Analysis of wind energy potential in north east Nigeria

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1 Journal of Energy and Natural Resources 014; (4): Published online August 10, 014 ( doi: /j.jenr ISSN: (Print); ISSN: (Online) Analysis of wind energy potential in north east Nigeria A. Ahmed 1, A. A. Bello, D. Habou 1 Department of Mechanical Engineering, Kano Uniersity of Science and Technology, Wudil, Nigeria Department of Mechanical Engineering, Abubaar Tafawa Balewa Uniersity, Bauchi, Nigeria address: abdula@yahoo.com (A. Ahmed), biieeyz@yahoo.com (A. A. Bello), hdandauta@gmail.com (D. Habou) To cite this article: A. Ahmed, A. A. Bello, D. Habou. Analysis of Wind Energy Potential in North East Nigeria. Journal of Energy and Natural Resources. Vol., No. 4, 014, pp doi: /j.jenr Abstract: This research reports wind energy potential ealuation of two locations in the north east Nigeria (Bauchi and Borno). The ealuation is based on Weibull and Rayleigh models using 17 years mean monthly wind speed data coering the period ( ). The result shows that Rayleigh is best fit model that describes the wind speed data at 10 m height. Reference mean power density (based on the measured probability distribution) was compared with those obtained from the Weibull and Rayleigh models. In calculating the percentage error, results shows that Weibull proided better power density estimation in all 1 months than the Rayleigh model. From this research wor, it was found that Borno has high wind power density 7.16 W/m for Weibull and W/m for Rayleigh in the month of June as compared Bauchi with highest power density of 1.45 W/m for Weibull and 7.06 W/m for Rayleigh in the month of May. Keywords: Wind Energy Potential, Nigeria, Generation, Weibull, Rayleigh, Probability Density Function 1. Introduction Wind energy is currently the most economic renewable energy apart from hydropower, its usage ersatility and ability to use it as a decentralized energy form mae its applications possible in rural areas where it is technically and economically feasible in the country. The major challenge to using wind as a source of electricity generation is that wind is intermittent and it does not always blow when electricity is needed. Howeer, wind power is one of the most the promising and cost effectie renewable. In the 1980 s, California purchased large quantities of wind power inest on operating experience needed to bring the cost of wind power down to a power installed in California, and another 1000W installed in other parts to generate electricity for oer 750,000 homes. The number of wind farms in US has increased substantially in the wind farms installed. The US Department of Energy projects that by the power, enough to generate electricity for 1.7 million homes, due to the power will fall. Currently, wind power costs between and 6 cents maing it one of the cheapest resources aailable [1]. Nigeria is subject to the seasonal rain bearing south westerlies, which blow strongly from April to October and to the dry and dusty North- East trade wind which blow strongly from Noember to March eery year. Most areas sometimes experience some periods of doldrums in between these periods. In Nigeria, wind energy reseres at 10 m height shows that some sites hae wind regime for between 1.0 to 5.1 ms -1. Energy supply in Nigeria is a major problem for both large and small scale purposes. Highly centralized production and distribution units hae not been equally distributed thus becomes inadequate in meeting the economic needs of both urban and rural populace in Nigeria. With respect to this problem, solar and wind energy are some of the alternatie sources of energy that can be exploited to meet some of the populace needs. It is therefore necessary to ealuate the wind regimes in the country and assess the potential of wind, installing wind energy conersion system for the generation of electricity. In this context, oer the years researchers hae carried out a number of studies in order to assess the wind energy potential in some parts of the world. Shata [1] wored on the potential of electricity generation on the coast of Red Sea in Egypt. Celi [5] studied the distributional parameters used in assessment of suitability of wind speed probability density function. Ozoptal, et al [8] studied the

2 47 A. Ahmed et al.: Analysis of Wind Energy Potential in North East Nigeria regional wind energy potential of Turey. In this presentation, 17 years ( ) monthly mean wind speed data are obtained from Nigeria Meteorological Agency (NIMET) Abuja for some selected locations in the North east Nigeria (Bauchi and Borno), were statistically analyzed to ealuate wind power density based on the Weibull and Rayleigh models.. Data Collection and Wind Speed Characteristics Table 1. Geographical data of the locations Locations State Latitude (N) Longitude (E) Altitude Bauchi Bauchi 10 o 18'57 9 c 50'9 615 Maiduguri Borno 11 o 50'47 1 c 9'7 99 Table. Summary of aerage wind speed V m (ms -1 ) and standard deiation σ (ms -1 ). Bauchi Borno Months V m(ms -1 ) σ V m(ms -1 ) σ Jan Feb Mar Apr May Jun Jul Aug Sep Oct No Dec Table. Summary of aerage scale factor c (ms -1 ), shape factor and gamma function Bauchi Months c(m/s) Borno c(m/s) Jan Feb Mar Apr May Jun Jul Aug Sep Oct No Dec Table 4. Maximum and minimum alues of wind speed (ms -1 ) Locations Max. Vel. (m/s) Month Min. Vel. (m/s) Month Bauchi.16 May 1.47 Dec Borno 6.88 Jun 4.07 Oct In this study, statistical analyses of wind speed and power density aailable in selected states of north central Nigeria are inestigated. Seenteen years monthly mean speed ( ) from Nigeria Meteorological Agency (NIMET) Abuja at the height of 10m was used. Table 1 shows the geographical locations of the two locations in the north east of Nigeria (Bauchi and Borno). From the aboe Table it can be seen that Bauchi is located at longitude 9 c 50'9 East and latitude 10 o 18'57 North with a land scale slope of 615 meters. Borno is located at longitude 1 c 9'7 East and latitude 11 o 50'47 North with a land scale slope of 99 meters. The summary of aerage wind speed V m (ms -1 ) and standard deiation σ (ms -1 ) for the monthly distributional parameters for all the sites are presented in Table below. The summary of aerage scale factor c (ms -1 ), shape factor and gamma function for the monthly distributional parameters of the locations considered are presented in Table below while Table 4 presents the maximum and minimum alues of wind speed (ms -1 ) for the two locations and months of their occurrences. The frequency probability distribution for Bauchi in the month of January is presented in Table 5; the same pattern of table was computed for the two locations. Table 5. Frequency probability distribution for January for Bauchi. Vj Vmj fj f(j) fw(j) fr(j) E E E E Monthly Aerage Wind Speed and Standard Deiation The monthly aerage wind speed and the standard deiation can be obtained using equation 1 and below. σ = V m = N -1 1 N N 1 = i 1 N Vi i= 1 ( Vi Vm) 1/.. Wind Speed Probability Distributions The wind speed data in time series format is usually arranged in the frequency distribution format since it is more conenient for statistical analysis. Therefore, the aailable time series data were translated into frequency distribution format. This process is illustrated for an (1) ()

3 Journal of Energy and Natural Resources 014; (4): example for the month of January for Bauchi as presented in Table 5. The wind speed is grouped into classes (bins) as gien in the first column of Table 5.The mean wind speeds are calculated for each speed class interals are in second column. The probability density distribution is presented in the third column. The probability density obtained from Weibull and Rayleigh parameters are presented in the fourth and fifth columns. The wind speed distributions and the functions representing them mathematically are the main tools used in the wind related literature. Their use include a wide range of applications, from the techniques used to identify the parameters of the distribution function to the use of such functions for analyzing the wind speed data and wind energy economics. Two of the commonly used functions for fitting a measured wind probability distribution in a gien location oer a certain period of time are the Weibull and Rayleigh. The probability density function of the Weibull distribution is gien by; [ ] f w () = (/c) (/c) -1 exp ( / c) () The corresponding cumulatie probability function of the Weibull distribution is, [ ] F w () = 1 exp ( / c) c = V m = c Γ (4) ( ) = 1 (5) σ The Rayleigh model is a special and simplified case of the Weibull model. It is obtained when the shape factor of the Weibull model is assumed to be =. The probability density and the cumulatie functions of the Rayleigh model are gien by, π f R (V) = π exp m 4 m π F R (V) = 1- exp 4 m One of the most distinct adantages of the Rayleigh distribution is that the probability density and the cumulatie distribution functions could be obtained from the mean alue of the wind speed. The Rayleigh model has (6) (7) (8) (9) also widely been used to fit the measured probability density distribution... Power Density Distribution & Mean Power Density The power of the wind per unit area is gien by; P (V) = 1 PV (10) Where is assume to be 1.5 g/m in this paper. The wind power density for the measured probability density distribution which seres as the reference mean power density as shown below; n P m.r = PV mj f ( j) j= 1 1 P m (V) = P w = P R = 1 (11) ρ.(v ) m (1) 1 ρ. c Γ1 + (1) ρv. m π (14) The yearly aerage error in calculating power densities using both Weibull and Rayleigh functions is obtained by using equation below; Error (%) = P W, R P P i= 1 m, R m, R. Analysis of Wind Speed Data (15) The surface wind characteristics and stochastic analysis of the wind speed data in the two locations of the north east part of Nigeria were carried out. It is seen from Table that the highest aerage wind speed occurred in June (6.88 ms -1 ) in Borno while the lowest aerage wind speed occurred in December (1.47 ms -1 ) in Bauchi. The aerage scale factor c (ms -1 ) ranges from 1.66 ms -1 in Bauchi to 7.4 ms -1 in Borno, while the shape factor ranges from.54 in Bauchi to 6.91 in Borno as presented in Table..1. Best Fit Probability Distribution Model The aerage monthly alues of the correlation coefficient for the two locations in north east Nigeria ranges between 0.10 and 0.59 for the Weibull and the Rayleigh model ranges from 0.87 to as indicated in Table 6. The month to month comparison shows that Rayleigh model returns higher coefficient alues in all the twele months for Bauchi and Borno. It can be seen from Table 7 that in Borno, Rayleigh model returns higher coefficient in all months while Bauchi also returns higher coefficient

4 49 A. Ahmed et al.: Analysis of Wind Energy Potential in North East Nigeria alues for Rayleigh except in the month of Noember and December. Table 6. Correlation coefficient alues for all the location. Bauchi Borno MONTHS W R W R JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC AVE Fig. Power density (W/m ) for Borno (P m.r, P w & P R) From Figure, it can be seen that Rayleigh returns a higher percentage error in almost all the month expect for the month of December where Weibull hae a lower percentage error than Rayleigh model. It can be seen that from Figure 4, Rayleigh returns a higher percentage error in all the twele months. Table 7. Summary of best fit probability distribution models. Months Bauchi Borno Jan Rayleigh Rayleigh Feb Rayleigh Rayleigh Mar Rayleigh Rayleigh Apr Rayleigh Rayleigh May Rayleigh Rayleigh Jun Rayleigh Rayleigh Jul Rayleigh Rayleigh Aug Rayleigh Rayleigh Sep Rayleigh Rayleigh Oct Rayleigh Rayleigh No Weibull Rayleigh Dec Weibull Rayleigh Fig. % Error for Weibull and Rayleigh Bauchi 4. Results and Discussion The mean power density shows a large month to month ariation as shown in Figure 1 and. From Figure 1 the maximum power density occurs in month of May for Rayleigh (7.06 W/m ) while for Weibull is (1.45 W/m ). From Figure the maximum power density occurs in the month of June for Rayleigh (65.77 W/m ) while for Weibull is (7.16 W/m ). Fig 1. Power density (W/m ) for Bauchi (P m.r, P w & P R) 5. Conclusion Fig 4. % Error for Weibull and Rayleigh Borno In this study, it was found that Rayleigh returns a higher power density than Weibull and the highest power density is (65.77 W/m ) for Rayleigh in Borno in the month of June, it was also found that Rayleigh returns the best fit probability distribution than Weibull in almost all the months in the two locations of north east part of Nigeria considered in this research wor. Finally, in calculating error in power density the Weibull model returns smaller error in calculating the power density compared to Rayleigh model in all locations. The power density is estimated by the Weibull model with a smallest error alue of 1.5% for Bauchi in the month of March. From this research wor, it was found that Borno in north east part of Nigeria has high wind power density for the generation wind energy with a maximum alue of power

5 Journal of Energy and Natural Resources 014; (4): density of W/m for Weibull. Borno can be classified under wind class II since the power density is greater than 100 W/m while Bauchi can be classified under wind class I since the power density is less than 100 W/m. References [1] Ahmed A., Adisa AA, Habou D. An ealuation of wind energy potential in the northern and southern regions of Nigeria on the basis of Weibull and Rayleigh models. America Journal of Energy Engineering 01; 1: 7 4. [] Ahmed Shata AS, Hanistsch R. The potential of electricity generation on the east coast of Red Sea in Egypt. Renewable Energy 006; 1: [] Ahmed SA. Inestigation of wind characteristics and wind energy potential at RAS Ghareb, Egypt. Renewable and Sustainable Energy Reiews 011; 15: [4] Apinar EK, Apinar S. Determination of the wind energy potential for Maden-Elazig, Turey. Energy Conersion and Management 004; 45: [5] Brano VL, Orioli A, Ciulla G, Culotta S. Quality of wind speed fitting distributions for the urban area of Palermo, Italy. Renewable Energy 011; 6: [6] Celi AN. On the distributional parameters used in assessment of the suitability of wind speed probability density functions. Energy Conersion and Management 004; 45: [7] Celi AN. A statistical analysis of wind power density based on the Weibull and Rayleigh Models at the southern region of Turey. Renewable Energy 004; 9: [8] Celi AN. Assessing the suitability of wind speed probability distribution functions based on the wind power density. Renewable Energy 00; 8: [9] Oztopal A, Sahin AD, Agun N, Sen Z. On the regional wind energy potential of Turey. Energy 000; 5: [10] Sambo AS. The renewable energy for rural deelopment. The Nigerian perspectie ISESCO Science and Technology ision May, 005; 1: [11] Salem AL. Characteristics of surface wind speed and direction oer Egypt Solar Energy for sustainable deelopment 004; 4: [1] Seguro JV, Lambert TW. Modern estimation of the parameters of the Weibull wind speed distribution for wind speed distribution for wind energy analysis. J Wind Eng Ind Aerodyn 000; 85: [1] Weisser D. Wind energy analysis of Grenada: an estimation using the Weibull density function. Renewable Energy 00; 8: [14] World Wind Energy Association (WWEA). World wind energy report 011 website: Accessed August, 01.