Analysis of wind speed data and wind energy potential in three selected locations in south-east Nigeria
© Oyedepo et al.; licensee Springer. 2012
Received: 7 March 2012
Accepted: 25 May 2012
Published: 25 May 2012
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© Oyedepo et al.; licensee Springer. 2012
Received: 7 March 2012
Accepted: 25 May 2012
Published: 25 May 2012
In this study, the wind speed characteristics and energy potential in three selected locations in the southeastern part of Nigeria were investigated using wind speed data that span between 24 and 37 years and measured at a height of 10 m. It was shown that the annual mean wind speed at a height of 10 m for Enugu, Owerri and Onitsha are 5.42, 3.36 and 3.59 m/s, respectively, while the annual mean power densities are 96.98, 23.23 and 28.34 W/m2, respectively. It was further shown that the mean annual value of the most probable wind speed are 5.47, 3.72 and 3.50 m/s for Enugu, Owerri and Onitsha, respectively, while the respective annual value of the wind speed carrying maximum energy are 6.48, 4.33 and 3.90 m/s. The performance of selected commercial wind turbine models (with rated power between 50 and 1,000 kW) designed for electricity generation and a windmill (rated power, 0.36 kW) for water pumping located in these sites was examined. The annual energy output and capacity factor for these turbines, as well as the water produced by the windmill, were determined. The minimum required design parameters for a wind turbine to be a viable option for electricity generation in each location are also suggested.
The quest to reduce environmental impacts of conventional energy resources and, more importantly, to meet the growing energy demand of the global population had motivated considerable research attention in a wide range of environmental and engineering application of renewable form of energy. It is recognized that wind energy, as a renewable energy source, has stood out as the most valuable and promising choice. Wind energy by nature is clean, abundant, affordable, inexhaustible and environmentally preferable. Due to its many advantages, wind energy has also become the fastest growing renewable source of energy in both developed and developing countries. For example, wind energy is widely used to produce electricity in countries like Denmark, Spain, Germany, USA, China and India. Interestingly, the global cumulative installed capacity of wind power had increased sharply from 6,100 MW in 1996 to about 237,669 MW in 2011 . In Africa, for example, Egypt, Morocco and Tunisia are the leading countries with installed capacities of 550, 291 and 114 MW, respectively, at the end of 2011 .
The increasing energy demand, the rapidly depleting fossil fuel reserves and the environmental problems associated with the use of fossil fuel have necessitated the development of alternative energy sources like wind energy for electricity generation in Nigeria. It is reported that the electricity production in Nigeria as of the end of 2010 is less than 4,000 MW due to fluctuations in the availability and maintenance of production sources, leading to a shortfall in supply . However, analyses of available wind data for selected cities have confirmed a high prospect of wind energy resources in Nigeria. Several studies on renewable sources of energy have also been performed. A detailed review and discussion of these studies can be found in [2–6] and are not repeated here. Worthy of mention here from these studies, however, is that the effective utilization of wind energy at a typical location requires sound knowledge of the wind characteristics and accurate wind data analysis. For example, the choice of wind turbine design must be based on the average wind velocity at a selected wind turbine installation site . Prior studies have also shown that the wind flow patterns are influenced by terrains, vegetation and water bodies.
Although several studies have been performed to investigate the characteristics and pattern of wind speed across Nigeria, less attention has been given to sites in the south-east region. According to [8, 9], the few reported studies on wind speed in this part of the country were limited to wind speed distributions, while less attention was paid to the wind energy potential evaluation. The focus of this study is, therefore, to evaluate the wind energy potential in three selected locations (Enugu, Owerri and Onitsha) in the south-east region and to assess the performance of selected small- to medium-size commercial wind turbines. It is the authors' view that this information will be helpful to the government and any organization in making an informed decision with regard to investment in wind energy resource in this part of Nigeria.
The geographical location of the selected stations
1971 to 2007
1977 to 2002
1978 to 2003
Equation 6 is used in this study to estimate the monthly and annual scale factors.
The most probable wind speed corresponds to the peak of the probability density function, while the wind speed carrying maximum energy can be used to estimate the wind turbine design or rated wind speed. Prior studies have shown that wind turbine system operates most efficiently at its rated wind speed. Therefore, it is required that the rated wind speed and the wind speed carrying maximum energy should be as close as possible .
A wind energy conversion system can operate at its maximum efficiency only if it is designed for a particular site because the rated power and cut-in and cut-off wind speeds must be defined based on the site wind characteristics . It is essential that these parameters are selected so that energy output from the conversion system is maximized. The performance of a wind turbine installed in a given site can be examined by the amount of mean power output over a period of time (P e,ave) and the conversion efficiency or capacity factor of the turbine. The capacity factor C f is defined as the ratio of the mean power output to the rated electrical power (P eR) of the wind turbine [12, 20].
where v c v r and v f are the cut-in wind speed, rated wind speed and cut-off wind speed, respectively. For an investment in wind power to be cost effective, it is suggested that the capacity factor should be greater than 0.25 .
The cumulative probability distributions of the wind speed at all the study locations (Figure 1b) show a similar trend. The cumulative distribution function can be used for estimating the time for which wind speed is within a certain speed interval. For wind speeds greater or equal to 2.5 m/s cut-in wind speed, Enugu, Owerri and Onitsha have frequencies of about 96.9%, 86.5% and 86.9%, respectively, while the same locations respectively have frequencies of about 88.4%, 44.7% and 55.3% for wind speed of 3.5 m/s cut-in wind speed. According to Ojosu and Salawu , if a wind turbine system with a design cut-in wind speed of 2.2 m/s is used in these sites for wind energy resource for electricity generation, all the sites will have frequencies of more than 92%.
Characteristic speeds and mean power density in Enugu at a height of 10 m
Characteristic speeds and mean power density in Owerri at a height of 10 m
Characteristic speeds and mean power density in Onitsha at a height of 10 m
The monthly mean wind speed varies between 4.13 m/s in November and 6.30 m/s in March for Enugu site (Table 2). The monthly mean power density varies between 43.15 W/m2 in November and 153.16 W/m2 in March. Therefore, based on PNL wind power classification scheme , the monthly mean power density mostly falls into class 1 (P D ≤ 100) except in January, February, April and July, when it falls into class 2 (100 < P D ≤ 150), and in March, when it falls into class 3 (150 < P D ≤ 200). However, the annual mean power density for this site is 96.98 W/m2 (class 1). For Owerri (Table 3), the minimum and maximum values of the monthly mean wind speeds are 2.72 and 3.70 m/s, respectively, while the annual mean wind speed for this site is 3.36 m/s. The monthly mean power density varies between 11.66 W/m2 in November and 31.02 W/m2 in January. The monthly mean power density falls into class 1 wind resource category (P D ≤ 100) in all the months, and the annual mean power density for this site is 23.23 W/m2 (class 1). In the case of Onitsha (Table 4), the minimum and maximum values of the monthly mean wind speeds are 3.01 m/s (in November) and 4.23 m/s (in March), respectively. The monthly mean power density varies between 16.70 W/m2 in November and 46.36 W/m2 in March. The monthly mean power density falls into class 1 wind resource category (P D ≤ 100) in all the months, and the annual mean power density for this site is 28.34 W/m2 (class 1). Detailed information about these sites' wind speed characteristics (mean wind speed, most probable wind speed (V F) and the wind speed carrying maximum energy (V E)) and mean power density are illustrated in Tables 2 3 and 4.
The least monthly value of the Weibull shape parameter k for Owerri is 2.61 in January and reached the highest value of 8.48 in the month of May. Therefore, the wind speed is most uniform in Owerri in May, while it is least uniform in December. The annual shape factors for Enugu, Owerri and Onitsha are 4.05, 5.10 and 4.27, respectively. The least monthly value of Weibull scale parameter c is obtained as 2.97 m/s in the month of November in Owerri, and the highest value of 6.81 m/s in the month of March in Enugu. The annual shape factors for Enugu, Owerri and Onitsha are 5.87, 3.65 and 3.96 m/s, respectively.
In summary, Enugu has the highest annual mean wind speed among the sites considered in this study. Also, this site has the highest values of annual power density. Even though the most probable wind speed (V F) is a statistical characteristic, which may not be directly connected to wind energy , it does not necessarily mean that Enugu has much higher wind potentials than the other locations considered. However, as mentioned earlier, the efficiency of a wind turbine is closely related to these parameters, especially V E, which should be as close as possible to the design or rated wind speed of the system. Therefore, the proposed wind turbine, if installed in Enugu, would likely produce more power than other locations. Moreover, it can be considered as the best site for wind energy development in southern Nigeria (based on the three sites considered in this study). Furthermore, the monthly mean wind speeds in south Nigeria ranges from 2.72 to 6.30 m/s. The monthly mean power density varies between 11.66 and 153.15 W/m2, while the annual mean power density is in the range of 23.23 to 96.98 W/m2. It can be inferred from this analysis that the wind resource in this part of Nigeria can be classified mostly into class 2 or less category. Furthermore, the annual mean energy density varies between 203.53 and 717.62 kWh/m2.
Even though the wind resource in these locations falls into class 2 or less, which is considered as marginally or unsuitable for wind power development, the wind power can be used for water pumping and small-scale electricity generation, providing intermittent power requirements for a variety of purposes that need low-energy capacity, slow-running high-torque wind turbines with multi-blade, e.g., [3, 4, 6, 29]. For a modern wind turbine, the cut-in wind speed required by it to start generating electricity is generally between 3 to 5 m/s. Depending on the size of the turbine, the peak power output can be attained when the wind speed (rated wind speed) is in the range of 10 to 15 m/s . For water pumping, wind turbine can be operated at a lower wind speed; however, they can function effectively when the wind speed is more than 3 m/s. Based on the required quantity of water, a site with a mean wind speed around 2.0 m/s can be considered for wind-powered pump application . Similarly, depending on the end use of the generated power, it can be concluded that these locations may be suitable for utilization of wind energy.
Characteristics of the selected wind turbines
Rated power (kW)
Hub height (m)
Rotor diameter (m)
Cut-in wind speed (m/s)
Rated wind speed (m/s)
Cut-out wind speed (m/s)
The minimum annual energy outputs of 6.05 and 18.83 MWh/year are observed for Owerri and Onitsha, respectively, using the P19-100 model. While the maximum annual energy outputs are 178.58 and 461.32 MWh/year, respectively, for Owerri and Onitsha with the WWD-1-60 model. For each of these sites, the power generated by each wind turbine follows the same trend observed in Enugu. Regardless of the location, the WWD-1-60 wind turbine model produced the highest quantity of annual energy output. For example, if 1,000-kW turbines are to be operated at the same hub height, the POLARIS 62–1000 will likely perform better than WWD-1-60 and BONUS-1000-54 because of its low cut-in wind speed and rated wind speed as well as its bigger rotor diameter compared with other models.
The cost effectiveness of a wind turbine can be roughly estimated by the capacity factor of the turbine. This factor is a useful parameter for both consumer and manufacturer of the wind turbine system . The WWD-1-60 model has the highest value among the models considered for all the sites. The C f values for this model are 27.90%, 2.04% and 5.27% for Enugu, Owerri and Onitsha, respectively. The C f values for Enugu for POLARIS 15–50, POLARIS 62–1000 and WWD-1-60 are 28.30%, 27.75% and 27.90%, respectively. These values are greater than the suggested recommended value before an investment can be considered worthwhile. Hence, these wind turbines or similar turbine model will be good for electricity generation in Enugu. In Owerri, however, the capacity factor for other wind turbine models ranges from 0.49% for the BONUS-1000-54 model to 2.04% for the WWD-1-60 model. Similarly, the minimum and maximum capacity factors at Onitsha are 1.62% for the BONUS-1000-54 model and 5.27% for the WWD-1-60 model, respectively. Therefore, these two sites may be considered for wind energy development for small-scale applications such as water pumping (see ‘Wind-powered pumps performance’). It should be noted that the cost of generating electricity using wind turbine is inversely proportional to the capacity factor. The higher the capacity factor (or higher wind speed regime), the lower the cost of generated electricity, assuming that all factors remain the same (see, e.g., Paul et al. ).
Based on the annual energy output and the capacity factor, the POLARIS 62–1000 and WWD-1-60 models or wind turbines with similar designed characteristics will be best suited for electricity generation at Enugu and small-scale application in other locations. However, by redesigning the selected wind turbine models to operate at lower cut-in wind speed (especially the WWD-1-60 and BONUS-100-54 models), lower rated wind speed (especially BONUS-100-54) and higher hub height compared with their current design parameters (cut-in and rated wind speeds and hub height), both the annual energy output and capacity factor could significantly be improved. For instance, if POLARIS 62–1000 is to be operated at a hub height of 70 m and rated wind speed of 10 m/s, the capacity factor for Enugu, Owerri and Onitsha will be 59.17%, 8.20% and 16.99%, respectively. However, increasing the hub height may increase the overall capital cost of the wind turbines. But this is compensated for by increased in capacity factor and, hence, the energy output from the wind turbines.
In order to meet the minimum recommended capacity factor (25%) for electricity generation, the following design parameters are suggested: wind turbine model with a minimum hub height of 55 m, cut-in wind speed of less than 3.5 m/s, rated wind speed of around 12 m/s and cut-out wind speed of 25 m/s are recommended for Enugu; for Owerri, wind turbine with a minimum height of 75 m, cut-in wind speed of less than 3.5 m/s, rated wind speed of around 8.5 m/s and cut-out wind speed of 20 m/s are recommended; while a system with a minimum hub height of 65 m, cut-in wind speed of less than 3.5 m/s, rated wind speed of around 9 m/s and cut-out wind speed of 20 m/s are recommended for Onitsha.
Wind turbine parameters and rotodynamic pump specifications
Rated power (W)
Rated speed (m/s)
Cut-in speed (m/s)
Cut-out speed (m/s)
Design speed ratio
Design power coefficient
Efficiency (pump and transmission)
Monthly water produced and the number of habitants that can be served per month
(m 3 )
(m 3 )
(m 3 )
The total numbers of habitants that can be served by water discharged from these sites are also shown in Table 7. Based on water usage of 36 L/capita/day in Nigeria as of 2006 , the water output at Enugu can serve between 2,900 and 3,250 habitants depending on the month. The average number of people that can be served per month is estimated to be around 3,190. However, if the estimate is based on the minimum recommended water usage of 50 L/capita/day , the water produced can only serve about 2,290 habitants per month on average. In Owerri, the water produced can serve about 2,340 and 1,690 habitants per month on average based on 36 and 50 L/capita/day, respectively, while the total numbers of habitants that can be served by the water produced in Onitsha are 2,530 and 1,820/month on average based on water usage of 36 and 50 L/capita/day, respectively. Therefore, for small rural communities scattered across the southeastern part of Nigeria where access to good water and unreliable supply of water is a regular problem, wind resource development for water pumping will be a good option. For a larger community, the performance of a pump can be increased if a wind turbine with higher rated power (or diameter) is used instead of the small size used in this study. In addition, two or more wind turbines can be installed in these sites in order to increase the quantity of water produced.
In this study, the wind speed and wind energy potential in selected three locations in the southeastern part of Nigeria were investigated. In addition, the performance of selected commercial wind turbine models designed for both electricity generation and water pumping located in these sites was investigated. The findings from this study can be summarized as follows:
The annual mean wind speeds for Enugu, Owerri and Onitsha are 5.42, 3.36 and 3.59 m/s, respectively. The annual values of the wind speed carrying maximum energy for these locations are respectively 6.48, 4.33 and 3.90 m/s.
The mean annual value of Weibull shape parameter k is between 4.05 and 5.10, while the annual value of scale parameter c is between 3.96 and 5.87 m/s.
The annual mean power density for Enugu, Owerri and Onitsha are 96.98, 23.23 and 28.34 W/m2, respectively. Therefore, based on the wind data used in this study, the wind energy resource in south-east Nigeria may generally be classified into class 1. However, based on monthly mean power density, the wind resource may fall into higher class category in some cases.
Based on the capacity factor, the POLARIS 15–50, POLARIS 62–1000 and WWD-1-60 models or wind turbines with similar designed characteristics will be best suited for electricity generation in Enugu. However, in order to meet the minimum recommended capacity factor (25%) for electricity generation, wind turbine models with cut-in wind speed of less than 3.5 m/s minimum and hub height of 55, 75 and 65 m, as well as rated wind speed of about 12, 8.5 and 9 m/s, respectively, are recommended for Enugu, Owerri and Onitsha.
Using a 0.36-kW wind turbine, the average monthly water produced by a rotodynamic pump assumed to be installed in Enugu, Owerri and Onitsha is determined as 3,442, 2,530 and 2,730 m3, respectively. The quantity of water can serve about 2,290, 1,690 and 1,820 habitants in respective locations.
wind power rotor swept area
Weibull scale parameter
scale factor at the height ho
wind turbine power coefficient
wind turbine design power coefficient
wind turbine rotor diameter
mean energy density
probability of observing wind speed (V)
cumulative of observing wind speed (V)
pump gear ratio
acceleration due to gravity
wind turbine hub height
cup-generator anemometer height
dimensionless Weibull shape parameter
shape parameter height ho
pump speed at design condition
mean wind power density
ave: mean power output
rated wind turbine power
water produced at a given time
volume discharge of the pump at any wind speed V
period or time
wind speed carrying maximum energy
most probable wind speed
mean wind speed
wind speed at the cup-generator anemometer height
cut-in wind speed
vr, rated wind speed
cut-off wind speed.
The authors are grateful to the Nigerian Meteorological Agency (NIMET), Oshodi, Lagos, Nigeria, for providing data for this study.