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Wind energy potential and micro-turbine performance analysis in Djibouti-city, Djibouti
Engineering Science and Technology, an International Journal xxx (xxxx) xxx
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Wind energy potential and micro-turbine performance analysis in Djibouti-city, Djibouti Abdoulkader Ibrahim Idriss a, Ramadan Ali Ahmed b, Abdou Idris Omar a, Rima Kassim Said b, Tahir Cetin Akinci c,⇑ a Laboratoire de Recherche en Science et Technologies Industrielles (GRE), Faculty of Engineering, Department of Electrical, and Energy Engineering, University of Djibouti, Street Djanaleh, B.P. 1904, Djibouti b Laboratoire de Recherche en Science et Technologies Industrielles (GRE), Faculty of Sciences, Department of Physics and Chemistry, University of Djibouti, Street Djanaleh, B.P. 1904, Djibouti c Department of Electrical Engineering, Istanbul Technical University, 34469 Istanbul, Turkey
a r t i c l e
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Article history: Received 5 May 2019 Revised 15 June 2019 Accepted 16 June 2019 Available online xxxx Keywords: Wind speed Weibull distribution Wind power density Wind turbine Capacity factor Urban Djibouti
a b s t r a c t Djibouti has an ambitious program to develop and expand its energy demand to satisfy local demand and increase the energy access in rural and urban areas. The renewable energy development is vital in Djibouti’s strategy including 2020’s vision. To increase investment in clean energy by reducing dependence on oil and derivative products, Djibouti want to be the first sub-Saharan African nation using 100% clean energy. This paper examines, for the first time, wind energy potential at Djibouti-city using 5-years (2014–2018) wind speed data collected at 10 m height of wind power using Weibull parameters. Such a study was not feasible before due to the lack of data in this urban area. The results showed the possibility to implement and develop urban wind energy sector in Djibouti-city for domestic applications. The statistical wind speed, the wind rose, and the power density were computed. This study is also part of the implementation project of five micro wind turbines of 1–25 kW in different height areas. The strong wind flows are Eastern and Western over the considered period. The Polaris P12-25 wind turbine provided the best capacity factor (CF = 9.629%) and energy output (AEP = 2.1 104 MWh) comparing to the others technologies because the study site having low monthly wind speeds. Finally, according to the analysis of wind power production, Djibouti-city needs to install the wind turbines with high hub height greater than 30.5 m for efficient harvesting. Ó 2019 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction Depending on oil price volatility, EDD which stands for Electricity of Djibouti has the legal monopoly of the transport and the distribution of electric energy on the whole national territory. The EDD [1] tariffs range from a social price of 0.21$/kWh to 0.52$/kWh paid by industrial customers. The cost of electricity in Djibouti is high compared to neighboring countries like Ethiopia and Kenya [1].Although Djibouti is rich by geothermal resources from renewable energy sources, it does not have hydroelectric power plants. In this sense, it is dependent on foreign energy. The demand for energy is constantly increasing in the world. In order to meet the need for energy, thermal power plants using fossil fuels are used. In order to ensure continuity in economic ⇑ Corresponding author. E-mail address: [email protected] (T.C. Akinci). Peer review under responsibility of Karabuk University.
development, the plants using renewable energy sources should be increased [2].In this sense, small wind turbines are designed and implemented to contribute to meeting the local energy needs [3–7]. The development of technology ensures the continuous improvement of wind turbine dimensions and performance. The efficiency and performance of wind turbines are increasing in proportion to the quality of energy production in the turbines [8–13]. The wind speed measured in urban centers can be affected by the positioning of buildings in the city [4,14]. In this case, the wind prediction for enterprises using small wind turbines should be done with a two-parameter Weibull probability distribution [15,16]. In this study, a five-year wind speed data of Djibouti was analyzed by micro wind turbine in the city center of Djibouti. The data obtained from the anemometer at a height of 10 m were used in the analysis. The goal of this study is to determine the economic impact of energy produced by micro wind turbines and attract the interest of investors. Moreover, this study is one of the first
https://doi.org/10.1016/j.jestch.2019.06.004 2215-0986/Ó 2019 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: A. I. Idriss, R. A. Ahmed, A. I. Omar et al., Wind energy potential and micro-turbine performance analysis in Djibouti-city, Djibouti, Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.06.004
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studies evaluating the wind potential in the Republic of Djibouti. Weibull distribution function gives good results in the analysis of variable data. This is the most efficient method for the distribution functions that best fit the measured data [17–21]. In this study, the data collected is located at different heights of five wind turbines, examining the performance of these five-year data, the average daily and monthly wind speed was considered average power output and capacity factor. 2. Site description and theoretical background The location of the weather station for the wind energy potential assessment is in Djibouti-city (11.5658 °N; 43.15435 °E; altitude 7 m). The city is situated in East Africa and has a typical tropical climate and is officially classed as a maritime desert: a climatic region that is hot and humid in winter and extremely hot and humid in the summer. In the maritime desert, the air temperature is only slightly less than those of the inland regions. However, the presence of the warm ocean in the area creates a high level of humidity. The temperatures are high at 20–30 °C during the cool season from October to April and 30–45 °C during the hot season from May to September. Detailed analysis is required for small wind projects. In this planning, the country’s altitude, meteorological characteristics, and wind speeds of past periods are the main criteria for estimating data. There are several methods used in the literature to determine the wind potential [22–27]. In this study, wind speed was evaluated on meteorological data. Table 1 shows the performancemeasure of the wind speed and definition. Wherein the Weibull probability density function (f (v)), cumulative distribution function (F (v)), power density (PD), energy density (ED), the average power output (Pe, ave), capacity factor (CF) as shown, and annual energy output (AEP). These quantities describe the performance of micro-turbine in relation to wind potential, wind characteristic in the site and energy production. In determining the potential of wind energy, the turbine should be installed in the location and wind performance [24].
Fig. 1. Statistical functions at 10 m height upon 5 years.
and scale parameters are 1.88 and 1.96 m/s, respectively. The probability density curve indicates the most frequent wind speed, which is 1.3 m/s with the probability of 42.1%. In statistical analysis, the cumulative distribution gives the possibility of being equal to or lower than the wind speed. Djibouti Republic of Djibouti as a result of the analysis made in the city’s maximum winds of 6 m/s was measured to have reached. Table 2 shows the monthly mean wind speed characteristics and wind energy potential. The results show that the values of c and k parameters, obtained are in the range from 1.462 m/s to 2.830 m/s and from 1.410 to 2.410, respectively. VF and the VE values were obtained using Equations (4) and (5). The mean wind speed value is low 1.247 m/s was in June, while February appears to have the highest mean wind speed value of 2.329 m/s. The lowest and highest VF is 0.609 m/s for June and 2.266 m/s in February, respectively. The value of wind speed denotes VE ranges from 2.223 m/s to 4.212 m/s. According to the Equations (6) and (7), the min. and max. power densities occur in June and February, respectively. The energy density gives the minimum value of 0.855 kWh/m2 in June and the maximum value of 5.2 kWh/m2 in February, respectively. To estimate the wind energy potential, Fig. 2 shows VFand VE wind speeds for the monthly periods (5-year average). The values of VF are ranged from0.6 m/s (in June) to 2.26 m/s (in February). Also, the values of VE are ranged from 2.22 m/s (in October) to 4.21 m/s (in August). These two values included in wind turbine design calculation [28,29]. Fig. 3 displays a 2-years (2015 and 2016) distribution of wind speed series, the hourly variation of mean wind speed, the wind
3. Results and discussions The wind speed data was measured at Djibouti-city weather station over a period of 5 years and was recorded every 10 min interval and was measured at 10 m. Fig. 1 shows the probability density function and cumulative distribution function. The histogram represents the measured wind speed data. The probability density function and Weibull cumulative density function are given in Eqs. (2) and (3), respectively. The values of the shape
Table 1 Summary of the performance measures equations for wind resources analysis. Performance measures
Definition
Vertical variation of wind speed Probability density function (PDF)
VðZ R Þ ¼ VðZÞ½lnðZ R =Z 0 Þ=lnðZ=Z 0 Þ
Equations
Cumulative distribution function (CDF)
FðVÞ ¼ 1 eðV =cÞ
f ðVÞ ¼ ðk=cÞðV=cÞk1 eðV=cÞ
k
(1) (2)
k
(3)
1=k
(4)
1=k
(5)
Most probable wind speed (VF)
V F ¼ cððk 1Þ=kÞ
Wind speed carrying maximum energy (VE)
V E ¼ cððk þ 2Þ=kÞ
Power density (PD) Energy density (ED) Weibull shape parameter (k) Weibull scale parameter (c) Exponent n Average power output (Pe, ave)
P D ¼ 12 qV 3m ED ¼ P D T kðhÞ ¼ k0 ½1 0:088lnðh0 =10Þ=½1 0:088lnðh=10Þ n cðhÞ ¼ c0 ðh=h0 Þ n ¼ ½0:37 0:088lnðc0 Þ=½1 0:088lnðh=10Þ n o k k k ðeðV c =cÞ eðV r =cÞ Þ=ððV r =cÞk ðV c =cÞk Þ eðV f =cÞ P e;av e ¼ P eR
(7) (8) (9) (10) (11)
Capacity factor (Cf) Annual energy output (AEP)
Cf ¼ P e;av e =P eR AEP ¼ Cf P eR t
(12) (13)
(6)
Please cite this article as: A. I. Idriss, R. A. Ahmed, A. I. Omar et al., Wind energy potential and micro-turbine performance analysis in Djibouti-city, Djibouti, Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.06.004
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A.I. Idriss et al. / Engineering Science and Technology, an International Journal xxx (xxxx) xxx Table 2 Analysis of wind speed characteristics in Djibouti over considered period. Month
Vm (m/s)
c (m/s)
k
VF (m/s)
VE (m/s)
PD (W/m2)
ED (kwh/m2)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1.710 2.329 1.801 1.541 1.339 1.247 1.669 1.782 1.375 1.688 1.839 1.502
2.135 2.830 2.180 1.877 1.612 1.462 1.598 2.348 1.716 1.660 2.297 1.840
1.802 2.410 2.290 2.095 1.710 1.410 1.966 1.47 1.668 2.207 2.060 1.527
1.363 2.266 1.697 1.377 0.964 0.609 1.113 1.081 0.991 1.263 1.664 0.917
3.230 3.636 2.868 2.585 2.536 2.736 2.283 4.212 2.752 2.223 3.194 3.183
3.063 7.738 3.578 2.241 1.470 1.188 2.848 3.466 1.592 2.946 3.809 2.075
2.279 5.200 2.662 1.614 1.094 0.855 2.119 2.579 1.146 2.192 2.743 1.544
Annual
1.652
1.963
1.885
1.276
2.953
3.001
2.169
Fig. 2. Comparing the periodic VF and VE wind speeds.
rose and the detailed monthly mean frequencies for main prevailing wind direction at 10 m hub height. Speed and direction of wind change rapidly with time. An example for a 2-years distribution of
wind speed series over the study area is shown in Fig. 3(a). Here, the wind fluctuates between 0 and 7.1 m/s. The lack of data during the data acquisition is observed for few days between the 9th and 30th are missing for September 2015 due to a technical problem of the cup-anemometer. We can see a close similarity of the wind speed for both years confirming a constancy profile of data for all considered period. These values from the distribution obtained in Fig. 3(a) are statistically analysed to generate monthly wind speed contour map obtained in Fig. 3(b). The hourly wind speed values range from 0 to 3.2 m/s. Maximum wind speed was observed from 10 h to 8 h pm. Also, the results reveal that from January to March, the wind speeds values are relatively high and till 3.2 m/s. From April to August, wind speeds are low (1–1.5 m/s). Wind speed density was high during working hours, which is crucial for microwind generators users in the urban zone. The wind rose diagram is a favourite method to represent wind speed as a function of direction. Fig. 3(c) shows a frequency rose with a relative frequency of different wind speed ranges (indicated by the color code in the legend section of the graph) of the study site upon 5 years analysis. Fig. 3(d) illustrates the detailed monthly mean
Fig. 3. (a) A two years (2015 and 2016) distribution of wind speed series, (b) Monthly hourly mean wind speeds contour map (c) Annual frequencies of wind rose and (d) Detailed monthly mean frequencies for main prevailing wind direction at the Djibouti-city at 10 m hub height.
Please cite this article as: A. I. Idriss, R. A. Ahmed, A. I. Omar et al., Wind energy potential and micro-turbine performance analysis in Djibouti-city, Djibouti, Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.06.004
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frequencies for main prevailing wind direction obtained in Fig. 3 (c). The dominant wind direction is eastern winds throughout the year (61.3%) and it reaches its minimum (10.39%) the summer periods. The second dominant sector of wind direction observed is western winds (36.05%), recorded from May to October due to relatively high levels of the Khamsin which is a hot dry wind that brings clouds of sand. Also, some south-easterly and northeasterly wind exist during the transition months of April and September with the frequency values range from 0 to 15.57%. Fig. 4 presents the monthly mean wind speed profile and the average energy density in Djibouti-city for the different heights for the five-year period. As can be observed in Fig. 4(a), throughout the whole year, the monthly average wind speed has similar distribution and varies from 1.2 m/s to 2.65 m/s for different heights, with maximum values observed in February, August, and November. The wind speed increases with height variation (H = 10, 20 and 30 m) as expressed in Equation (1) (with Zr is the reference height (10 m) and Z0 is the roughness length, which is 0.0024 m for open terrain with the smooth surface has been used in the calculation). It is also noted in the histogram plot in Fig. 4(b) the average energy density varies between 0 kWh/m2 and 7.5 kWh/m2 with maximum points obtained in February, August, and November. The PD in Djibouti-city can be included into class 1 with a value of 3 W/m2 and it is limited but it is appropriate for low potential wind turbines in urban and small communities. In a study conducted by [30,31], the Ethiopian wind energy is categorised as class 1 with the wind speed data according to class of 3.5–5.6 m/s. Djibouti-city is located in the coastal Djibouti region on the Gulf of Tadjoura, and its wind energy potential varies depending on the region. The available wind potential for Djibouti-city couldn’t be estimated due to lack of data on other areas.
of the selected wind turbines are shown in Fig. 5. The graph illustrates the power available from each wind turbine (indexed from A to E) across a range of wind speeds. The chosen horizontal wind turbines have various capacities from 1 kW to 25 kW from different manufacturers to estimate their performance in the Djibouti-city. These turbines were also considered for their reasonable cost. The estimated Capacity factor (Cf) and the annual energy output (AEP) are the performance indicator of the turbine’s generation capacity. The Eqs. (8)–(12) were usedto calculate the monthly power output from each turbine. Fig. 6(a) and (b) depict the monthly average Cf and AEP of generation for five turbines upon 5 years.
4. Wind turbines characteristics and performance Depending on the site’s wind speed profile, we selected five commercial wind turbines with technical parameters (cut-in (Vc), rated (Vr) and cut-off (Vf) wind speeds, hub height, and the rated electrical power (PeR)) are given in Table 3 and the power curves
Fig. 5. Power curves of selected five turbines (indexed from A to E).
Fig. 4. Plots comparing monthly average (a) wind speeds variations and (b) energy density at the different hub heights (10, 20, and 30 m) of whole-year data series covering 2014–2018.
Table 3 Technical details of the wind turbine model from different manufacturers. Turbine index
Wind turbine Model
Vc (m/s)
Vf (m/s)
Vr (m/s)
Hub height (m)
PeR (kW)
A B C D E
AEOLOS-1 AEOLOS V-5 HAWT-5 HAWT-11 POLARIS P12-25
1.5 2.5 3 3 2.7
50 52.5 60 25 25
10 10 11 10 10
2.8 5.3 12 18 30.5
1 5 5 11 25
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Fig. 6. Monthly mean of (a) Cf and (b) AEP of five wind turbines using mean wind speed at 10 m hub height of whole-year data series covering 2014–2018.
In August, the values of Cf are 4.324, 5.397, 3.337, 5.509 and 9.629 respectively while been lowest in July and October. Turbines E and D had the highest capacity factors. The highest annual power energy output is given by turbine E with a value of 2.1 104 MWh, whereas the lowest value is given by turbine A with a value of 0.14 104 MWh. The results are affected by the wind speed’s profile of the site and characteristic of each wind turbine. The highest capacity factor of the wind generators will give the highest value of annual power energy production. Although, the Cf was relatively low (10%) in the study area.
5. Conclusion In this study, the first wind energy potential and micro-turbine performance analysis were carried out in Djibouti. Five years wind speed data were subjected to Weibull k and c parameters and other statistical analyses. The most important outcomes are summarized as follows: 1. The monthly mean wind speed is recorded as 1.247 m/s and 2.329 m/s in June and February, respectively. The monthly mean energy and power densities values are respectively computed as 1.188 W/m2, 0.855 kWh/m2 in June and 7.7738 W/m2, 2 kWh/m2 in February. 2. k ranges from 1.41 to 2.41 while c is ranged from 1.462 m/s to 2.83 m/s. 3. The measured time history of wind speed confirming a constancy profile of data for all considered period. The monthly wind power class in Djibouti-city is class 1, but, this potential is appropriate for low capacity wind turbines in urban and small communities. 4. The prevailing wind direction is eastern winds (61.3%) and the second prevailing sector of wind direction observed is western winds (36.05%) upon 5 years over the study area. 5. The performance of five wind turbines were analyzed and the results illustrate that Polaris P12-25 turbine yielded the highest capacity factor (Cf = 9.629%) and energy output (AEP = 2.1 104 MWh) in August. Finally, the results show that Djibouti-city has a potential to develop the small-scale applications wind turbines and to facilitate further actions for similar research projects in other locations, an extensive study on power feasibility should be done in larger geographical areas of Djibouti-city to choose the best possible options for different scale wind power generation. This study is only the first phase of a project to set up a power system with microturbines for an urban community not connected to the electricity grid.
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Full Length Article
Wind energy potential and micro-turbine performance analysis in Djibouti-city, Djibouti Abdoulkader Ibrahim Idriss a, Ramadan Ali Ahmed b, Abdou Idris Omar a, Rima Kassim Said b, Tahir Cetin Akinci c,⇑ a Laboratoire de Recherche en Science et Technologies Industrielles (GRE), Faculty of Engineering, Department of Electrical, and Energy Engineering, University of Djibouti, Street Djanaleh, B.P. 1904, Djibouti b Laboratoire de Recherche en Science et Technologies Industrielles (GRE), Faculty of Sciences, Department of Physics and Chemistry, University of Djibouti, Street Djanaleh, B.P. 1904, Djibouti c Department of Electrical Engineering, Istanbul Technical University, 34469 Istanbul, Turkey
a r t i c l e
i n f o
Article history: Received 5 May 2019 Revised 15 June 2019 Accepted 16 June 2019 Available online xxxx Keywords: Wind speed Weibull distribution Wind power density Wind turbine Capacity factor Urban Djibouti
a b s t r a c t Djibouti has an ambitious program to develop and expand its energy demand to satisfy local demand and increase the energy access in rural and urban areas. The renewable energy development is vital in Djibouti’s strategy including 2020’s vision. To increase investment in clean energy by reducing dependence on oil and derivative products, Djibouti want to be the first sub-Saharan African nation using 100% clean energy. This paper examines, for the first time, wind energy potential at Djibouti-city using 5-years (2014–2018) wind speed data collected at 10 m height of wind power using Weibull parameters. Such a study was not feasible before due to the lack of data in this urban area. The results showed the possibility to implement and develop urban wind energy sector in Djibouti-city for domestic applications. The statistical wind speed, the wind rose, and the power density were computed. This study is also part of the implementation project of five micro wind turbines of 1–25 kW in different height areas. The strong wind flows are Eastern and Western over the considered period. The Polaris P12-25 wind turbine provided the best capacity factor (CF = 9.629%) and energy output (AEP = 2.1 104 MWh) comparing to the others technologies because the study site having low monthly wind speeds. Finally, according to the analysis of wind power production, Djibouti-city needs to install the wind turbines with high hub height greater than 30.5 m for efficient harvesting. Ó 2019 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction Depending on oil price volatility, EDD which stands for Electricity of Djibouti has the legal monopoly of the transport and the distribution of electric energy on the whole national territory. The EDD [1] tariffs range from a social price of 0.21$/kWh to 0.52$/kWh paid by industrial customers. The cost of electricity in Djibouti is high compared to neighboring countries like Ethiopia and Kenya [1].Although Djibouti is rich by geothermal resources from renewable energy sources, it does not have hydroelectric power plants. In this sense, it is dependent on foreign energy. The demand for energy is constantly increasing in the world. In order to meet the need for energy, thermal power plants using fossil fuels are used. In order to ensure continuity in economic ⇑ Corresponding author. E-mail address: [email protected] (T.C. Akinci). Peer review under responsibility of Karabuk University.
development, the plants using renewable energy sources should be increased [2].In this sense, small wind turbines are designed and implemented to contribute to meeting the local energy needs [3–7]. The development of technology ensures the continuous improvement of wind turbine dimensions and performance. The efficiency and performance of wind turbines are increasing in proportion to the quality of energy production in the turbines [8–13]. The wind speed measured in urban centers can be affected by the positioning of buildings in the city [4,14]. In this case, the wind prediction for enterprises using small wind turbines should be done with a two-parameter Weibull probability distribution [15,16]. In this study, a five-year wind speed data of Djibouti was analyzed by micro wind turbine in the city center of Djibouti. The data obtained from the anemometer at a height of 10 m were used in the analysis. The goal of this study is to determine the economic impact of energy produced by micro wind turbines and attract the interest of investors. Moreover, this study is one of the first
https://doi.org/10.1016/j.jestch.2019.06.004 2215-0986/Ó 2019 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: A. I. Idriss, R. A. Ahmed, A. I. Omar et al., Wind energy potential and micro-turbine performance analysis in Djibouti-city, Djibouti, Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.06.004
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studies evaluating the wind potential in the Republic of Djibouti. Weibull distribution function gives good results in the analysis of variable data. This is the most efficient method for the distribution functions that best fit the measured data [17–21]. In this study, the data collected is located at different heights of five wind turbines, examining the performance of these five-year data, the average daily and monthly wind speed was considered average power output and capacity factor. 2. Site description and theoretical background The location of the weather station for the wind energy potential assessment is in Djibouti-city (11.5658 °N; 43.15435 °E; altitude 7 m). The city is situated in East Africa and has a typical tropical climate and is officially classed as a maritime desert: a climatic region that is hot and humid in winter and extremely hot and humid in the summer. In the maritime desert, the air temperature is only slightly less than those of the inland regions. However, the presence of the warm ocean in the area creates a high level of humidity. The temperatures are high at 20–30 °C during the cool season from October to April and 30–45 °C during the hot season from May to September. Detailed analysis is required for small wind projects. In this planning, the country’s altitude, meteorological characteristics, and wind speeds of past periods are the main criteria for estimating data. There are several methods used in the literature to determine the wind potential [22–27]. In this study, wind speed was evaluated on meteorological data. Table 1 shows the performancemeasure of the wind speed and definition. Wherein the Weibull probability density function (f (v)), cumulative distribution function (F (v)), power density (PD), energy density (ED), the average power output (Pe, ave), capacity factor (CF) as shown, and annual energy output (AEP). These quantities describe the performance of micro-turbine in relation to wind potential, wind characteristic in the site and energy production. In determining the potential of wind energy, the turbine should be installed in the location and wind performance [24].
Fig. 1. Statistical functions at 10 m height upon 5 years.
and scale parameters are 1.88 and 1.96 m/s, respectively. The probability density curve indicates the most frequent wind speed, which is 1.3 m/s with the probability of 42.1%. In statistical analysis, the cumulative distribution gives the possibility of being equal to or lower than the wind speed. Djibouti Republic of Djibouti as a result of the analysis made in the city’s maximum winds of 6 m/s was measured to have reached. Table 2 shows the monthly mean wind speed characteristics and wind energy potential. The results show that the values of c and k parameters, obtained are in the range from 1.462 m/s to 2.830 m/s and from 1.410 to 2.410, respectively. VF and the VE values were obtained using Equations (4) and (5). The mean wind speed value is low 1.247 m/s was in June, while February appears to have the highest mean wind speed value of 2.329 m/s. The lowest and highest VF is 0.609 m/s for June and 2.266 m/s in February, respectively. The value of wind speed denotes VE ranges from 2.223 m/s to 4.212 m/s. According to the Equations (6) and (7), the min. and max. power densities occur in June and February, respectively. The energy density gives the minimum value of 0.855 kWh/m2 in June and the maximum value of 5.2 kWh/m2 in February, respectively. To estimate the wind energy potential, Fig. 2 shows VFand VE wind speeds for the monthly periods (5-year average). The values of VF are ranged from0.6 m/s (in June) to 2.26 m/s (in February). Also, the values of VE are ranged from 2.22 m/s (in October) to 4.21 m/s (in August). These two values included in wind turbine design calculation [28,29]. Fig. 3 displays a 2-years (2015 and 2016) distribution of wind speed series, the hourly variation of mean wind speed, the wind
3. Results and discussions The wind speed data was measured at Djibouti-city weather station over a period of 5 years and was recorded every 10 min interval and was measured at 10 m. Fig. 1 shows the probability density function and cumulative distribution function. The histogram represents the measured wind speed data. The probability density function and Weibull cumulative density function are given in Eqs. (2) and (3), respectively. The values of the shape
Table 1 Summary of the performance measures equations for wind resources analysis. Performance measures
Definition
Vertical variation of wind speed Probability density function (PDF)
VðZ R Þ ¼ VðZÞ½lnðZ R =Z 0 Þ=lnðZ=Z 0 Þ
Equations
Cumulative distribution function (CDF)
FðVÞ ¼ 1 eðV =cÞ
f ðVÞ ¼ ðk=cÞðV=cÞk1 eðV=cÞ
k
(1) (2)
k
(3)
1=k
(4)
1=k
(5)
Most probable wind speed (VF)
V F ¼ cððk 1Þ=kÞ
Wind speed carrying maximum energy (VE)
V E ¼ cððk þ 2Þ=kÞ
Power density (PD) Energy density (ED) Weibull shape parameter (k) Weibull scale parameter (c) Exponent n Average power output (Pe, ave)
P D ¼ 12 qV 3m ED ¼ P D T kðhÞ ¼ k0 ½1 0:088lnðh0 =10Þ=½1 0:088lnðh=10Þ n cðhÞ ¼ c0 ðh=h0 Þ n ¼ ½0:37 0:088lnðc0 Þ=½1 0:088lnðh=10Þ n o k k k ðeðV c =cÞ eðV r =cÞ Þ=ððV r =cÞk ðV c =cÞk Þ eðV f =cÞ P e;av e ¼ P eR
(7) (8) (9) (10) (11)
Capacity factor (Cf) Annual energy output (AEP)
Cf ¼ P e;av e =P eR AEP ¼ Cf P eR t
(12) (13)
(6)
Please cite this article as: A. I. Idriss, R. A. Ahmed, A. I. Omar et al., Wind energy potential and micro-turbine performance analysis in Djibouti-city, Djibouti, Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.06.004
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A.I. Idriss et al. / Engineering Science and Technology, an International Journal xxx (xxxx) xxx Table 2 Analysis of wind speed characteristics in Djibouti over considered period. Month
Vm (m/s)
c (m/s)
k
VF (m/s)
VE (m/s)
PD (W/m2)
ED (kwh/m2)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1.710 2.329 1.801 1.541 1.339 1.247 1.669 1.782 1.375 1.688 1.839 1.502
2.135 2.830 2.180 1.877 1.612 1.462 1.598 2.348 1.716 1.660 2.297 1.840
1.802 2.410 2.290 2.095 1.710 1.410 1.966 1.47 1.668 2.207 2.060 1.527
1.363 2.266 1.697 1.377 0.964 0.609 1.113 1.081 0.991 1.263 1.664 0.917
3.230 3.636 2.868 2.585 2.536 2.736 2.283 4.212 2.752 2.223 3.194 3.183
3.063 7.738 3.578 2.241 1.470 1.188 2.848 3.466 1.592 2.946 3.809 2.075
2.279 5.200 2.662 1.614 1.094 0.855 2.119 2.579 1.146 2.192 2.743 1.544
Annual
1.652
1.963
1.885
1.276
2.953
3.001
2.169
Fig. 2. Comparing the periodic VF and VE wind speeds.
rose and the detailed monthly mean frequencies for main prevailing wind direction at 10 m hub height. Speed and direction of wind change rapidly with time. An example for a 2-years distribution of
wind speed series over the study area is shown in Fig. 3(a). Here, the wind fluctuates between 0 and 7.1 m/s. The lack of data during the data acquisition is observed for few days between the 9th and 30th are missing for September 2015 due to a technical problem of the cup-anemometer. We can see a close similarity of the wind speed for both years confirming a constancy profile of data for all considered period. These values from the distribution obtained in Fig. 3(a) are statistically analysed to generate monthly wind speed contour map obtained in Fig. 3(b). The hourly wind speed values range from 0 to 3.2 m/s. Maximum wind speed was observed from 10 h to 8 h pm. Also, the results reveal that from January to March, the wind speeds values are relatively high and till 3.2 m/s. From April to August, wind speeds are low (1–1.5 m/s). Wind speed density was high during working hours, which is crucial for microwind generators users in the urban zone. The wind rose diagram is a favourite method to represent wind speed as a function of direction. Fig. 3(c) shows a frequency rose with a relative frequency of different wind speed ranges (indicated by the color code in the legend section of the graph) of the study site upon 5 years analysis. Fig. 3(d) illustrates the detailed monthly mean
Fig. 3. (a) A two years (2015 and 2016) distribution of wind speed series, (b) Monthly hourly mean wind speeds contour map (c) Annual frequencies of wind rose and (d) Detailed monthly mean frequencies for main prevailing wind direction at the Djibouti-city at 10 m hub height.
Please cite this article as: A. I. Idriss, R. A. Ahmed, A. I. Omar et al., Wind energy potential and micro-turbine performance analysis in Djibouti-city, Djibouti, Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.06.004
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frequencies for main prevailing wind direction obtained in Fig. 3 (c). The dominant wind direction is eastern winds throughout the year (61.3%) and it reaches its minimum (10.39%) the summer periods. The second dominant sector of wind direction observed is western winds (36.05%), recorded from May to October due to relatively high levels of the Khamsin which is a hot dry wind that brings clouds of sand. Also, some south-easterly and northeasterly wind exist during the transition months of April and September with the frequency values range from 0 to 15.57%. Fig. 4 presents the monthly mean wind speed profile and the average energy density in Djibouti-city for the different heights for the five-year period. As can be observed in Fig. 4(a), throughout the whole year, the monthly average wind speed has similar distribution and varies from 1.2 m/s to 2.65 m/s for different heights, with maximum values observed in February, August, and November. The wind speed increases with height variation (H = 10, 20 and 30 m) as expressed in Equation (1) (with Zr is the reference height (10 m) and Z0 is the roughness length, which is 0.0024 m for open terrain with the smooth surface has been used in the calculation). It is also noted in the histogram plot in Fig. 4(b) the average energy density varies between 0 kWh/m2 and 7.5 kWh/m2 with maximum points obtained in February, August, and November. The PD in Djibouti-city can be included into class 1 with a value of 3 W/m2 and it is limited but it is appropriate for low potential wind turbines in urban and small communities. In a study conducted by [30,31], the Ethiopian wind energy is categorised as class 1 with the wind speed data according to class of 3.5–5.6 m/s. Djibouti-city is located in the coastal Djibouti region on the Gulf of Tadjoura, and its wind energy potential varies depending on the region. The available wind potential for Djibouti-city couldn’t be estimated due to lack of data on other areas.
of the selected wind turbines are shown in Fig. 5. The graph illustrates the power available from each wind turbine (indexed from A to E) across a range of wind speeds. The chosen horizontal wind turbines have various capacities from 1 kW to 25 kW from different manufacturers to estimate their performance in the Djibouti-city. These turbines were also considered for their reasonable cost. The estimated Capacity factor (Cf) and the annual energy output (AEP) are the performance indicator of the turbine’s generation capacity. The Eqs. (8)–(12) were usedto calculate the monthly power output from each turbine. Fig. 6(a) and (b) depict the monthly average Cf and AEP of generation for five turbines upon 5 years.
4. Wind turbines characteristics and performance Depending on the site’s wind speed profile, we selected five commercial wind turbines with technical parameters (cut-in (Vc), rated (Vr) and cut-off (Vf) wind speeds, hub height, and the rated electrical power (PeR)) are given in Table 3 and the power curves
Fig. 5. Power curves of selected five turbines (indexed from A to E).
Fig. 4. Plots comparing monthly average (a) wind speeds variations and (b) energy density at the different hub heights (10, 20, and 30 m) of whole-year data series covering 2014–2018.
Table 3 Technical details of the wind turbine model from different manufacturers. Turbine index
Wind turbine Model
Vc (m/s)
Vf (m/s)
Vr (m/s)
Hub height (m)
PeR (kW)
A B C D E
AEOLOS-1 AEOLOS V-5 HAWT-5 HAWT-11 POLARIS P12-25
1.5 2.5 3 3 2.7
50 52.5 60 25 25
10 10 11 10 10
2.8 5.3 12 18 30.5
1 5 5 11 25
Please cite this article as: A. I. Idriss, R. A. Ahmed, A. I. Omar et al., Wind energy potential and micro-turbine performance analysis in Djibouti-city, Djibouti, Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.06.004
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Fig. 6. Monthly mean of (a) Cf and (b) AEP of five wind turbines using mean wind speed at 10 m hub height of whole-year data series covering 2014–2018.
In August, the values of Cf are 4.324, 5.397, 3.337, 5.509 and 9.629 respectively while been lowest in July and October. Turbines E and D had the highest capacity factors. The highest annual power energy output is given by turbine E with a value of 2.1 104 MWh, whereas the lowest value is given by turbine A with a value of 0.14 104 MWh. The results are affected by the wind speed’s profile of the site and characteristic of each wind turbine. The highest capacity factor of the wind generators will give the highest value of annual power energy production. Although, the Cf was relatively low (10%) in the study area.
5. Conclusion In this study, the first wind energy potential and micro-turbine performance analysis were carried out in Djibouti. Five years wind speed data were subjected to Weibull k and c parameters and other statistical analyses. The most important outcomes are summarized as follows: 1. The monthly mean wind speed is recorded as 1.247 m/s and 2.329 m/s in June and February, respectively. The monthly mean energy and power densities values are respectively computed as 1.188 W/m2, 0.855 kWh/m2 in June and 7.7738 W/m2, 2 kWh/m2 in February. 2. k ranges from 1.41 to 2.41 while c is ranged from 1.462 m/s to 2.83 m/s. 3. The measured time history of wind speed confirming a constancy profile of data for all considered period. The monthly wind power class in Djibouti-city is class 1, but, this potential is appropriate for low capacity wind turbines in urban and small communities. 4. The prevailing wind direction is eastern winds (61.3%) and the second prevailing sector of wind direction observed is western winds (36.05%) upon 5 years over the study area. 5. The performance of five wind turbines were analyzed and the results illustrate that Polaris P12-25 turbine yielded the highest capacity factor (Cf = 9.629%) and energy output (AEP = 2.1 104 MWh) in August. Finally, the results show that Djibouti-city has a potential to develop the small-scale applications wind turbines and to facilitate further actions for similar research projects in other locations, an extensive study on power feasibility should be done in larger geographical areas of Djibouti-city to choose the best possible options for different scale wind power generation. This study is only the first phase of a project to set up a power system with microturbines for an urban community not connected to the electricity grid.
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Please cite this article as: A. I. Idriss, R. A. Ahmed, A. I. Omar et al., Wind energy potential and micro-turbine performance analysis in Djibouti-city, Djibouti, Engineering Science and Technology, an International Journal, https://doi.org/10.1016/j.jestch.2019.06.004