# Technical and economic assessment of wind power potential of Nooriabad, Pakistan

- Zahid Hussain Hulio
^{1}Email author, - Wei Jiang
^{1}and - S. Rehman
^{2}

**7**:35

https://doi.org/10.1186/s13705-017-0137-9

© The Author(s). 2017

**Received: **17 May 2017

**Accepted: **6 October 2017

**Published: **6 November 2017

## Abstract

### Background

Pakistan is a developing nation and heavily spends on the development of conventional power plants to meet the national energy demand. The objective of this paper is to investigate wind power potential of site using wind speed, wind direction, and other meteorological data collected over a period of 1 year. This type of detailed investigation provides information of wind characteristics of potential sites and helps in selecting suitable wind turbine.

### Methods

The site-specific air density, wind shear, wind power density, annual energy yield, and capacity factors have been calculated at 30 and 50 m above the ground level (AGL). The Weibull parameters have been calculated using empirical (EM), maximum likelihood (MLM), modified maximum likelihood (MMLM), energy pattern (EPFM), and graphical (GM) methods to determine the other dependent parameters. The accuracies of these methods are determined using correlation coefficient (*R*
^{2}) and root mean square error (*RMSE*) values. At last, the wind energy economic analysis has been carried out at 30- and 50-m heights.

### Results

The annual mean wind speeds were found to be 5.233and 6.55 m/s at 30- and 50-m heights, respectively, with corresponding standard deviations of 2.295 and 2.176. All methods fitted very well with the measured wind speed data except GM model. The frequency of wind speed observed that Weibull distribution gave better fitting results than Rayleigh distribution at wind site. The mean wind power densities were found to be 169.4 and 416.7 W/m^{2} at 30- and 50-m heights. The economic analysis showed that at Nooriabad site in Pakistan, the wind energy can be produced at US$0.02189/kWh at a hub height of 50 m.

### Conclusions

The results showed that the site has potential to install utility wind turbines to generate energy at the lowest cost per kilowatt-hour at height of 50 m.

## Keywords

## Background

Energy resources are important for socio-economic development of a nation. To maintain status quo in terms of socio-economic development, states need economical and consistent supply of energy. Due to fluctuating and always increasing international oil prices, states try to find out alternative, natural, clean, and renewable sources of energy. These sources include but not limited to biogas, biomass, hydro, tidal, thermal, solar photovoltaic, and wind energy. The renewable energy sources are environment-friendly and available free of cost, have no political or geographical boundaries, are distributive in nature, and can be tapped anywhere. The Kyoto Protocol paved the way to global community to enhance power generation using new and renewable sources of energy. Of the above mentioned renewable sources of energy, wind and solar photovoltaic have been exploited in many countries and are contributing towards achieving the set goals of clean energy capacities.

*NREL*) prepared wind maps of the Pakistan, given in Fig. 3. Accordingly, Pakistan has 68,863 km

^{2}appropriate land; about 9.06% of total land can be used for wind farm development. Sindh and Baluchistan areas have been identified as wind corridors in the country. The coastal belt of Sindh has 2.5% of land which is favorable for production of wind energy [4].

Till 2003, Pakistan did not have any working power plant based on renewable sources of energy. Later on, due to rising prices of oil and gas, the government established “Alternate Energy Development Board” of Pakistan. The objective of this board was to develop, facilitate, educate, and promote the development and utilization of renewable sources of energy in a country. The board started collection of wind speed data in cooperation with meteorological department. Based on measured meteorological data, the board prepared short-, mid-, and long-term renewable energy deployment goals.

### Literature review

Mostafaeipour et al. [5] conducted feasibility study of wind energy of Shahrbabak, Kaman province, of Iran. The author used two-parameter Weibull distribution function for wind analysis and wind power density for energy generation and concluded that the site is suitable for the installation of small wind energy farm. Mostafaeipour [6] in another work conducted the feasibility study of wind potential of Yazd province of Iran. In this work, the author analyzed the 13-year wind data and used the measured data at the height of 10 m. The study suggested that the site is suitable for wind farm development.

Keyhani et al. [7] investigated the wind climate for the energy production at Tehran, the capital of Iran. In this work, the author used the two-parameter Weibull distribution function for seasonal wind analysis using measured data at the height of 10 m. Kwon [8] investigated the wind uncertainty of a Kwangyang Bay of Chonnam Peninsula of the southern coast of Korea and found 11% wind uncertainty. The author used probability models for wind variability including air density, surface roughness factor, wind speed, Weibull parameters, and error estimation of long-term wind speed based upon the Measure-Correlate-Predict method for uncertainty analysis.

Mohammadi and Mostafaeipour [9] estimated wind power potential of Zarinah and used the standard deviation and wind power density method to find accurate wind power density at the site. Mostafaeipour et al. [10] investigated the wind power potential of Binalood of Iran and concluded that the site has potential for the wind energy generation. Mirhosseini et al. [11] conducted feasibility study of five towns of Saman province of Iran. The study used the wind speed data collected at the heights of 10, 30, and 40 m respectively. Baseer et al. [12] analyzed the wind resources of seven locations of Jubail, Saudi Arabia. The author used maximum likelihood, least square regression method, and WAsP algorithm.

Dahmouni et al. [13] assessed the wind power potential of Borj Cedria in Tunisia using measured wind speeds at 10-, 20-, and 30-m heights above the ground level. The author used the seven 1.5 MW rated capacity wind turbines for the wind power potential of site. Li and Li [14] conducted wind potential of Waterloo, Canada. The authors carried out the annual, monthly, seasonal analysis of wind speed data for realistic wind energy assessment. Lashin and Shata [15] analyzed wind speed data on seasonal, yearly, and monthly basis for energy generation at Port Said in Egypt. The wind energy flux method was used to calculate annual and monthly mean wind speed. Himri et al. [16] investigated 8 years wind speed data of Tindouf, Algeria, for wind resource assessment. The authors used RET screen software and compared the energy production in terms of avoidance of oil results for clean energy. Đurišić and Mikulović [17] studied wind power potential of South Banat region, Serbia. The author developed mathematical model to estimate the vertical wind speed based upon the least square method and concluded that the site is suitable for wind farm.

Ouarda et al. [18] evaluated the wind speed with reference to probability density function and concluded the selection of appropriate pdf to minimize the wind power estimation error. The author used two-parameter Weibull distribution function, parametric models, mixture models, and one nonparametric method using kernel density method. Rehman and Al-Abbadi [19, 20] investigated wind shear and its effects on energy production and concluded the significant influence by the seasonal and diurnal changes. Firtin et al. [21] suggested that accurate determination of wind shear is critical to design and optimization of wind power investment. Wind turbines are constantly subjected to asymmetrical loads like wind shear which will lead to unsteady loading upon the blade and affect its performance [22].

Al-Abbadi [23] investigated wind potential of Yanbu, Saudi Arabia. The wind data analyzed for annual, seasonal, and diurnal and suggested that the site has potential for small wind turbines. Rehman et al. [24] studied wind power potential of seven sites of Saudi Arabia and used Weibull function to study wind characteristics at three different heights. Bassyouni et al. [25] analyzed the wind speed characteristics including daily, monthly, and annual wind speed, and wind probability density distribution, shape and scale parameters at 10-m height, based on 11 years wind data record of Jeddah city of Saudi Arabia.

This paper is focused on detailed analysis of wind speeds measured at 30 and 50 m above the ground level for a period of 1 year, i.e., 2009 to assess the wind power potential using two-parameter Weibull distribution function. The wind power density, annual wind energy, and capacity factor are calculated at data measurement heights. The Weibull shape and scale parameters are calculated using different methods like empirical, maximum likelihood, modified maximum likelihood, graphical, and energy pattern. The results are evaluated in terms of root mean square error (RMSE) and coefficient of determination *R*
^{2} values. The economic assessment is also carried out to estimate the cost of energy in order to select the best wind turbine.

### Wind data measurement site description

Specification of the wind speed and temperature sensors

Wind speed sensor (model #40) | Temperature sensor (model #110) | |
---|---|---|

Type | 3-cup anemometer | ICT with 6-plate radiation shield |

Maximum operating range | 1–96 m/s (2.2 to 214 mph) | − 40–52.5 °C (− 40–126.5 °F) |

Accuracy | ± 0.8% | ± 1.1 °C (2 °F) |

Temperature range | − 55–60 °C | − 40–52.5 °C (− 40–126.5 °F) |

Distance constant | 3.0 m (10 ft) | – |

Output signal range | 0–125 Hz | 0–2.5 V DC |

Weight | 0.14 kg (0.3 lb) | 0.47 kg (1.04 lb) |

## Methods

### Wind data analysis

Wind is the random variable and highly fluctuating meteorological parameter and changes with time of the day, day of the year, and year to year. Probability density function can be used to calculate the wind speed variation over a period of time. Wind data is essential for the investigation of wind potential of specific site, and energy generation can be estimated.

### Wind shear

*V*

_{1}is the wind speed at the height

*Z*

_{1},

*V*

_{2}is the wind speed at the height

*Z*

_{2}, and

*α*is the wind shear coefficient. The wind shear exponent of the site is also calculated using the following empirical equation [27]:

### Air density

where *p* refers to the air pressure(Pa or N/m^{2}), *R* refers to the specific gas constant for air (287 J/kg ), and *T* refers to the air temperature in Kelvin (*c* + 273^{°}).

### Weibull probability distribution function

*f*(

*v*) as follows [28, 29]:

*V*refers to the wind speed,

*k*refers to the shape parameter, and

*c*refers to the scale parameter. The cumulative distribution function

*F*(

*v*) is given as follows:

*k*is fixed as 2. The probability and cumulative distribution function can be represented following [30, 31] which is given as follows:

*V*

_{mean}potential. It can be calculated as given in [28] and the wind speed variance

*σ*

^{2}can be expressed as follows:

*Γ*is the gamma function and can be solved by following equation:

### Wind power density

*P*

_{ W }is the wind power,

*V*is the wind speed,

*p*is the air density, and

*A*

_{ T }is the swept area of the wind turbine blade. Betz theorem states that less than 59% (16/27) of the kinetic energy can be converted to mechanical energy using wind turbines. The Betz coefficient is denoted by

*C*

_{ p }and is expressed as follows:

*E*refers to the energy obtained,

*T*refers to the time period, and

*P*(

*V*) refers to the power curve of wind turbine. Substituting the values of Eq. 4 in Eq. 18, we can find the following equation:

The above equation is the Weibull distribution function for achieving the wind energy.

### Capacity factor

### Weibull parameter assessment methods

There are different parameter estimation methods to determine the Weibull parameters. The Weibull parameters are essential for analyzing the wind power potential and its characteristics. If Weibull parameter obtained precisely, the Weibull distribution not only provides better fit results but also represents wind power potential accurately. The methods used for the estimation of Weibull parameters are given below.

### Empirical method

*k*and

*c*, are calculated by using Eqs. 21 and 22.

### Maximum likelihood method

*k*and

*c*. The shape

*k*and scale

*c*parameters are calculated as follows:

where *N* is number of wind speed data points and *V*
_{
i
} is a wind speed value for the *i*th data measurement.

### Modified maximum likelihood method

*k*and

*c*are used to determine the values of shape

*k*and scale

*c*parameters as follows:

### Energy pattern method

### Graphical method

*a*refers to slope and

*b*is the intercept line and can be obtained by least square regression method. The values of the shape and scale,

*k*and

*c*, can be calculated by [36]:

### Statistical error analysis and goodness for fit

### Economic analysis of wind turbine

*I*be the initial investment,

*C*

_{ om }the operation and maintenance cost which is

*n%*of initial investment, and

*T*the life time of the wind turbine. The discounted costs of operation and maintenance for life time

*T*of wind turbine for an initial year, net present worth (NPW), and the total cost (Tc) of the energy can be calculated by [5]:

*E*is the energy generated by wind turbines annually and is obtained using the Eq. 40 [5]:

## Results and discussion

In this paper, the site-specific technical and economic assessment of the wind power potential has been carried out for an industrial city Nooriabad in Pakistan. The measured wind data is considered for a period of 1 year from January 2009 to December 2009 at 30- and 50-m heights. The results of study are discussed below.

### Site-specific wind shear

Monthly mean wind shear coefficient at a measurement site

Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Mean |
---|---|---|---|---|---|---|---|---|---|---|---|---|

0.269 | 0.265 | 0.26 | 0.23 | 0.208 | 0.195 | 0.2 | 0.205 | 0.233 | 0.275 | 0.289 | 0.272 | 0.24 |

### Seasonal and diurnal variation of wind speed

Monthly mean diurnal variation of wind speed

Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Mean |
---|---|---|---|---|---|---|---|---|---|---|---|---|

4.26 | 4.45 | 4.62 | 6.05 | 7.92 | 9.50 | 8.22 | 8.30 | 6.08 | 4.01 | 3.55 | 4.11 | 5.92 |

### Monthly mean temperature and air density variations

^{3}over the data collection period. The seasonal values of air density were found to be 1.211, 1.183, 1.166, and 1.178 kg/m

^{3}during winter, spring, summer, and autumn, respectively.

### Wind speed variation at different heights

The seasonal average wind speed were found to be 4.495, 5.439, 6.748, and 4.251 m/s during winter, spring, summer, and autumn seasons at a height of 30 m, respectively. The average seasonal wind speeds at 50-m height were found to be 4.66, 7.11, 9.16, and 5.27 m/s in winter, spring, summer, and autumn, respectively. Higher wind speeds were noticed during summer season at both the heights. The wind speed was also analyzed using the concept of most probable wind speed. Accordingly, the highest values of 9.62 and 9.5 m/s of most probable winds were found in June at 30 and 50 m, respectively.

In this paper, two-parameter Weibull distribution function is used to assess the wind power potential at the measurement site and determine the effectiveness of different methods used for the estimation of shape and scale parameters. Weibull distribution function provides the better fit to measured wind speed data and effective in analyzing the wind potential for energy production. The value of scale parameter *c* also changes with time and location like the wind speed. The shape parameter is dimensionless parameter. A value of *k* between 1 and 2 is an indicative of low wind. If the value of *k* factor showing increasing tendency, the distribution can be considered as skewed to high level of winds. In simple words, both parameters have to be considered for getting the near accurate results.

*k*and

*c*parameters are estimated using five methods including empirical, maximum likelihood, graphical, modified maximum likelihood, and energy pattern for wind speed data measured at two heights 30 and 50 m. The values of shape and scale parameters obtained using five methods were used to fit the measured wind speed data in different wind speed bins, as shown in Fig. 10. It is evident from Fig. 10 that all the models fitted very well with the measured wind speed data except GM model. Figure 11 shows the comparison between Weibull and Rayleigh distribution of wind speeds over frequency distribution at 30- and 50-m heights. However, it is observed from Fig. 11 that Weibull distribution gave better fitting results than Rayleigh distribution for wind data gathered at the Nooriabad site.

*k*and

*c*and standard deviation at the two heights are summarized in Table 4. The average values of

*k*and

*c*parameters, obtained using entire data set, were found to be 2.4 and 5.90 m/s at 30 m and 3.25 and 7.39 m/s at 50 m, as given in Table 4. The monthly mean values of

*k*varied between a minimum of 1.757 and a maximum of 2.92 corresponding to November and June, respectively. The seasonal values of shape parameter were 2.337, 2.543, 2.456, and 2.280 in winter, spring, summer and autumn at 30 m, respectively. The maximum value of shape parameter

*k*was found to be 2.543 during the spring and the lowest 2.28 in autumn. Similarly, the monthly and seasonal mean values of shape parameter

*k*at 50-m height are also included in Table 4.

Site-specific wind speed, standard deviation, and Weibull *k* and *c* parameters for a period of a year from January–December 2009

PM | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Mean |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Height 30 m | |||||||||||||

| 4.38 | 4.607 | 4.392 | 5.47 | 6.455 | 9.62 | 5.98 | 4.644 | 5.589 | 3.626 | 3.54 | 4.498 | 5.233 |

| 2.246 | 2.241 | 1.789 | 2.406 | 2.729 | 3.038 | 2.646 | 2.37 | 2.438 | 1.506 | 2.032 | 2.11 | 2.295 |

| 2.057 | 2.71 | 2.66 | 2.432 | 2.537 | 2.92 | 2.394 | 2.055 | 2.497 | 2.588 | 1.757 | 2.246 | 2.4 |

| 4.942 | 5.198 | 4.955 | 6.172 | 7.283 | 10.85 | 6.747 | 5.24 | 6.307 | 4.0916 | 3.994 | 5.076 | 5.9 |

Height 50 m | |||||||||||||

| 4.92 | 4.84 | 5.18 | 7.1 | 9.05 | 9.5 | 9.1 | 8.89 | 7.28 | 4.55 | 3.98 | 4.22 | 6.55 |

| 2.349 | 2.67 | 2.369 | 2.145 | 2.255 | 2.282 | 2.256 | 2.051 | 2.157 | 1.965 | 1.57 | 2.049 | 2.176 |

| 2.217 | 1.926 | 2.254 | 3.884 | 4.299 | 4.32 | 4.296 | 4.56 | 3.957 | 2.461 | 2.643 | 2.177 | 3.25 |

| 5.551 | 5.4615 | 5.845 | 8.011 | 10.22 | 10.72 | 10.268 | 10.032 | 8.2148 | 5.134 | 4.491 | 4.761 | 7.392 |

The scale parameter values for each month over an entire data collection period and at both measurement heights are provided in Table 4. In general, higher values of *c* were observed in summer months and lower during winter time. Accordingly at 30 m, a minimum value of 3.99 m/s was observed in November while a maximum of 10.850 m/s in June. Similarly at 50-m height, the scale parameter values varied between a minimum of 4.491 m/s and maximum of 10.72 m/s corresponding to November and June months of the year. The average seasonal values of *c* parameter were found to be 5.072, 6.137, 7.612, and 4.797 m/s for winter, spring, summer, and autumn, respectively, at 30 m while 5.25, 8.025, 10.34, and 5.94 m/s at 50 m.

The standard deviation values over an entire data collection period are given in Table 4. The average standard deviation was found to be 2.295 and 2.176 for 1 year at 30 and 50 m. The seasonal calculation showed variation over a period of time. The standard deviation was found to be 2.199, 2.308, 2.684, and 1.992 in winter, spring, summer, and autumn at 30 m, respectively. Similarly, the standard deviation was found to be 2.356, 2.256, 2.196, and 1.897 in winter, spring, summer, and autumn at 50 m, respectively.

### Calculation of wind power density and energy

^{2}at 30-m height. The highest wind power density value was found to be 800 W/m

^{2}in June and lowest of 40 W/m

^{2}in November at 30 m. Similarly, the seasonal wind power density was found to be 82, 155, 360.6, and 80 W/m

^{2}in winter, spring, summer, and autumn at 30 m. The highest wind power density found in summer and lowest in autumn at 30 m. The average wind power density was found to be 416.7 W/m

^{2}at 50 m. The wind power density increased by 40.65% at 50 m compared to that of 30 m. The highest wind power density was found to be 974 W/m

^{2}while lowest 72 W/m

^{2}in June and November at 50 m. The seasonal wind power densities were found to be 116.4, 468.7, 876, and 205.7 W/m

^{2}in winter, spring, summer, and autumn at 50 m.

Wind power density (W/m^{2}) and energy density (kWh/m^{2}) at 50 and 30 m for a period of a year 2009

PM | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Mean |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

50 m | |||||||||||||

W/m | 135 | 129 | 158 | 406 | 842 | 974 | 856 | 798 | 438 | 107 | 72 | 85 | 416.7 |

kWh/m | 465 | 438 | 535 | 1096 | 1622 | 1648 | 1630 | 1587 | 1148 | 368 | 237 | 289 | 921.9 |

30 m | |||||||||||||

W/m | 76 | 88 | 76 | 147 | 242 | 800 | 192 | 90 | 157 | 43 | 40 | 82 | 169.4 |

kWh/m | 289 | 333 | 289 | 526 | 763 | 1438 | 649 | 342 | 552 | 167 | 149 | 316 | 484.4 |

The average wind energy density was found to be 484.4 kWh/m^{2} at 30 m as given in Table 5. The highest wind energy density was found to be 1438 kWh/m^{2} in June and the lowest of 149 kWh/m^{2} in November at 30 m. The average seasonal wind energy density was found to be 312.7, 526, 809.7, and 289.4 kWh/m^{2} during winter, spring, summer, and autumn at 30 m. The average wind energy density was found to be 921.9 kWh/m^{2} at 50 m. The wind energy density increased by 52.54% at 50 m compared to that of 30 m. The highest wind energy density was found to be 1648 kWh/m^{2} in June and the lowest 237 kWh/m^{2} in November at 50 m. The average seasonal wind energy density was found to be 397.4, 1084.4, 1621.7, and 584.4 kWh/m^{2} during winter, spring, summer, and autumn at 50 m. For 50 m, the highest energy density was found in summer and lowest in winter.

Wind turbines energy yield and capacity factor for a period of a year 2009

PM | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Mean |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Wind turbine 1 (1000 kW) | |||||||||||||

GWh | 0.94 | 0.9 | 1.1 | 2.45 | 3.75 | 4.01 | 3.8 | 3.68 | 2.57 | 0.75 | 0.5 | 0.85 | 25.4 |

CF | 12 | 12 | 14 | 30 | 45 | 46 | 45 | 43 | 32 | 10 | 6 | 9 | 25.33 |

Wind turbine 2 (900 kW) | |||||||||||||

GWh | 0.98 | 0.93 | 1.13 | 2.32 | 3.44 | 3.5 | 3.46 | 3.37 | 2.43 | 0.78 | 0.5 | 0.61 | 23.5 |

CF | 13 | 12 | 14 | 29 | 44 | 44 | 44 | 43 | 31 | 10 | 6 | 8 | 24.83 |

Wind turbine 3 (300 kW) | |||||||||||||

GWh | 0.253 | 0.291 | 0.253 | 0.46 | 0.67 | 1.26 | 0.57 | 0.3 | 0.48 | 0.14 | 0.13 | 0.27 | 5.09 |

CF | 10 | 11 | 10 | 18 | 25 | 48 | 22 | 11 | 18 | 6 | 5 | 11 | 16.25 |

Wind turbine 4 (250 kW) | |||||||||||||

GWh | 0.17 | 0.21 | 0.17 | 0.33 | 0.5 | 0.99 | 0.41 | 0.21 | 0.35 | 0.09 | 0.08 | 0.18 | 3.69 |

CF | 8 | 9 | 8 | 15 | 22 | 45 | 19 | 10 | 16 | 4 | 4 | 8 | 14 |

### Economic assessment

Economic analysis of wind turbines

E | CF | Cost/kWh (US$) | |
---|---|---|---|

WT 1 | 25.4 | 25.3 | 0.02189 |

WT 2 | 23.5 | 24.83 | 0.0228 |

WT 3 | 5.09 | 16.25 | 0.0336 |

WT 4 | 3.69 | 14 | 0.0387 |

The wind turbines have been selected and analyzed, according to their mechanical configuration. According to the results, the wind turbine 1 is capable of producing wind energy at the lowest value of US$0.02189/kWh, as given in Table 7.

## Conclusions

In this paper, the wind power potential of Nooriabad is studied by using the wind measurements for a period of 1 year in 2009 at 30- and 50-m heights. The mean wind shear coefficient and air density were found to be 0.24 and 1.189 kg/m^{3}, respectively. The measured mean wind speed was found to be 5.233 and 6.55 m/s at 30 and 50 m, respectively. The Weibull *k* parameter was found to be 2.4 and 3.24, while *c* parameter was found to be 5.9 and 7.392 at 30 and 50 m, respectively. The average values of standard deviation were found to be 2.295 and 2.176 at 30 and 50 m. In this paper, two-parameter Weibull distribution function is used to assess the wind power potential at the measurement site and determine the effectiveness of different methods used for the estimation of shape and scale parameters. Weibull distribution function provides the better fit to measured wind speed data and effective in analyzing the wind potential for energy production. The methods used including empirical (EM), maximum likelihood (MLM), modified maximum likelihood (MMLM), and energy pattern (EPEM) were fitted very well except the graphical method (GM). Weibull distribution gave better fitting results than Rayleigh distribution for wind data gathered at the Nooriabad site. The mean wind power density was found to be 169.9 and 416.7 W/m^{2} at 30 and 50 m. The annual energy density was found to be 484.4 and 921.94 kW/m^{2} at 30 and 50 m. Wind turbine 1 has the highest energy yield of 25.4 GWh and has the lowest cost of energy generation of US$0.02189/kWh. The assessment of wind potential shows that the site has potential for installation of wind turbines for energy generation.

## Declarations

### Authors’ contributions

The main idea of this paper was proposed and written by ZHH and WJ. The paper was thoroughly revised by SR. All authors read and approved the final manuscript.

### Competing interests

The authors declare that they have no competing interests.

### Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Open Access**This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

## Authors’ Affiliations

## References

- Global Wind Statistics, GWEC (2016) http://www.gwec.net/wp-content/uploads/2017/02/1_Global-Installed-.Wind-Power-Capacity-MW-%E2%80%93-Regional-Distribution.jpg. Accessed on 28 Feb 2017.
- Sheikh MA (2010) Energy and renewable energy scenario of Pakistan. Renew Sust Energ Rev 14(1):354–363View ArticleMathSciNetGoogle Scholar
- Shaikh F, Ji Q, Fan Y (2015) The diagnosis of an electricity crisis and alternative energy development in Pakistan. Renew Sust Energ Rev 52:1172–1185View ArticleGoogle Scholar
- Shami SH et al (2016) Evaluating wind energy potential in Pakistan’s three provinces, with proposal for integration into national power grid. Renew Sust Energ Rev 53:408–421View ArticleGoogle Scholar
- Mostafaeipour A et al (2011) Wind energy feasibility study for city of Shahrbabak in Iran. Renew Sust Energ Rev 15(6):2545–2556View ArticleGoogle Scholar
- Mostafaeipour A (2010) Feasibility study of harnessing wind energy for turbine installation in province of Yazd in Iran. Renew Sust Energ Rev 14(1):93–111View ArticleGoogle Scholar
- Keyhani A et al (2010) An assessment of wind energy potential as a power generation source in the capital of Iran. Tehran Energy 35(1):188–201MathSciNetGoogle Scholar
- Kwon S-D (2010) Uncertainty analysis of wind energy potential assessment. Appl Energy 87(3):856–865View ArticleGoogle Scholar
- Mohammadi K, Mostafaeipour A (2013) Using different methods for comprehensive study of wind turbine utilization in Zarrineh, Iran. Energy Convers Manag 65:463–470View ArticleGoogle Scholar
- Mostafaeipour A et al (2013) Evaluation of wind energy potential as a power generation source for electricity production in Binalood, Iran. Renew Energy 52:222–229View ArticleGoogle Scholar
- Mirhosseini M, Sharifi F, Sedaghat A (2011) Assessing the wind energy potential locations in province of Semnan in Iran. Renew Sust Energ Rev 15(1):449–459View ArticleGoogle Scholar
- Baseer M et al (2017) Wind power characteristics of seven data collection sites in Jubail, Saudi Arabia using Weibull parameters. Renew Energy 102:35–49View ArticleGoogle Scholar
- Dahmouni A et al (2011) Assessment of wind energy potential and optimal electricity generation in Borj-Cedria. Tunisia Renewable and Sustainable Energy Reviews 15(1):815–820View ArticleGoogle Scholar
- Li M, Li X (2005) Investigation of wind characteristics and assessment of wind energy potential for Waterloo region, Canada. Energy Convers Manag 46(18):3014–3033View ArticleGoogle Scholar
- Lashin A, Shata A (2012) An analysis of wind power potential in Port Said, Egypt. Renew Sust Energ Rev 16(9):6660–6667View ArticleGoogle Scholar
- Himri Y et al (2012) Wind energy for rural areas of Algeria. Renew Sust Energ Rev 16(5):2381–2385View ArticleGoogle Scholar
- Đurišić Ž, Mikulović J (2012) Assessment of the wind energy resource in the South Banat region, Serbia. Renew Sust Energ Rev 16(5):3014–3023View ArticleGoogle Scholar
- Ouarda T et al (2015) Probability distributions of wind speed in the UAE. Energy Convers Manag 93:414–434View ArticleGoogle Scholar
- Rehman S, Al-Abbadi NM (2005) Wind shear coefficients and their effect on energy production. Energy Convers Manag 46(15):2578–2591View ArticleGoogle Scholar
- Rehman S, Al-Abbadi NM (2008) Wind shear coefficient, turbulence intensity and wind power potential assessment for Dhulom, Saudi Arabia. Renew Energy 33(12):2653–2660View ArticleGoogle Scholar
- Fırtın E, Güler Ö, Akdağ SA (2011) Investigation of wind shear coefficients and their effect on electrical energy generation. Appl Energy 88(11):4097–4105View ArticleGoogle Scholar
- Shen X, Zhu X, Du Z (2011) Wind turbine aerodynamics and loads control in wind shear flow. Energy 36(3):1424–1434View ArticleGoogle Scholar
- Al-Abbadi NM (2005) Wind energy resource assessment for five locations in Saudi Arabia. Renew Energy 30(10):1489–1499View ArticleGoogle Scholar
- Rehman S et al (2012) Wind speed characteristics and resource assessment using Weibull parameters. Int J Green Energy 9(8):800–814View ArticleMathSciNetGoogle Scholar
- Bassyouni M et al (2015) Assessment and analysis of wind power resource using Weibull parameters. Energy Explor Exploit 33(1):105–122View ArticleGoogle Scholar
- Katinas V et al (2017) Statistical analysis of wind characteristics based on Weibull methods for estimation of power generation in Lithuania. Renew Energy 113:190–201. https://doi.org/10.1016/j.renene.2017.05.071.
- Manwell JF, McGowan JG, Rogers AL (2010) Wind energy explained: theory, design and application. John Wiley & Sons. Chichester, UK. doi:10.1002/9781119994367.
- Akpinar EK, Akpinar S (2005) An assessment on seasonal analysis of wind energy characteristics and wind turbine characteristics. Energy Convers Manag 46(11):1848–1867View ArticleGoogle Scholar
- Amirinia G, Mafi S, Mazaheri S (2017) Offshore wind resource assessment of Persian Gulf using uncertainty analysis and GIS. Renew Energy 113:915–929View ArticleGoogle Scholar
- Yaniktepe B, Koroglu T, Savrun M (2013) Investigation of wind characteristics and wind energy potential in Osmaniye. Turkey Renewable and Sustainable Energy Reviews 21:703–711View ArticleGoogle Scholar
- Gökçek M, Bayülken A, Bekdemir Ş (2007) Investigation of wind characteristics and wind energy potential in Kirklareli, Turkey. Renew Energy 32(10):1739–1752View ArticleGoogle Scholar
- Penchah MM, Malakooti H, Satkin M (2017) Evaluation of planetary boundary layer simulations for wind resource study in east of Iran. Renew Energy 111:1–10View ArticleGoogle Scholar
- Ucar A, Balo F (2009) Evaluation of wind energy potential and electricity generation at six locations in Turkey. Appl Energy 86(10):1864–1872View ArticleGoogle Scholar
- Akdağ SA, Dinler A (2009) A new method to estimate Weibull parameters for wind energy applications. Energy Convers Manag 50(7):1761–1766View ArticleGoogle Scholar
- Chang TP (2011) Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application. Appl Energy 88(1):272–282View ArticleGoogle Scholar
- Rocha PAC et al (2012) Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil. Appl Energy 89(1):395–400View ArticleGoogle Scholar
- Ullah I, Chipperfield AJ (2010) An evaluation of wind energy potential at Kati Bandar, Pakistan. Renew Sust Energ Rev 14(2):856–861View ArticleGoogle Scholar
- Association, D.W.T.M (1999) Guided tour on wind energy. Danish Wind Turbine Manufacturers AssociationGoogle Scholar