61	2.17	1.03	1.79	1.85	2.05
			LCOH($/kg)	40.92	37.48	30.19	53.49	59.30	66.98	55.01	40.93	86.05	49.62	48.03	43.22
		De wind 06	${\mathrm{M}}_{\mathrm{hyd}}$ (tons)	12.44	13.26	15.84	11.41	8.98	7.62	8.85	10.62	6.45	12.85	11.95	13.04
			LCOH($/kg)	14.92	13.99	11.72	16.26	20.67	24.36	20.97	17.48	28.76	14.44	15.53	14.24
		Nordex N90	${\mathrm{M}}_{\mathrm{hyd}}$ (tons)	23.458	24.939	29.68	21.69	16.83	14.03	16.40	19.70	11.80	24.40	22.64	24.66
			LCOH($/kg)	14.53	13.67	11.48	15.72	20.25	24.29	20.79	17.30	28.89	13.97	15.06	13.82
	PEM	Vestas V44	${\mathrm{M}}_{\mathrm{hyd}}$ (tons)	1.040	1.136	1.41	0.79	0.71	0.63	0.77	1.04	0.49	0.85	0.88	0.98
			LCOH($/kg)	111.27	101.90	82.09	145.45	161.25	182.12	149.56	111.29	233.96	134.91	130.60	117.52
		De wind 06	${\mathrm{M}}_{\mathrm{hyd}}$ (tons)	5.95	6.35	7.58	5.46	4.30	3.65	4.24	5.08	3.09	6.15	5.72	6.24
			LCOH($/kg)	40.56	38.03	31.85	44.20	56.19	66.20	56.99	47.51	78.17	39.25	42.21	38.69
		Nordex N90	${\mathrm{M}}_{\mathrm{hyd}}$ (tons)	11.23	11.94	14.21	10.38	8.06	6.72	7.85	9.43	5.65	11.69	10.84	11.81
			LCOH($/kg)	39.52	37.17	31.23	42.74	55.05	66.05	56.53	47.05	78.55	37.99	40.94	37.59

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