# Exploring Wind Energy Potential as a Driver of Sustainable Development in the Southern Coasts of Iran: The Importance of Wind Speed Statistical Distribution Model

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## Abstract

**:**

## 1. Introduction

#### 1.1. Wind Energy in Iran

^{2}(about half of this area is habitable), and largely depends on fossil fuels such as crude oil in its energy sector [7]. Now, there is a considerable consumption of traditional energy resources in Iran because of the large amount of low-cost fossil fuels [8]. This exacerbated the misuse of energy in industrial, transportation, and home sectors resulting in various environmental problems [9]. The situation compelled energy policymakers to move toward renewable energies in the country. The average growth rate of energy consumption and generation in Iran is 4% and 2%, respectively and therefore, it is expected that Iran increasingly will need to provide a great share of its energy demand from renewable energy sources in years ahead, to meet future rising energy demand [10]. As set out in the 6th national development plan of the country, the Iranian government has the target of extracting 5000 MW from renewable energy resources by 2020 [11]. Due to this high demand, extensive studies on different types of renewable energy should be considered [12].

#### 1.2. Review of the Literature

## 2. Area of Interest

## 3. Analysis

#### 3.1. Wind Speed Distribution Models

- Graphical method or Least squares algorithm [60]
- Maximum likelihood method (MLE) [61]
- Moments Method (MM) [61]
- Standard deviation method [63]
- Empirical method of Jestus [64]
- Empirical method of Lysen [65]
- Equivalent energy method [62]
- Energy pattern factor method (power density method) [66]
- WAsP method [49]

#### 3.1.1. Wind Speed Extrapolation

#### 3.1.2. Goodness of Fit Tests

^{2}) is used to measure the linear relationship between the observed and predicted probabilities. Additionally, root-mean-square error (RMSE) is used to show the level of concentration of data around the fitted distribution. Moreover, because of using the MLE method for parameter estimation Akaike information criterion (AIC) and Bayesian information criterion (BIC) is used to assess the accuracy of the fitted distribution. Table 5 presents the formulae and definitions of parameters for each of these four statistical indicators. Lower values for RMSE, AIC, and BIC indicate higher goodness of fit, while on the contrary, a larger value for R

^{2}shows better effectiveness of the fitted distribution.

#### 3.2. Wind Power and Energy Density

#### 3.3. Capacity Factor

_{f}) of a wind turbine is an indicator that defines the output viability of a wind turbine at a selected station. It determines the ratio of average power yield to the rated power of the turbine. C

_{f}is one the most reliable measures for choosing wind turbine because it inherently shows the performance of the wind turbine. C

_{f}can be expressed as [53]:

#### 3.4. Availability Factor

## 4. Results and Discussion

#### 4.1. Analysis of Distribution Functions

^{2}shows a better correlation between observed data and fitted distribution. Note that different GoF indicators can yield different results. For example, in the S3 station, Gamma performs better in terms of R

^{2}, whereas Lognormal performs better in terms of RMSE. This paper assigns R

^{2}a greater weight for the assessment and selects it as the first reference index. Results show that Gamma is the best distribution for S1, S3, S5, and GEV has the best fit for S2, S4, and S6. Weibull is only suitable for S7. Results show that R

^{2}values for Weibull distribution are 1 to 7 % lower than that of the best distribution in stations S1 to S6.

#### 4.2. Analysis of Wind Power and Energy Density

#### 4.3. Wind Turbine Selection

#### 4.4. Comparison with Previous Studies

^{2}in that paper are 0.923, 0.910, and 0.901, respectively, which are consistent with R

^{2}of the current study with the values 0.926, 0.924, and 0.917. Furthermore, in the current study, GEV has the best performance in terms of R

^{2}value and has R

^{2}= 0.988.

^{2}is not published. While, in this study, Gamma function is selected for this location with R

^{2}= 0.969.

^{2}for Weibull distribution was 0.986, while Gamma distribution has the highest R

^{2}equal to 0.993 in the current study. Although R

^{2}is slightly increased, different data sources should be considered. Another study in the region is performed by Mohammadi et al. [35] based on long-term data from 2002 to 2009. Again, Weibull distribution opted. R

^{2}was not calculated. Wind power density at the height of 10m reported 111 W/m

^{2}, while this research calculated 144 W/m

^{2}using Gamma density function with R

^{2}equal to 0.993.

^{2}reported 0.9782 while in current research GEV function was the most suitable for the region with R

^{2}= 0.986. Weibull distribution ranked third among six PDFs with R

^{2}= 0.975, which is in line with the previous study.

^{2}was not reported [41]. Mohammadi et al. studied data from 2002 to 2009 and used Weibull distribution [35]. Again, R

^{2}was not announced. Wind power density at the height of 10 m is calculated 111 W/m

^{2}while in the current study, using Gamma function, wind power density is determined as 119 W/m

^{2}which is slightly higher. This distinction might cause due to different wind speed data and distribution used. Moreover, Alavi et al. studied the station with data from 2008 to 2009. They conducted analysis using Weibull, Gamma, Lognormal, and GEV functions with R

^{2}= 0.999, 0.999, 0.998, and 0.999 respectively which are significantly high. Additionally, Nakagami distribution function yielded the best fitness with R

^{2}= 0.9999. Accordingly, in the current study, R

^{2}for those distributions are 0.994, 0.984, 0.923, and 0.969, respectively. Apart from Nakagami distribution, Weibull shows the best fitness in both studies and R

^{2}of the two analyses are approximately equal. The negligible difference might occur because of different data. It should be noted that this region is very important in the development programs of Iran and therefore has great potential for the construction of coastal and offshore structures [93,94].

^{2}in comparison with previous studies. Moreover, in other sections of the analysis, a more precise approach is conducted to compute capacity factors. Additionally, to increase the practicality of the article, a broad range of wind turbines are considered to analysis to obtain a more concrete insight toward wind energy capacity in the south coastal zone of Iran.

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Conflicts of Interest

## Abbreviations

GEV | Generalized Extreme Value |

IG | Inverse Gaussian |

OWC | Oscillating Water Column |

WEC | Wave Energy Converter |

WRA | Wind Resource Assessment |

WSC | wind shear coefficient |

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**Figure 2.**Location of seven stations across the southern coasts of Iran, under study (credit: [13], raw map from “Map data ©2019 Google”).

**Figure 3.**Wind rose diagrams for stations (Wind roses plotted using SATBA data in highcharts.com [46]).

**Figure 5.**Schematic power curve of a wind turbine, reproduced with better quality from [88].

Year | Ref. | Distribution (s) | Method of Estimation | Case Study Location | Coastal City? |
---|---|---|---|---|---|

2010 | [26] | Weibull | Method of Moments | Tehran city | No |

2011 | [27] | Weibull | Empirical method | North and South Khorasan provinces | No |

2011 | [28] | Weibull | Empirical method | Semnan province | No |

2011 | [29] | Weibull | Method of Moments | Sharbabak city | No |

2012 | [30] | Weibull | Not mentioned | Abadan city | Yes |

2013 | [31] | Weibull | Empirical method | Kish and Jask regions | Yes |

2013 | [32] | Weibull | standard deviation method | Kerman province | No |

2013 | [33] | Weibull | standard deviation method | Aligoodarz city | No |

2014 | [20] | Weibull, Lognormal, Rayleigh, Logistic | graphical method, Maximum likelihood, Method of moments | Mahshahr city | Yes |

2014 | [34] | Weibull | standard deviation method | Mil-E Nader region | No |

2014 | [35] | Weibull | Empirical method | Chabahar, Kish and Salafchegan | Yes |

2014 | [36] | Weibull | Empirical method | Zahedan city | No |

2015 | [37] | Weibull | Method of Moments | Firouzkooh city | No |

2015 | [38] | Weibull | Method of Moments | Tabriz and Ardabil cities | No |

2016 | [22] | gamma, lognormal, Rayleigh, Weibull | Maximum likelihood, Method of moments | Bam, Bardsir, Arzuiyeh, Rafsanjan, Shahrbabak | No |

2016 | [39] | Weibull | Not mentioned | Kahnuj city | No |

2016 | [40] | Weibull | standard deviation method | Asaluyeh, Bordkhoon, Delvar, Haft-Chah | Yes |

2016 | [23] | Weibull | graphical method, Maximum likelihood, Method of moments | Gulf of Oman | Yes |

2017 | [41] | Weibull | maximum likelihood | Chabahar, Dehak and Dalgan | Yes (Chabahar) |

2017 | [42] | Weibull | Not mentioned | Fars province | No |

2017 | [3] | Weibull | standard deviation method | Zabol, Zahak, Zahedan and Mirjaveh cities | No |

2018 | [24] | Weibull | Standard deviation method, Empirical method of Lysen, Power density method | Nine central provinces | No |

2018 | [43] | Weibull | standard deviation method | provinces of East Azerbaijan, West Azerbaijan and Ardabil | No |

2018 | [25] | 46 different functions | Not mentioned | Shurje region, Qazvin Province | No |

2019 | [44] | Weibull | Empirical method | Lotak and Shandol | No |

2020 | [45] | Weibull | Maximum-likelihood | Persian Gulf | No |

**Table 2.**Location and properties of studied wind data [46].

Station | Designate | Lat. (N) | Long. (E) | Data Period | Time Interval | Recorded Data | Data Statistics | ||
---|---|---|---|---|---|---|---|---|---|

Mean | SD | Max. | |||||||

Abadan | S1 | 30.447 | 48.306 | 2007–2009 | 10-min | 90,656 | 4.35 | 2.51 | 19.76 |

Mahshahr | S2 | 30.579 | 49.086 | 2007–2009 | 10-min | 91,923 | 4.44 | 2.41 | 21.46 |

Delvar | S3 | 28.835 | 51.046 | 2006–2008 | 10-min | 72,186 | 3.40 | 2.14 | 15.92 |

Bordekhoon | S4 | 27.985 | 51.492 | 2006–2008 | 10-min | 82,492 | 4.87 | 2.73 | 19.93 |

Kish | S5 | 26.553 | 53.910 | 2006–2008 | 10-min | 81,217 | 4.59 | 2.81 | 22.38 |

Jask | S6 | 25.685 | 58.109 | 2006–2007 | 10-min | 59,518 | 3.44 | 2.04 | 20.82 |

Chabahar | S7 | 25.328 | 60.663 | 2008–2009 | 10-min | 73,296 | 4.97 | 2.14 | 15.41 |

Name | Probability Distribution Functions | Parameters |
---|---|---|

Weibull [57] | $f\left(v\right)=\frac{k}{c}.{\left(\frac{v}{c}\right)}^{k-1}.{e}^{-{\left(\frac{V}{c}\right)}^{k}}$ | k: shape c: scale |

Rayleigh [37] | $f\left(v\right)=\frac{2v}{{c}^{2}}.{e}^{-{\left(\frac{V}{c}\right)}^{2}}$ | c: scale |

Lognormal [27] | $f\left(v\right)=\frac{1}{c.v.\sqrt{2\pi}}exp\left[-\frac{1}{2}{\left(\frac{ln\left(v\right)-k}{c}\right)}^{2}\right]$ | k: shape c: scale |

Gamma [58] | $f\left(v\right)=\frac{{v}^{k-1}}{\mathsf{\Gamma}\left(k\right).{c}^{k}}exp\left(-\frac{v}{c}\right)$ | k: shape c: scale |

Inverse Gaussian [22] | $f\left(v\right)={\left(\frac{k}{2\pi {v}^{3}}\right)}^{\frac{1}{2}}.exp\left[-\frac{k{\left(v-c\right)}^{2}}{2v{c}^{2}}\right]$ | k: shape c: scale |

Generalized Extreme Value [2] | $f\left(v\right)=\frac{1}{\sigma}{\left(1+k\frac{v-\mu}{\sigma}\right)}^{-1-\frac{1}{k}}exp\left[-{\left(1+k\frac{v-\mu}{\sigma}\right)}^{\frac{-1}{k}}\right]$ | k: shape $\sigma $: scale $\mu $: location |

**Table 4.**Wind Shear Coefficient (WSC) [85].

Terrain Type | WSC |
---|---|

Lake, ocean and smooth hard ground | 0.10 |

Foot high grass on ground level | 0.15 |

Tall crops, hedges, and shrubs | 0.20 |

Wooded country | 0.25 |

Small town with some trees and shrubs | 0.30 |

City area with tall buildings | 0.40 |

Indicator | Formula | Parameters |
---|---|---|

R^{2} | ${R}^{2}=1-\frac{{\sum}_{i=1}^{n}{\left({y}_{i}-{y}_{ic}\right)}^{2}}{{\sum}_{i=1}^{n}{\left({y}_{i}-\overline{y}\right)}^{2}}$ | ${y}_{i}$: Observed data ${y}_{ic}$: fitted data n: Number of data samples. |

RMSE | $RMSE={\left[\frac{1}{n}{\sum}_{i=1}^{n}{\left({y}_{i}-{y}_{ic}\right)}^{2}\right]}^{\frac{1}{2}}$ | ${y}_{i}$: Observed data ${y}_{ic}$: fitted data n: number of data samples |

AIC | $AIC=-2log\left(L\right)+2k$ | L: likelihood k: number of parameters |

BIC | $BIC=-2log\left(L\right)+klogn$ | L: likelihood k: number of parameters n: number of data samples |

Wind Power Class | Power Density (W/m^{2}) | Description |
---|---|---|

1 | 0–200 | Unsuitable for any wind applications |

2 | 200–300 | Suitable for Stand-alone |

3 | 300–400 | Good |

4 | 400–500 | Good |

5 | 500–600 | Excellent |

6 | 600–800 | Outstanding |

7 | 800–2000 | Superb |

**Table 7.**Estimated parameters of probability distribution functions of the stations at height of 10 m.

Station | Distribution | |||||
---|---|---|---|---|---|---|

Weibull | Gamma | Lognormal | GEV | Rayleigh | IG | |

S1 | k = 1.837, c = 4.910 | k = 3.019, c = 1.440 | LL = 1.295, LS = 0.625 | k = 0.0702, sigma = 1.825, mu = 3.157 | c = 3.548 | k = 8.798, c = 4.347 |

S2 | k = 1.942, c = 5.012 | k = 3.390, c = 1.309 | LL = 1.33541, LS = 0.607406 | k = 0.032, sigma = 1.791, mu = 3.345 | c = 3.569 | k = 4.919, c = 4.438 |

S3 | k = 1.677, c = 3.821 | k = 2.540, c = 1.338 | LL = 1.014, LS = 0.694 | k = 0.1432, sigma = 1.454, mu = 2.334 | c = 2.841 | k = 4.495, c = 3.399 |

S4 | k = 1.899, c = 5.508 | k = 3.392, c = 1.436 | LL = 1.428, LS = 0.659 | k = 0.077, sigma = 1.928, mu = 3.597 | c = 3.946 | k = 11.435, c = 4.869 |

S5 | k = 1.720, c = 5.164 | k = 2.620, c = 1.752 | LL = 1.321, LS = 0.690 | k = 0.093, sigma = 1.997, mu = 3.232 | c = 3.805 | k = 5.579, c = 4.589 |

S6 | k = 1.755, c = 3.862 | k = 2.625, c = 1.310 | LL = 1.032, LS = 0.723 | k = 0.042, sigma = 1.522, mu = 2.492 | c = 2.826 | k = 1.955, c = 3.438 |

S7 | k = 2.474, c = 5.605 | k = 4.780, c = 1.040 | LL = 1.495, LS = 0.503 | k = -0.124, sigma = 1.908, mu = 4.075 | c = 3.827 | k = 14.317, c = 4.971 |

Station | Distribution Function | R2 | RMSE | AIC | BIC | ||||
---|---|---|---|---|---|---|---|---|---|

Value | Rank | Value | Rank | Value | Rank | Value | Rank | ||

S1 | Weibull | 0.953 | 4 | 0.045 | 4 | 404,569 | 3 | 404,551 | 3 |

Gamma | 0.989 | 1 | 0.026 | 1 | 401,817 | 1 | 401,798 | 1 | |

Lognormal | 0.976 | 2 | 0.397 | 3 | 406,898 | 5 | 406,879 | 4 | |

GEV | 0.972 | 3 | 0.345 | 2 | 402,987 | 2 | 402,959 | 2 | |

Rayleigh | 0.943 | 5 | 0.049 | 5 | 405,758 | 4 | 405,766 | 5 | |

IG | 0.921 | 6 | 0.058 | 6 | 412,268 | 6 | 412,276 | 6 | |

S2 | Weibull | 0.924 | 3 | 0.061 | 4 | 406,040 | 3 | 406,059 | 3 |

Gamma | 0.959 | 2 | 0.045 | 2 | 403,145 | 2 | 403,164 | 2 | |

Lognormal | 0.917 | 4 | 0.064 | 5 | 414,720 | 5 | 414,739 | 5 | |

GEV | 0.988 | 1 | 0.029 | 1 | 400,500 | 1 | 400,528 | 1 | |

Rayleigh | 0.924 | 3 | 0.059 | 3 | 406,188 | 4 | 406,196 | 4 | |

IG | 0.516 | 5 | 0.15 | 6 | 406,188 | 4 | 406,196 | 4 | |

S3 | Weibull | 0.913 | 4 | 0.068 | 3 | 295,197 | 3 | 295,215 | 3 |

Gamma | 0.969 | 1 | 0.049 | 2 | 293,315 | 1 | 293,334 | 1 | |

Lognormal | 0.955 | 2 | 0.041 | 1 | 298,399 | 4 | 298,418 | 4 | |

GEV | 0.929 | 3 | 0.076 | 4 | 294,194 | 2 | 294,222 | 2 | |

Rayleigh | 0.857 | 6 | 0.09 | 6 | 299,472 | 5 | 299,479 | 5 | |

IG | 0.877 | 5 | 0.084 | 5 | 315,908 | 6 | 315,916 | 6 | |

S4 | Weibull | 0.924 | 4 | 0.055 | 4 | 381,906 | 5 | 381,888 | 5 |

Gamma | 0.976 | 2 | 0.031 | 2 | 377,063 | 2 | 377,082 | 2 | |

Lognormal | 0.963 | 3 | 0.039 | 3 | 380,270 | 4 | 380,289 | 4 | |

GEV | 0.992 | 1 | 0.023 | 1 | 376,076 | 1 | 376,104 | 1 | |

Rayleigh | 0.924 | 4 | 0.055 | 4 | 377,578 | 3 | 377,586 | 3 | |

IG | 0.918 | 5 | 0.057 | 5 | 377,578 | 3 | 377,586 | 3 | |

S5 | Weibull | 0.967 | 4 | 0.035 | 3 | 378,525 | 3 | 378,544 | 3 |

Gamma | 0.993 | 1 | 0.016 | 1 | 377,071 | 1 | 377,089 | 1 | |

Lognormal | 0.974 | 2 | 0.032 | 2 | 384,730 | 5 | 384,748 | 5 | |

GEV | 0.973 | 3 | 0.04 | 4 | 378,108 | 2 | 378,136 | 2 | |

Rayleigh | 0.925 | 5 | 0.052 | 5 | 382,014 | 4 | 382,022 | 4 | |

IG | 0.806 | 6 | 0.083 | 6 | 412,722 | 6 | 412,729 | 6 | |

S6 | Weibull | 0.975 | 3 | 0.038 | 3 | 241,574 | 2 | 241,592 | 2 |

Gamma | 0.985 | 2 | 0.028 | 1 | 241,851 | 3 | 241,869 | 3 | |

Lognormal | 0.937 | 5 | 0.06 | 5 | 253,252 | 5 | 253,270 | 5 | |

GEV | 0.986 | 1 | 0.035 | 2 | 240,873 | 1 | 240,900 | 1 | |

Rayleigh | 0.958 | 4 | 0.048 | 4 | 243,486 | 4 | 243,493 | 4 | |

IG | 0.246 | 6 | 0.205 | 6 | 313,354 | 6 | 313,361 | 6 | |

S7 | Weibull | 0.994 | 1 | 0.015 | 1 | 315,837 | 3 | 315,856 | 2 |

Gamma | 0.984 | 4 | 0.024 | 4 | 317,647 | 2 | 317,666 | 3 | |

Lognormal | 0.923 | 6 | 0.053 | 6 | 326,363 | 5 | 32,632 | 5 | |

GEV | 0.969 | 5 | 0.033 | 5 | 315,762 | 1 | 315,789 | 1 | |

Rayleigh | 0.99 | 3 | 0.021 | 3 | 320,866 | 4 | 320,874 | 4 | |

IG | 0.991 | 2 | 0.02 | 2 | 341,748 | 6 | 341,756 | 6 |

**Table 9.**Estimated wind power density and wind energy density and classification of the stations for the wind power based on NREL.

Station | WPD (W/m^{2}) | WED ($\frac{\mathit{kWh}}{{\mathit{m}}^{2}}/\mathit{year}$ ) | Class | |||||
---|---|---|---|---|---|---|---|---|

10 m | 30 m | 50 m | 10 m | 30 m | 50 m | |||

S1 | Gamma | 111 | 215 | 292 | 972 | 1883 | 2562 | 2 |

S2 | GEV | 112 | 216 | 295 | 981 | 1892 | 2584 | 2 |

S3 | Gamma | 111 | 214 | 293 | 972 | 1875 | 2567 | 2 |

S4 | GEV | 161 | 311 | 423 | 1410 | 2724 | 3705 | 4 |

S5 | Gamma | 144 | 279 | 379 | 1261 | 2444 | 3319 | 3 |

S6 | GEV | 61 | 117 | 160 | 534 | 1025 | 1402 | 1 |

S7 | Weibull | 119 | 231 | 314 | 1042 | 2024 | 2751 | 3 |

**Table 10.**Characteristics of wind turbines used in the study [91].

Turbine Model | Name | Rated Power Output (MW) | Hub Height (m) | Cut-in Wind Speed (m/s) | Rated Wind Speed (m/s) | Cut-Out Wind Speed (m/s) | Swept Area (m^{2}) |
---|---|---|---|---|---|---|---|

Vestas V15 | T1 | 0.055 | 20 | 4 | 12.5 | 25 | 176 |

AIRCON 10 S | T2 | 0.0098 | 30 | 3.5 | 11 | 25 | 39.6 |

Enercon E-12 | T3 | 0.03 | 30 | 3 | 11 | 35 | 113 |

Enercon E-44 | T4 | 0.9 | 45 | 3 | 16.5 | 34 | 1521 |

Enercon E-30 | T5 | 0.3 | 50 | 2.5 | 13.5 | 25 | 707 |

Goldwind S43/600 | T6 | 0.6 | 50 | 3 | 14 | 25 | 1452 |

Vestas V52 | T7 | 0.85 | 55 | 4 | 14 | 25 | 2124 |

Goldwind S50/750 | T8 | 0.75 | 60 | 3.5 | 14.5 | 25 | 1964 |

Nordex N54 | T9 | 1 | 60 | 3.5 | 14 | 25 | 2290 |

Suzlon S.33-350 | T10 | 0.35 | 70 | 3.5 | 14 | 25 | 876.1 |

United Power UP2000-97 | T11 | 2 | 80 | 3 | 10.1 | 25 | 7390 |

Goldwind GW 62/1200 | T12 | 1.2 | 85 | 3 | 12.5 | 25 | 3000 |

Envision EN106-1.8 | T13 | 1.8 | 90 | 3 | 9.5 | 20 | 8825 |

General Electric GE 1.6-100 | T14 | 1.6 | 100 | 3.5 | 11 | 25 | 7854 |

Senvion 4.2M118 | T15 | 4.2 | 100 | 3 | 12.5 | 22 | 10,936 |

Station | Turbine | T2 | T3 | T11 | T12 | T13 | T14 | T15 |
---|---|---|---|---|---|---|---|---|

S1 | Capacity factor | - | - | 0.360 | 0.258 | 0.401 | 0.330 | 0.278 |

Annual energy output (MWh) | - | - | 6312 | 2710 | 6315 | 4630 | 10223 | |

S2 | Capacity factor | - | - | 0.367 | 0.258 | 0.412 | 0.335 | 0.279 |

Annual energy output (MWh) | - | - | 6432 | 2716 | 6489 | 4691 | 10273 | |

S4 | Capacity factor | - | 0.260 | 0.416 | 0.302 | 0.455 | 0.382 | 0.320 |

Annual energy output (MWh) | - | 68 | 7282 | 3172 | 7168 | 5358 | 11783 | |

S5 | Capacity factor | - | - | 0.388 | 0.298 | 0.421 | 0.359 | 0.304 |

Annual energy output (MWh) | - | - | 6796 | 3136 | 6631 | 5027 | 11170 | |

S7 | Capacity factor | 0.264 | 0.279 | 0.481 | 0.336 | 0.540 | 0.444 | 0.368 |

Annual energy output (MWh) | 23 | 73 | 8423 | 3530 | 8517 | 6218 | 13,538 |

**Table 12.**Estimated Availability factor of wind turbines for each turbine across the seven stations.

S1 | S2 | S3 | S4 | S5 | S6 | S7 | |
---|---|---|---|---|---|---|---|

T1 | 0.570 | 0.604 | 0.366 | 0.653 | 0.585 | 0.410 | 0.735 |

T2 | 0.695 | 0.741 | 0.487 | 0.778 | 0.698 | 0.542 | 0.834 |

T3 | 0.733 | 0.816 | 0.577 | 0.847 | 0.766 | 0.630 | 0.884 |

T4 | 0.802 | 0.848 | 0.622 | 0.875 | 0.797 | 0.672 | 0.904 |

T5 | 0.809 | 0.907 | 0.724 | 0.924 | 0.860 | 0.765 | 0.941 |

T6 | 0.731 | 0.855 | 0.630 | 0.879 | 0.803 | 0.683 | 0.908 |

T7 | 0.687 | 0.733 | 0.478 | 0.771 | 0.691 | 0.535 | 0.830 |

T8 | 0.761 | 0.809 | 0.564 | 0.838 | 0.758 | 0.621 | 0.879 |

T9 | 0.761 | 0.081 | 0.564 | 0.838 | 0.758 | 0.638 | 0.879 |

T10 | 0.774 | 0.822 | 0.581 | 0.849 | 0.770 | 0.727 | 0.888 |

T11 | 0.842 | 0.884 | 0.678 | 0.903 | 0.833 | 0.726 | 0.926 |

T12 | 0.731 | 0.884 | 0.678 | 0.903 | 0.839 | 0.730 | 0.926 |

T13 | 0.844 | 0.884 | 0.682 | 0.897 | 0.829 | 0.736 | 0.930 |

T14 | 0.803 | 0.848 | 0.619 | 0.872 | 0.795 | 0.675 | 0.905 |

T15 | 0.731 | 0.893 | 0.696 | 0.906 | 0.840 | 0.746 | 0.934 |

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**MDPI and ACS Style**

Filom, S.; Radfar, S.; Panahi, R.; Amini, E.; Neshat, M. Exploring Wind Energy Potential as a Driver of Sustainable Development in the Southern Coasts of Iran: The Importance of Wind Speed Statistical Distribution Model. *Sustainability* **2021**, *13*, 7702.
https://doi.org/10.3390/su13147702

**AMA Style**

Filom S, Radfar S, Panahi R, Amini E, Neshat M. Exploring Wind Energy Potential as a Driver of Sustainable Development in the Southern Coasts of Iran: The Importance of Wind Speed Statistical Distribution Model. *Sustainability*. 2021; 13(14):7702.
https://doi.org/10.3390/su13147702

**Chicago/Turabian Style**

Filom, Siyavash, Soheil Radfar, Roozbeh Panahi, Erfan Amini, and Mehdi Neshat. 2021. "Exploring Wind Energy Potential as a Driver of Sustainable Development in the Southern Coasts of Iran: The Importance of Wind Speed Statistical Distribution Model" *Sustainability* 13, no. 14: 7702.
https://doi.org/10.3390/su13147702