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

^{1}

^{2}

^{3}

^{4}

^{5}

^{*}

## 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 |

## References

- Amini, E.; Golbaz, D.; Amini, F.; Majidi Nezhad, M.; Neshat, M.; Astiaso Garcia, D. A parametric study of wave energy converter layouts in real wave models. Energies
**2020**, 13, 6095. [Google Scholar] [CrossRef] - Shukla, R.K.; Trivedi, M.; Kumar, M. On the proficient use of GEV distribution: A case study of subtropical monsoon region in India. arXiv
**2012**, arXiv:1203.0642. [Google Scholar] - Fazelpour, F.; Markarian, E.; Soltani, N. Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran. Renew. Energy
**2017**, 109, 646–667. [Google Scholar] [CrossRef] - GWEC. Global Wind Capacity Forecast to Hit 800 GW by 2021; GWEC: Brussels, Belgium, 2021; in press. [Google Scholar]
- IRENA. Global Energy Transformation: A Roadmap to 2050; International Renewable Energy Agency: Abu Dhabi, United Arab Emirates, 2018. [Google Scholar]
- Heravi, G.; Salehi, M.M.; Rostami, M. Identifying cost-optimal options for a typical residential nearly zero energy building’s design in developing countries. Clean Technol. Environ. Policy
**2020**, 22, 2107–2128. [Google Scholar] [CrossRef] - Radfar, S.; Panahi, R. Economic Analysis of Developing Tidal Stream Energy Farms in the South Coasts of Iran. Iran. J. Mar. Sci. Technol.
**2017**, 21, 41–47. [Google Scholar] - Amini, E.; Golbaz, D.; Asadi, R.; Nasiri, M.; Ceylan, O.; Majidi Nezhad, M.; Neshat, M. A Comparative Study of Metaheuristic Algorithms for Wave Energy Converter Power Take-Off Optimisation: A Case Study for Eastern Australia. J. Mar. Sci. Eng.
**2021**, 9, 490. [Google Scholar] [CrossRef] - Neshat, M.; Sergiienko, N.Y.; Amini, E.; Majidi Nezhad, M.; Astiaso Garcia, D.; Alexander, B.; Wagner, M. A New Bi-Level Optimisation Framework for Optimising a Multi-Mode Wave Energy Converter Design: A Case Study for the Marettimo Island, Mediterranean Sea. Energies
**2020**, 13, 5498. [Google Scholar] [CrossRef] - Radfar, S.; Panahi, R.; Javaherchi, T.; Filom, S.; Mazyaki, A.R. A comprehensive insight into tidal stream energy farms in Iran. Renew. Sustain. Energy Rev.
**2017**, 79, 323–338. [Google Scholar] [CrossRef] - Mills, R. The Politics of Low-Carbon Energy in Iran and Iraq. In Low Carbon Energy in the Middle East and North Africa; Springer: Berlin/Heidelberg, Germany, 2021; pp. 19–56. [Google Scholar]
- Amiri, A.; Panahi, R.; Radfar, S. Parametric study of two-body floating-point wave absorber. J. Mar. Sci. Appl.
**2016**, 15, 41–49. [Google Scholar] [CrossRef] - Wheeler, E.; Desai, M. Iran’s Renewable Energy Potential; Middle East Institute: Washington, DC, USA, 2016. [Google Scholar]
- Aien, M.; Mahdavi, O. On the Way of Policy Making to Reduce the Reliance of Fossil Fuels: Case Study of Iran. Sustainability
**2020**, 12, 10606. [Google Scholar] [CrossRef] - Karaminia, G.; Tavanpourpaveh, M.; Amini, F. Atlas of Energy, 3rd ed.; National Cartographic Center: Tehran, Iran, 2014. [Google Scholar]
- Renewable Energy and Energy Efficiency Organization. Renewable Atlas Coordinates and Current Status of the Stations; Renewable Energy and Energy Efficiency Organization: Abu Dhabi, United Arab Emirates, 2020. [Google Scholar]
- Korzeniowski, A.; Ghorbani, N. Put Options with Linear Investment for Hull-White Interest Rates. J. Math. Financ.
**2021**, 11, 152. [Google Scholar] [CrossRef] - Ghorbani, N.; Korzeniowski, A. Adaptive Risk Hedging for Call Options under Cox-Ingersoll- Ross Interest Rates. J. Math. Financ.
**2020**, 10, 697–704. [Google Scholar] [CrossRef] - Mohammadi, K.; Mostafaeipour, A. Using different methods for comprehensive study of wind turbine utilization in Zarrineh, Iran. Energy Convers. Manag.
**2013**, 65, 463–470. [Google Scholar] [CrossRef] - Nedaei, M.; Assareh, E.; Biglari, M. An extensive evaluation of wind resource using new methods and strategies for development and utilizing wind power in Mah-shahr station in Iran. Energy Convers. Manag.
**2014**, 81, 475–503. [Google Scholar] [CrossRef] - Alavi, O.; Sedaghat, A.; Mostafaeipour, A. Sensitivity analysis of different wind speed distribution models with actual and truncated wind data: A case study for Kerman, Iran. Energy Convers. Manag.
**2016**, 120, 51–61. [Google Scholar] [CrossRef] - Alavi, O.; Mohammadi, K.; Mostafaeipour, A. Evaluating the suitability of wind speed probability distribution models: A case of study of east and southeast parts of Iran. Energy Convers. Manag.
**2016**, 119, 101–108. [Google Scholar] [CrossRef] - Nedaei, M.; Ataei, A.; Adaramola, M.S.; Mirzahosseini, A.H.; Khalaji Assadi, M.; Assareh, E. Comparative analysis of three numerical methods for estimating the onshore wind power in a coastal area. Int. J. Ambient Energy
**2018**, 39, 58–72. [Google Scholar] [CrossRef] - Faghani, G.R.; Ashrafi, Z.N.; Sedaghat, A. Extrapolating wind data at high altitudes with high precision methods for accurate evaluation of wind power density, case study: Center of Iran. Energy Convers. Manag.
**2018**, 157, 317–338. [Google Scholar] [CrossRef] - Nedaei, M.; Assareh, E.; Walsh, P.R. A comprehensive evaluation of the wind resource characteristics to investigate the short term penetration of regional wind power based on different probability statistical methods. Renew. Energy
**2018**, 128, 362–374. [Google Scholar] [CrossRef] - Keyhani, A.; Ghasemi-Varnamkhasti, M.; Khanali, M.; Abbaszadeh, R. An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran. Energy
**2010**, 35, 188–201. [Google Scholar] [CrossRef] - Saeidi, D.; Mirhosseini, M.; Sedaghat, A.; Mostafaeipour, A. Feasibility study of wind energy potential in two provinces of Iran: North and South Khorasan. Renew. Sustain. Energy Rev.
**2011**, 15, 3558–3569. [Google Scholar] [CrossRef] - Mirhosseini, M.; Sharifi, F.; Sedaghat, A. Assessing the wind energy potential locations in province of Semnan in Iran. Renew. Sustain. Energy Rev.
**2011**, 15, 449–459. [Google Scholar] [CrossRef] - Mostafaeipour, A.; Sedaghat, A.; Dehghan-Niri, A.; Kalantar, V. Wind energy feasibility study for city of Shahrbabak in Iran. Renew. Sustain. Energy Rev.
**2011**, 15, 2545–2556. [Google Scholar] [CrossRef] - Nedaei, M. Wind resource assessment in Abadan airport in Iran. Int. J. Renew. Energy Dev.
**2012**, 1, 87–97. [Google Scholar] [CrossRef] - Nedaei, M. Wind resource assessment in Hormozgan province in Iran. Int. J. Sustain. Energy
**2014**, 33, 650–694. [Google Scholar] [CrossRef] - Mostafaeipour, A. Economic evaluation of small wind turbine utilization in Kerman, Iran. Energy Convers. Manag.
**2013**, 73, 214–225. [Google Scholar] [CrossRef] - Mohammadi, K.; Mostafaeipour, A. Economic feasibility of developing wind turbines in Aligoodarz, Iran. Energy Convers. Manag.
**2013**, 76, 645–653. [Google Scholar] [CrossRef] - Tizpar, A.; Satkin, M.; Roshan, M.; Armoudli, Y. Wind resource assessment and wind power potential of Mil-E Nader region in Sistan and Baluchestan Province, Iran–Part 1: Annual energy estimation. Energy Convers. Manag.
**2014**, 79, 273–280. [Google Scholar] [CrossRef] - Mohammadi, K.; Mostafaeipour, A.; Sabzpooshani, M. Assessment of solar and wind energy potentials for three free economic and industrial zones of Iran. Energy
**2014**, 67, 117–128. [Google Scholar] [CrossRef] - Mostafaeipour, A.; Jadidi, M.; Mohammadi, K.; Sedaghat, A. An analysis of wind energy potential and economic evaluation in Zahedan, Iran. Renew. Sustain. Energy Rev.
**2014**, 30, 641–650. [Google Scholar] [CrossRef] - Pishgar-Komleh, S.; Keyhani, A.; Sefeedpari, P. Wind speed and power density analysis based on Weibull and Rayleigh distributions (a case study: Firouzkooh county of Iran). Renew. Sustain. Energy Rev.
**2015**, 42, 313–322. [Google Scholar] [CrossRef] - Fazelpour, F.; Soltani, N.; Soltani, S.; Rosen, M.A. Assessment of wind energy potential and economics in the north-western Iranian cities of Tabriz and Ardabil. Renew. Sustain. Energy Rev.
**2015**, 45, 87–99. [Google Scholar] [CrossRef] - Soltani, N.; Fazelpour, F. Evaluation of wind energy potential and economics for the city of Kahnuj in Kerman Province, Iran. In Proceedings of the 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), Florence, Italy, 7–10 June 2016; pp. 1–6. [Google Scholar]
- Dabbaghiyan, A.; Fazelpour, F.; Abnavi, M.D.; Rosen, M.A. Evaluation of wind energy potential in province of Bushehr, Iran. Renew. Sustain. Energy Rev.
**2016**, 55, 455–466. [Google Scholar] [CrossRef] - Minaeian, A.; Sedaghat, A.; Mostafaeipour, A.; Alemrajabi, A.A. Exploring economy of small communities and households by investing on harnessing wind energy in the province of Sistan-Baluchestan in Iran. Renew. Sustain. Energy Rev.
**2017**, 74, 835–847. [Google Scholar] [CrossRef] - Rezaei-Shouroki, M.; Mostafaeipour, A.; Qolipour, M. Prioritizing of wind farm locations for hydrogen production: A case study. Int. J. Hydrogen Energy
**2017**, 42, 9500–9510. [Google Scholar] [CrossRef] - Bina, S.M.; Jalilinasrabady, S.; Fujii, H.; Farabi-Asl, H. A comprehensive approach for wind power plant potential assessment, application to northwestern Iran. Energy
**2018**, 164, 344–358. [Google Scholar] [CrossRef] - Teimourian, A.; Bahrami, A.; Teimourian, H.; Vala, M.; Oraj Huseyniklioglu, A. Assessment of wind energy potential in the southeastern province of Iran. Energy Sources Part A Recover. Util. Environ. Eff.
**2020**, 42, 329–343. [Google Scholar] [CrossRef] - Mahmoodi, K.; Ghassemi, H.; Razminia, A. Wind energy potential assessment in the Persian Gulf: A spatial and temporal analysis. Ocean Eng.
**2020**, 216, 107674. [Google Scholar] [CrossRef] - SATBA. Iran Resource Assessment; Renewable Energy and Energy Efficiency Organization: Abu Dhabi, United Arab Emirates, 2020. [Google Scholar]
- Hewson, E.W.; Wade, J.E.; Baker, R.W. Handbook on the Use of Trees as an Indicator of Wind Power Potential; Final Report, Technical Report; Department of Atmospheric Science, Oregon State University: Corvallis, OH, USA, 1979. [Google Scholar]
- Anjum, L. Wind resource estimation techniques—An overview. Int. J. Wind Renew. Energy
**2014**, 3, 26–38. [Google Scholar] - Murthy, K.; Rahi, O. A comprehensive review of wind resource assessment. Renew. Sustain. Energy Rev.
**2017**, 72, 1320–1342. [Google Scholar] [CrossRef] - Jung, C.; Schindler, D. The role of air density in wind energy assessment—A case study from Germany. Energy
**2019**, 171, 385–392. [Google Scholar] [CrossRef] - Patel, M.R. Wind and Solar Power Systems: Design, Analysis and Operation; CRC Press: Boca Raton, FL, USA, 2005. [Google Scholar]
- Wang, J.; Hu, J.; Ma, K. Wind speed probability distribution estimation and wind energy assessment. Renew. Sustain. Energy Rev.
**2016**, 60, 881–899. [Google Scholar] [CrossRef] - Ayodele, T.; Ogunjuyigbe, A.; Amusan, T. Wind power utilization assessment and economic analysis of wind turbines across fifteen locations in the six geographical zones of Nigeria. J. Clean. Prod.
**2016**, 129, 341–349. [Google Scholar] [CrossRef] - Soulouknga, M.; Doka, S.; Revanna, N.; Djongyang, N.; Kofane, T. Analysis of wind speed data and wind energy potential in Faya-Largeau, Chad, using Weibull distribution. Renew. Energy
**2018**, 121, 1–8. [Google Scholar] [CrossRef] - Ramírez, P.; Carta, J.A. The use of wind probability distributions derived from the maximum entropy principle in the analysis of wind energy. A case study. Energy Convers. Manag.
**2006**, 47, 2564–2577. [Google Scholar] [CrossRef] - Chang, T.P. Estimation of wind energy potential using different probability density functions. Appl. Energy
**2011**, 88, 1848–1856. [Google Scholar] [CrossRef] - Fyrippis, I.; Axaopoulos, P.J.; Panayiotou, G. Wind energy potential assessment in Naxos Island, Greece. Appl. Energy
**2010**, 87, 577–586. [Google Scholar] [CrossRef] - Sherlock, R. Analyzing winds for frequency and duration. In On Atmospheric Pollution; Springer: Berlin/Heidelberg, Germany, 1951; pp. 42–49. [Google Scholar]
- Carta, J.A.; Mentado, D. A continuous bivariate model for wind power density and wind turbine energy output estimations. Energy Convers. Manag.
**2007**, 48, 420–432. [Google Scholar] [CrossRef] - Carta, J.A.; Ramirez, P.; Velazquez, S. A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands. Renew. Sustain. Energy Rev.
**2009**, 13, 933–955. [Google Scholar] [CrossRef] - Chandel, S.; Ramasamy, P.; Murthy, K. Wind power potential assessment of 12 locations in western Himalayan region of India. Renew. Sustain. Energy Rev.
**2014**, 39, 530–545. [Google Scholar] [CrossRef] - Allouhi, A.; Zamzoum, O.; Islam, M.; Saidur, R.; Kousksou, T.; Jamil, A.; Derouich, A. Evaluation of wind energy potential in Morocco’s coastal regions. Renew. Sustain. Energy Rev.
**2017**, 72, 311–324. [Google Scholar] [CrossRef] - Rocha, P.A.C.; de Sousa, R.C.; de Andrade, C.F.; da Silva, M.E.V. Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil. Appl. Energy
**2012**, 89, 395–400. [Google Scholar] [CrossRef] - Justus, C.; Hargraves, W.; Mikhail, A.; Graber, D. Methods for estimating wind speed frequency distributions. J. Appl. Meteorol.
**1978**, 17, 350–353. [Google Scholar] [CrossRef] - Lysen, H. Introduction to Wind Energy, Consultancy Services; Wind Energy, Developing Countries (CWD): London, UK, 1983; p. 82-1. [Google Scholar]
- Akdağ, S.A.; Dinler, A. A new method to estimate Weibull parameters for wind energy applications. Energy Convers. Manag.
**2009**, 50, 1761–1766. [Google Scholar] [CrossRef] - Elsner, P. Continental-scale assessment of the African offshore wind energy potential: Spatial analysis of an under-appreciated renewable energy resource. Renew. Sustain. Energy Rev.
**2019**, 104, 394–407. [Google Scholar] [CrossRef] [Green Version] - Qing, X. Statistical analysis of wind energy characteristics in Santiago island, Cape Verde. Renew. Energy
**2018**, 115, 448–461. [Google Scholar] [CrossRef] - Alkhalidi, M.A.; Al-Dabbous, S.K.; Neelamani, S.; Aldashti, H.A. Wind energy potential at coastal and offshore locations in the state of Kuwait. Renew. Energy
**2019**, 135, 529–539. [Google Scholar] [CrossRef] - Stevens, M.; Smulders, P. The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes. Wind Eng.
**1979**, 3, 132–145. [Google Scholar] - Luankaeo, S.; Tirawanichakul, Y. Assessment of wind energy potential in Prince of Songkla University (South Part of Thailand): Hatyai campus. Energy Procedia
**2017**, 138, 704–709. [Google Scholar] [CrossRef] - Bahrami, A.; Teimourian, A.; Okoye, C.O.; Shiri, H. Technical and economic analysis of wind energy potential in Uzbekistan. J. Clean. Prod.
**2019**, 223, 801–814. [Google Scholar] [CrossRef] - Bataineh, K.M.; Dalalah, D. Assessment of wind energy potential for selected areas in Jordan. Renew. Energy
**2013**, 59, 75–81. [Google Scholar] [CrossRef] - Belabes, B.; Youcefi, A.; Guerri, O.; Djamai, M.; Kaabeche, A. Evaluation of wind energy potential and estimation of cost using wind energy turbines for electricity generation in north of Algeria. Renew. Sustain. Energy Rev.
**2015**, 51, 1245–1255. [Google Scholar] [CrossRef] - Oyedepo, S.O.; Adaramola, M.S.; Paul, S.S. Analysis of wind speed data and wind energy potential in three selected locations in south-east Nigeria. Int. J. Energy Environ. Eng.
**2012**, 3, 1–11. [Google Scholar] [CrossRef] [Green Version] - Li, Y.; Wu, X.P.; Li, Q.S.; Tee, K.F. Assessment of onshore wind energy potential under different geographical climate conditions in China. Energy
**2018**, 152, 498–511. [Google Scholar] [CrossRef] - Shoaib, M.; Siddiqui, I.; Rehman, S.; Khan, S.; Alhems, L.M. Assessment of wind energy potential using wind energy conversion system. J. Clean. Prod.
**2019**, 216, 346–360. [Google Scholar] [CrossRef] - Solyali, D.; Altunç, M.; Tolun, S.; Aslan, Z. Wind resource assessment of Northern Cyprus. Renew. Sustain. Energy Rev.
**2016**, 55, 180–187. [Google Scholar] [CrossRef] - Masseran, N. Integrated approach for the determination of an accurate wind-speed distribution model. Energy Convers. Manag.
**2018**, 173, 56–64. [Google Scholar] [CrossRef] - Chowdhury, N.; Pilo, F.; Pisano, G. Optimal energy storage system positioning and sizing with robust optimization. Energies
**2020**, 13, 512. [Google Scholar] [CrossRef] [Green Version] - Wei, J.; Zhang, Y.; Wang, J.; Cao, X.; Khan, M.A. Multi-period planning of multi-energy microgrid with multi-type uncertainties using chance constrained information gap decision method. Appl. Energy
**2020**, 260, 114188. [Google Scholar] [CrossRef] - Chen, Y.; Zhang, Y.; Wang, J.; Lu, Z. Optimal operation for integrated electricity–heat system with improved heat pump and storage model to enhance local energy utilization. Energies
**2020**, 13, 6729. [Google Scholar] [CrossRef] - Wei, J.; Zhang, Y.; Wang, J.; Wu, L. Distribution LMP-based Demand Management in Industrial Park via a Bi-level Programming Approach. IEEE Trans. Sustain. Energy
**2021**, 12, 1695–1706. [Google Scholar] [CrossRef] - Hsu, S.; Meindl, E.A.; Gilhousen, D.B. Determining the power-law wind-profile exponent under near-neutral stability conditions at sea. J. Appl. Meteorol. Climatol.
**1994**, 33, 757–765. [Google Scholar] [CrossRef] [Green Version] - Li, J.; Yu, X.B. Onshore and offshore wind energy potential assessment near Lake Erie shoreline: A spatial and temporal analysis. Energy
**2018**, 147, 1092–1107. [Google Scholar] [CrossRef] - Parajuli, A. A statistical analysis of wind speed and power density based on Weibull and Rayleigh models of Jumla, Nepal. Energy Power Eng.
**2016**, 8, 271–282. [Google Scholar] [CrossRef] [Green Version] - Zheng, C.-W.; Xiao, Z.-N.; Peng, Y.-H.; Li, C.-Y.; Du, Z.-B. Rezoning global offshore wind energy resources. Renew. Energy
**2018**, 129, 1–11. [Google Scholar] [CrossRef] - Christakos, K. Characterization of the Coastal Marine Atmospheric Boundary Layer (MABL) for Wind Energy Applications. Master’s Thesis, The University of Bergen, Bergen, Norway, 2013. [Google Scholar]
- El-Shimy, M. Optimal site matching of wind turbine generator: Case study of the Gulf of Suez region in Egypt. Renew. Energy
**2010**, 35, 1870–1878. [Google Scholar] [CrossRef] - Li, H.; Chen, Z. Design optimization and site matching of direct-drive permanent magnet wind power generator systems. Renew. Energy
**2009**, 34, 1175–1184. [Google Scholar] [CrossRef] - Bauer, L. Krasnovsky WIME D-30—100,00 kW—Wind Turbine. The United State, 2019. Available online: en.wind-turbine-models.com (accessed on 25 March 2020).
- Chauhan, A.; Saini, R. Statistical analysis of wind speed data using Weibull distribution parameters. In Proceedings of the 2014 1st International Conference on Non Conventional Energy (ICONCE 2014), Kalyani, India, 16–17 January 2014; pp. 160–163. [Google Scholar]
- Radfar, S.; Shafieefar, M.; Akbari, H.; Galiatsatou, P.A.; Mazyak, A.R. Design of a rubble mound breakwater under the combined effect of wave heights and water levels, under present and future climate conditions. Appl. Ocean Res.
**2021**, 112, 102711. [Google Scholar] [CrossRef] - Golbaz, D.; Asadi, R.; Amini, E.; Mehdipour, H.; Nasiri, M.; Nezhad, M.M.; Naeeni, S.T.O.; Neshat, M. Ocean Wave Energy Converters Optimization: A Comprehensive Review on Research Directions. arXiv
**2021**, arXiv:2105.07180. [Google Scholar]

**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] | $f\left(v\right)=\frac{k}{c}.{\left(\right)}^{\frac{v}{c}}k-1$ | k: shape c: scale |

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

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

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

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

Generalized Extreme Value [2] | $f\left(v\right)=\frac{1}{\sigma}{\left(\right)}^{1}-1-\frac{1}{k}$ | 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} | ${R}^{2}=1-\frac{{\sum}_{i=1}^{n}{\left(\right)}^{{y}_{i}}2}{}{\sum}_{i=1}^{n}{\left(\right)}^{{y}_{i}}2$ | ${y}_{i}$: Observed data ${y}_{ic}$: fitted data n: Number of data samples. |

RMSE | $RMSE={\left(\right)}^{\frac{1}{n}}$ | ${y}_{i}$: 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 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**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