sustainability

Article Offshore Energy Resource Assessment across the Territory of Oman: A Spatial-Temporal Data Analysis

Amer Al-Hinai 1,2,* , Yassine Charabi 3 and Seyed H. Aghay Kaboli 1,3

1 Research Center, Sultan Qaboos University, 123 Muscat, Oman; [email protected] 2 Department of Electrical & Computer Engineering, College of Engineering, Sultan Qaboos University, 123 Muscat, Oman 3 Center for Environmental Studies and Research, Sultan Qaboos University, 123 Muscat, Oman; [email protected] * Correspondence: [email protected]

Abstract: Despite the long shoreline of Oman, the wind energy industry is still confined to onshore due to the lack of knowledge about offshore wind potential. A spatial-temporal wind data analysis is performed in this research to find the locations in Oman’s territorial with the highest potential for offshore wind energy. Thus, wind data are statistically analyzed for assessing wind characteristics. Statistical analysis of wind data include the density, and Weibull scale and shape factors. In addition, there is an estimation of the possible energy production and by three commercial offshore wind turbines suitable for 80 up to a 110 m hub height. The findings show that offshore wind turbines can produce at least 1.34 times more energy than land-based and nearshore wind turbines. Additionally, offshore wind turbines generate more power in the Omani

 peak electricity demand during the summer. Thus, offshore wind turbines have great advantages  over land-based wind turbines in Oman. Overall, this work provides guidance on the deployment

Citation: Al-Hinai, A.; Charabi, Y.; and production of offshore wind energy in Oman. A thorough study using bankable wind data along Aghay Kaboli, S.H. Offshore Wind with various logistical considerations would still be required to turn offshore wind potential into real Energy Resource Assessment across wind farms in Oman. the Territory of Oman: A Spatial- Temporal Data Analysis. Keywords: data analysis; offshore wind energy; sultanate of Oman; wind assessment; Sustainability 2021, 13, 2862. https:// doi.org/10.3390/su13052862

Academic Editor: Domenico Curto 1. Introduction The Sultanate of Oman is a hydrocarbon-based economy where the prolonged drop in Received: 30 December 2020 oil and gas prices since 2015 has triggered an unprecedented economic depression, put a Accepted: 2 March 2021 Published: 6 March 2021 strain on the financial situation and induced high external borrowing needs. Oil markets are experiencing a fundamental change. New technologies have increased the supply of

Publisher’s Note: MDPI stays neutral oil from old and new sources while growing concerns over climate change are forcing with regard to jurisdictional claims in the world to increasingly shifting away from oil. The global oil demand is projected to published maps and institutional affil- rise more slowly and progressively decline in the next two decades due to advances in iations. energy-saving technology, the massive deployment of sources, and a strong international commitment to combat climate change. These expectations will pose a significant challenge for Oman due to its limited and declining oil and gas resources and call for accelerating the pace of transformation towards a post-oil economy. The transition to a post-oil future involves significant challenges, mainly for the power sector in Oman. Copyright: © 2021 by the authors. Four significant challenges face the power sector: Licensee MDPI, Basel, Switzerland. This article is an open access article • High government per capita subsidies for the electric sector: Despite the economic distributed under the terms and depression due to low oil prices, the government subsidies for the power sector per conditions of the Creative Commons capita rose by 5.09% from $1202 in 2018 to $1271 in 2019 [1]. Attribution (CC BY) license (https:// • A steady increase in energy demand: The energy peak demand increases at about 9% creativecommons.org/licenses/by/ annually, from 5122 MW in 2014, and is forecast to hit 9530 MW in 2021 [1]. 4.0/).

Sustainability 2021, 13, 2862. https://doi.org/10.3390/su13052862 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 2862 2 of 18

• GHG emissions’ exponential rise: The GHG volume emitted from 2000 to 2015 has risen 5.64 times from 21,666 Gg in 2000 to 97,072 Gg in 2015. The energy sector accounts for 63% of all GHG emissions [2]. • The renewables make up less than 1% of the country’s electricity mix in 2020: Oman’s electricity supply is still entirely powered by nationally produced natural gas and diesel [3]. In the past two years, the government has made notable progress in integrating solar and wind energy into its energy mix. Oman’s government pledged a target of 30% renewable energy in its energy mix by 2030. Solar power will produce 21% of the total energy needed in 2030. Wind energy will contribute 6.5% of the energy, and waste will contribute 2.5% of the energy mix. However, the gas-fired power stations will dominate about 70% of Oman’s energy mix by 2030. The Sultanate plans to reach 2600 MW from renewables by 2025 through independent power producers, representing around 16% of the Sultanate’s power demand. By 2025, 12% of Oman’s total power will be from solar energy, while wind, waste energy and other source of renewable energy (e.g., wave-energy) account for 2% of the mix [2–7]. Furthermore, wind energy is given national attention due to the long shoreline ex- tended over 1700 km in the mainland and vast uninhabited areas that are associated. Oman is overlooking three seas, namely, the Persian Gulf, the of Oman, and the Arabian Sea, and affected by the north-east monsoon in the winter and the Southeast monsoon in the summer. Both wind systems ensure renewable energy generation throughout the year. The first onshore project in Oman and the Arabian Peninsula with a capacity of 50 MW is commercially operational since December 2020, in the mountain area of Dhofar, in the south of Oman. The Dhofar wind farm is a significant milestone in the Omani government’s transition towards using wind power. Dhofar onshore wind farm will reach 250 MW in capacity by 2025. Oman plans to construct a second large onshore wind farm of 300 MW in the Governorate of Dhofar by 2025. Another 400 MW onshore wind farm is planned in the Al Wusta Governorate. The envisaged onshore wind farm projects are now on a path to produce 850 MW of renewable energy for Oman by 2025 [2,3]. On the opposite, Oman’s offshore wind capacity has yet to be thoroughly explored due to the lack of proper maritime wind data. Several academic studies have focused on Oman’s onshore wind resource assessments using different techniques and data sets. Using NWP models for wind energy applications in Oman is widely reviewed [8–10]. A few research studies have been conducted so far to assess the offshore wind energy potential in Oman. NWP models are recently applied in [11] to investigate the offshore wind energy potential and develop wind speed maps over the Oman maritime zone (OMZ). This study reported that there is significant offshore wind energy potential in Oman. The previous research for offshore wind energy resource assessment is only limited to the Oman maritime zone (OMZ). Furthermore, the quantitative analysis and comparison for the onshore and offshore wind characteristics and their wind energy potentials have not been studied. In this study, the wind resources for both onshore and offshore across Oman’s territory are compared in parallel. This study will also provide a precise assessment of offshore wind energy potential in Oman and compare wind turbines’ output power at offshore sites across the Oman territorial seas. The long-term wind records will be statistically analyzed in this article. As recently proposed in [12], the five-parameter logistic function can accurately model a wind turbine’s power curve. The probability density function (PDF) of the wind turbine’s output power based on the five parameters logistic function is derived in this study; it has not been previously reported to the best of the authors’ knowledge. Besides the contributing to the body of knowledge on methodologies, this paper aims to fill a knowledge gap by focusing on influences of temporal cycles on offshore wind characteristics and energy potential. At the core, this paper aims to provide an in-depth assessment of wind resource assessment across Oman’s territory for plant feasibility investigation Sustainability 2021, 13, 2862 3 of 18

with publicly available data under current offshore wind turbine technology. This study conducts a detailed analysis of the offshore wind energy potential across Oman’s territory. This allows for evaluating potential offshore wind farm locations by improving the ability to predict Oman’s production potentials. This paper is organized as follows: Section2 describes datasets and methodology. Section3 presents all the simulation results. Finally, the conclusion was drawn by consolidating the important features of this study.

2. Dataset and Methodology 2.1. ECMWRF ERA5 Reanalysis Wind Data Key considerations in designing, siting, and operating an offshore wind farm include reliable assessments of wind energy resources, accurate forecasts, and wind power vari- ability quantification. The uncertainty in wind resource assessment could lead to highly inaccurate data. The financial risk-based model used for wind energy projects is contingent upon the uncertainties which hinder the extensive deployment of offshore wind energy. It is recognized known that an accurate estimation of offshore wind resources assessment is a complex process and a challenging task that requires an accurate source of data. Data collected from offshore meteorological masts or marine buoys is one of the most widely used data sources to create wind energy density maps due to their high reliability. Al- though masts and buoy measurements provide accurate information with a high temporal resolution, as its measurements represent only a single point, it is considered a low spatial resolution approach [13]. Ship measurements can provide extended spatial coverage, but this method’s main drawback is low temporal resolution. The associated drawbacks of both ship and buoy measurements are addressed by satellite observation. Hence, the offshore wind resource assessment would be biased if it is based on only one satellite information [13]. Recently, Numerical Weather Prediction (NWP) models are commonly used for wind resource assessment over the studying area to provide high spatial and tem- poral resolution data. The predicted data quality is strongly dependent on the associated input observation and the numerical model core [14]. Furthermore, higher resolution NWP models are provided by higher computational cost. Due to the lack of in-situ measurements using meteorological masts or buoy for off- shore wind in the maritime territory of Oman, the high uncertainty associated with satellite wind data, and the constraints of using the NWP model, the present study utilizes the wind data from the European center for medium-range weather forecasts (ECMWF), ERA5. ERA5 is the fifth generation ECMWF reanalysis. ECMWF ERA5 provides atmospheric reanalysis of the global climate with a horizontal resolution of 0.25◦ × 0.25◦. ECMWF wind data were used in several offshore wind resource assessments across the world and considered the most reliable and robust alternatives for long-term wind data [15].

2.2. Methodology for Hub-Height Wind Speed The ECMWF ERA5 reanalysis wind velocity data for the period 2014–2018 at 10, 50, 80, and 100 m height above the mean sea level was used to calculate the most common loading and energy production parameters on offshore Wind Turbines as follows: • Power law Wind shear is a variation in wind speed over a relatively short height above the earth’s surface. Generally, the wind speed at different atmospheric surface boundary layers (less than 150 m height) is calculated using a power law [16]. Hellman first proposed the power- law, so it is also known as Hellman’s exponential law. The wind speed at two different height levels are correlated by Hellman exponential law as expressed below:

 α l2 v2 = v1 (1) l1

where v1 and v2 represent wind speed at the height levels of l1 and l2 (m), respectively, the Hellmann exponent α represents the wind shear coefficient (WSC). The WSC values Sustainability 2021, 13, 2862 4 of 18

for different typical terrains are summarized in Table1. The WSC varies with various factors such as atmospheric stability, height, humidity, temperature, diurnal and seasonal effects [17–19].

Table 1. Terrains with different wind shear coefficients (WSC).

Terrain Type WSC Ocean, lake, and flat area 0.10 Tallgrass 0.15 Shrubs, hedges, and tall crops 0.20 Forest area 0.25 Small city 0.30 Town with high-rise buildings 0.40

• Weibull distribution A general wind distribution function with few parameters is needed to compare the wind characteristics at different locations correctly. Various probability density functions (PDFs) can be used to explain wind speed distribution [20]. The Weibull probability density function is considered one of the most proper distribution functions of wind speed measurements [21,22]. The general form of two parameters Weibull density function is defended as follows: − k  v k 1 −( v )k f (v) = e c (2) c c where v is the wind speed (m/s), and f (v) is the Weibull probability density function (PDF). k and c parameters, respectively, represent the shape and scale factors of the Weibull probability density function. For wind energy analysis, various approaches such as maximum likelihood method (MLM), graphical method (GM), and method of moments (MM) are widely applied to estimate two parameters of the Weibull probability distribution function [23]. The MLM is applied in this research for fitting the Weibull distribution to the time-series of wind speed data. Consequently, the shape and scale factors can be computed by the following formulas:

−  N N  1 k ∑ wi ln(wi) ∑ ln(wi)  = =  k =  i 1 − i 1  (3)  N   k N  ∑ wi i=1

N !1/k 1 k c = ∑ wi (4) N i=1

where N is the total number of observed wind speeds, i is the time step, and wi is the average wind speed in each time step. The wind speed can follow the Weibull distribution properly, and the power curve of a wind turbine can be accurately modeled by the five-parameter logistic function as proposed in [12]. Thus, the PDF of the wind turbine power can be derived from the statistical inference theorem [24] as follows:

 −1  d −1 f (y) = fx g (y) g (y) (5) Y dy

The variable with an unknown PDF is represented by y, the variable with a known PDF is represented by x. The one-to-one mapping between x and y is symbolized by g. The PDF of variables x and y are denoted by fx and fy(y), respectively. Given the five-parameter logistic function of the wind turbine power curve as be- low [12]: Sustainability 2021, 13, 2862 5 of 18

a − a y = g(x) = a + 2 1 (6) 1   a4 a5 1 + x a3 in accordance with Equation (5), the PDF of a wind turbine power based on the assump- tion that wind speed follows the Weibull distribution can be estimated by the following expression:

 k−1  − 1/a  1/a4 a2 a1 5 − y−a 1   ( | ) = a3k  1  − a3 k × fy y a1, a2, a3, a4, a5 c  c  exp c (7)  (1/a )−1 1/a (1/a4)−1 a1−a2 5 a1−a2 5 a3(a1−a2) −1 a1−y a1−y 2 a4a5(a1−y)

where the unknown parameters of the five-parameter logistic function are represented by a1, a2, a3, a4, and a5, these parameters control the PDF of the wind turbine power, and a curve fitting technique can estimate them. Equations (3) and (4) can estimate the two unknown parameters of Weibull distribution (k and c). • Wind power density Wind power density (WPD) is a crucial parameter for assessing the available wind energy potential at a certain site. The WPD measured in W/m2 specifies how much wind’s kinetic energy per swept area of the rotor blades is available at a site for conversion into electrical energy by a wind turbine. Generally, for generating higher electrical energy, wind turbines are erected at sites with higher WPD. The WPD of a site can be calculated by: 1 WPD = ρv3 (8) 2 where ρ is air density in kg/m3, and v is the wind velocity at the monitoring site in m/s. As validated in [25], the WPD of a site can be estimated by Weibull parameters from the following expression: 1  k + 3  WPD = ρc3Γ (9) 2 k where Γ is the gamma function [26] defined as below:

Z ∞ Γ(x) = e−uux−1du (10) 0

• Capacity factor The capacity factor is the ratio of energy output over a specific time period to the maximum possible energy output over that time period. If wind speed strictly follows Weibull distribution, then the capacity factor of a wind turbine can be estimated by Weibull distribution parameters [27] as follows:

 k  k − vin  − − vr   k! exp c exp c vo f f Cf = − exp − (11) vr k vin k c c − c

where vr, vin, and voff are rated, cut-in, and cut-off speeds of the wind turbine, respectively.

2.3. Description of the Monitoring Sites For this study’s purpose, the offshore and onshore sites are compared to determine the overall offshore and onshore potential. The selection of offshore and onshore sites with high wind energy potential was based on the preliminary assessment of the ECMWF ERA5 reanalysis wind velocity data for 2014–2018 at 10, 50, 80, and 100 m height above Sustainability 2021, 13, 2862 6 of 18

the mean sea level. The initial analysis shows that wind speed increases with height and moves from an onshore to an offshore location. The maximum wind speed is observed around Al Hallaniyah and Masirah islands. Three offshore locations with high wind energy potential along the shoreline of Oman were selected to analyze wind energy production Sustainability 2021, 13, x FOR PEER REVIEW(Figure 1) . As shown in Figure1 , site D, which has the highest onshore wind speed,7 of 20 is chosen to compare the wind energy potential of offshore sites with the highest onshore wind energy potential in the territory of Oman.

FigureFigure 1. 1.Annual Annual mean mean wind wind speed speed map map of of Oman. Oman. ((aa)) atat 1010 mm heightheight level;level; ((bb)) atat 5050 mm heightheight level;level; ((c)) at 80 m height height level; level; (d()d at) at 100 100 m m height height level;level; ((e)) zoomed view of the monitoring sites sites at at 100 100 m m height height level. level.

2.4. Wind Turbines Description Three of the most powerful wind turbines currently available on the market, namely (V164), GE (3.6sl), and (SWT113), with various technical characteris- tics, are selected in order to compare the power capacity factors at diverse conditions. Table 2 tabulates the general technical specification of the selected wind turbines. All three wind turbines are upwind horizontal axis and have three blades with different hub Sustainability 2021, 13, 2862 7 of 18

2.4. Wind Turbines Description

Sustainability 2021, 13, x FOR PEER REVIEWThree of the most powerful wind turbines currently available on the market, namely8 of 20 VESTAS (V164), GE (3.6sl), and SIEMENS (SWT113), with various technical characteristics, are selected in order to compare the power capacity factors at diverse conditions. Table2 tabulates the general technical specification of the selected wind turbines. All three wind turbinesheights aresuitable upwind for horizontaldiverse terrain axis andtypes. have SIEMENS three blades (SWT113) with differentis placed hub at 80 heights m hub suitableheight, for GE diverse (3.6sl) is terrain placed types. at 100 SIEMENS m hub, and (SWT113) VESTAS is (V164) placed is at placed 80 m hub at 110 height, m height. GE (3.6sl)The VESTAS is placed (V164), at 100 m GE hub, (3.6sl), and and VESTAS SIEMENS (V164) (SWT113) is placed wind at 110 turbin m height.es have The rated VESTAS power (V164),of 9500 GE kW, (3.6sl), 3600 and kW, SIEMENS and 3200 (SWT113)kW, respectively wind turbines. The power have curves rated powerof the selected of 9500 kW,wind 3600turbines, kW, and as illustrated 3200 kW,respectively. in Figure 2 are The mathematically power curves modeled of the selected by Equation wind turbines,(6). The curve as illustratedfitting technique in Figure is 2applied are mathematically to estimate the modeled parameters by of Equation wind turbine (6). The power curve curves fitting mod- techniqueels. is applied to estimate the parameters of wind turbine power curves models. Power (kW) Power

Figure 2. Selected wind turbine power curve. Figure 2. Selected wind turbine power curve.

TableTable 2. Characteristics2. Characteristics of of selected selected wind wind turbines. turbines.

RatedRated Power Cut-InCut-In Wind Wind Speed RatedRated Wind Speed Speed Cut-OffCut-Off Wind Wind SpeedHub Hub Height Height WindWind Turbine Turbine (kW)(kW) (m/s)(m/s) (m/s)(m/s) Speed(m/s) (m/s) (m)(m) VESTASVESTAS (V164) (V164) 9500 9500 3.5 3.5 1414 2525 110110 GE (3.6sl) 3600 3.5 14 27 100 GE (3.6sl) 3600 3.5 14 27 100 SIEMENS (SWT113) 3200 2.5 13.5 22 80 SIEMENS (SWT113) 3200 2.5 13.5 22 80 3. Results and Discussion 3.3.1. Results Wind and Speeds Discussion Characteristics 3.1. Wind Speeds Characteristics The seasonal characteristics of wind speed for three monitoring sites at 100 m height levelThe derived seasonal with characteristics theoretical methods of wind accord speed foring threeto Equations monitoring (2)–(4) sites are at tabulated 100 m height in levelTable derived 3. The withresults theoretical indicate that methods the maxi accordingmum wind to Equations speed happens (2)–(4) during are tabulated the summer, in Tableand3 the. The minimum results indicatewind speed that ha theppens maximum during wind the winter speed happensand autumn. during The the annual summer, mean andwind the speeds minimum at sites wind A, B, speed and C happens are 9.35 during m/s, 9.9 the m/s, winter and 7.44 and m/s. autumn. The highest The annual average meanwind wind speed speeds of the at three sites sites A, B, observed and C are in 9.35 the m/s,summer 9.9 m/s,is 16.86 and m/s, 7.44 17.27 m/s. m/s, The and highest 11.61 average wind speed of the three sites observed in the summer is 16.86 m/s, 17.27 m/s, and m/s, respectively. The variations of wind speed are most significant at all the sites during 11.61 m/s, respectively. The variations of wind speed are most significant at all the sites the autumn season. As strong blow during the summer, this season has the slightest during the autumn season. As strong winds blow during the summer, this season has the wind speed variations for all three sites. slightest wind speed variations for all three sites. TheTable wind 3. speed Wind isspeed related characteristics to the observed of offshore conditions sites. at the open ocean or sea by the Beaufort wind force scale. It indicated that the wind class is gentle (large wavelets at sea) in the autumn and winter but is strongWind (moderatelySpeed (m/s) high waves at sea) in the summer and Astronomical spring for all three sites. A strong trend of seasonalWeibull variation Weibull is observed in wind speed. Standard Coefficient of Seasons Mean Maximum Shape Factor Scale Factor Beaufort Scale Deviation Variation (%) k c Spring 10.43 21.02 4.84 46.37 2.31 11.81 5-Fresh breeze Site A Summer 16.85 23.89 4.57 27.14 4.64 18.36 7-Near gale Autumn 5.02 20.03 3.16 62.91 1.69 5.65 3-Gentle breeze Sustainability 2021, 13, 2862 8 of 18

Sustainability 2021, 13, x FOR PEER REVIEW 9 of 20 For all three monitoring sites, the wind speed is lower in the cold seasons and higher in the hot seasons, as there is an apparent high wind speed belt from May until September.

Winter 5.07 13.42Table 3. Wind 2.76 speed characteristics 54.41 of offshore 1.91 sites. 5.72 3-Gentle breeze Spring 11.27 22.97 4.72 41.87 2.57 12.70 6-Strong breeze Summer 17.27 23.86 4.27 24.74 Wind Speed (m/s) 5.19 18.69 8-Fresh gale Site B Astronomical Weibull Weibull Scale Autumn 5.21 20.95 3.27Standard Coefficient 62.79 of 1.69 5.85 3-Gentle breeze Seasons Mean Maximum Shape Factor Factor Beaufort Scale Deviation Variation (%) Winter 5.83 14.64 3.18 54.63 1.90k 6.57c 4-Moderate breeze SpringSpring 8.5410.43 18.87 21.02 3.56 4.84 41.73 46.37 2.56 2.31 9.62 11.81 5-Fresh 5-Fresh breeze breeze Summer 16.85 23.89 4.57 27.14 4.64 18.36 7-Near gale Site A Summer 11.61 17.94 3.44 29.64 3.90 12.84 6-Strong breeze Site C Autumn 5.02 20.03 3.16 62.91 1.69 5.65 3-Gentle breeze AutumnWinter 5.085.07 13.59 13.42 2.48 2.76 48.82 54.41 2.16 1.91 5.74 5.72 3-Gentle 3-Gentle breeze WinterSpring 4.5211.27 11.91 22.97 2.14 4.72 47.49 41.87 2.22 2.57 5.10 12.70 3-Gentle 6-Strong breeze Summer 17.27 23.86 4.27 24.74 5.19 18.69 8-Fresh gale Site B Autumn 5.21The 20.95 wind speed 3.27 is related to 62.79 the observed co 1.69nditions at the 5.85 open ocean 3-Gentle or sea breeze by the Winter 5.83 14.64 3.18 54.63 1.90 6.57 4-Moderate breeze Beaufort wind force scale. It indicated that the wind class is gentle (large wavelets at sea) Spring 8.54in the autumn 18.87 and winter 3.56 but is strong 41.73 (moderately 2.56 high waves 9.62 at sea) in the 5-Fresh summer breeze and Summer 11.61 17.94 3.44 29.64 3.90 12.84 6-Strong breeze Site C Autumn 5.08spring 13.59 for all three sites. 2.48 A strong 48.82 trend of seasonal 2.16 variation is 5.74 observed 3-Gentlein wind breeze speed. Winter 4.52For all 11.91 three monitoring 2.14 sites, the 47.49 wind speed is 2.22 lower in the cold 5.10 seasons 3-Gentle and higher breeze in the hot seasons, as there is an apparent high wind speed belt from May until September. The frequency histogramhistogram andand thethe wind wind rose rose of of seasonal seasonal wind wind speeds speeds for for all all monitoring monitor- ingsites sites are plottedare plotted in Figures in Figures3–8. The 3–8. distribution The distribution characteristics characteristics of wind of speed wind are speed widely are widelyanatomized anatomized by using by three using distribution, three distribution, i.e., log-normal i.e., log-normal distribution, distribution, gamma distribution,gamma dis- tribution,and Weibull and distribution. Weibull distribution. The comparative The comp analysisarative under analysis different under distributions different distribu- (Weibull tionsdistribution, (Weibull Log-normal distribution, distribution, Log-normal and di Gammastribution, distribution) and Gamma for all distribution) monitoring sitesfor all is monitoringshown in Figures sites is3 –shown5. As shown in Figures in these 3–5. figures,As shown the in log-normal these figures, distribution the log-normal has a more dis- tributionsignificant has error a more than significant the Gamma error and than Weibull. the Gamma and Weibull.

0.12 0.15 0.11 Histogram 0.14 Histogram Weibull distribution 0.1 0.13 Weibull distribution Lognormal distribution 0.12 Lognormal distribution 0.09 Gamma distribution 0.11 Gamma distribution 0.08 0.1 0.07 0.09 0.06 0.08 0.07 0.05

Probability 0.06 Probability 0.04 0.05 0.03 0.04 0.03 0.02 0.02 0.01 0.01 0 0 0 2 4 6 8 10 12 14 16 18 20 22 0 2 4 6 8 10 12 14 16 18 20 22 24 Wind Speed (m/s) Wind Speed (m/s) (a) (b) 0.19 0.12 0.18 Histogram Histogram 0.17 0.11 Weibull distribution Weibull distribution 0.16 0.1 0.15 Lognormal distribution Lognormal distribution 0.14 Gamma distribution 0.09 Gamma distribution 0.13 0.08 0.12 0.11 0.07 0.1 0.06 0.09 0.08 0.05 Probability 0.07 Probability 0.06 0.04 0.05 0.03 0.04 0.03 0.02 0.02 0.01 0.01 0 0 0 2 4 6 8 10 12 14 16 18 20 0 2 4 6 8 10 12 14 16 18 20 22 Wind Speed (m/s) Wind Speed (m/s) (c) (d)

Figure 3. SeasonalSeasonal wind wind speed frequency dist distributionribution at offshore site A ((a) spring; (b) summer; ((c)) autumn;autumn; ((d)) winter).winter). Sustainability 2021, 13, 2862 9 of 18 SustainabilitySustainability 20212021,, 1313,, xx FORFOR PEERPEER REVIEWREVIEW 1010 ofof 2020

0.120.12 0.170.17 HistogramHistogram 0.160.16 Histogram 0.110.11 Histogram WeibullWeibull distribution distribution 0.150.15 Weibull distribution 0.1 0.14 Weibull distribution 0.1 LognormalLognormal distribution distribution 0.14 Lognormal distribution 0.13 Lognormal distribution 0.090.09 Gamma distribution 0.13 Gamma distribution 0.12 GammaGamma distribution distribution 0.08 0.12 0.08 0.110.11 0.070.07 0.10.1 0.090.09 0.060.06 0.080.08 0.050.05 0.07 Probability Probability 0.07 Probability 0.06Probability 0.040.04 0.06 0.050.05 0.03 0.03 0.040.04 0.03 0.020.02 0.03 0.020.02 0.010.01 0.010.01

00 00 00 22 44 66 88 1010 1212 1414 1616 1818 2020 2222 2323 00 22 44 66 88 1010 1212 1414 1616 1818 2020 2222 2424 Wind Speed (m/s) Wind Speed (m/s) Wind Speed (m/s) Wind Speed (m/s) ((aa)) ((bb))

0.22 0.160.16 0.22 Histogram HistogramHistogram 0.150.15 Histogram 0.20.2 WeibullWeibull distribution distribution 0.140.14 WeibullWeibull distribution distribution 0.13 0.180.18 LognormalLognormal distribution distribution 0.13 LognormalLognormal distribution distribution Gamma distribution 0.120.12 Gamma distribution 0.160.16 Gamma distribution Gamma distribution 0.110.11 0.140.14 0.10.1 0.090.09 0.120.12 0.080.08 0.1 0.1 0.070.07 Probability Probability Probability

Probability 0.06 0.080.08 0.06 0.050.05 0.06 0.06 0.040.04 0.03 0.040.04 0.03 0.020.02 0.020.02 0.010.01

00 00 00 22 44 66 88 1010 1212 1414 1616 1818 2020 00 22 44 66 88 1010 1212 1414 1515 Wind Speed (m/s) Wind Speed (m/s) Wind Speed (m/s) Wind Speed (m/s) ((cc)) ((dd)) FigureFigureFigure 4.4. 4. SeasonalSeasonalSeasonal windwind wind speedspeed speed frequencyfrequency frequency distdist distributionributionribution atat at offshoreoffshore offshore sitesite BB (((( aa))) spring;spring; spring; (( bb)) summer;summer; (( cc)) autumn;autumn; (( dd)) winter. winter).winter.

0.12 0.140.14 0.12 0.13 Histogram HistogramHistogram 0.13 Histogram 0.110.11 Weibull distribution WeibullWeibull distribution distribution 0.120.12 Weibull distribution 0.1 Lognormal distribution 0.1 LognormalLognormal distribution distribution 0.110.11 Lognormal distribution Gamma distribution 0.090.09 Gamma distribution 0.10.1 Gamma distribution Gamma distribution 0.080.08 0.090.09 0.07 0.080.08 0.07 0.070.07 0.060.06 0.06 0.06 0.050.05 Probability Probability Probability 0.05Probability 0.05 0.040.04 0.040.04 0.030.03 0.030.03 0.020.02 0.020.02 0.01 0.010.01 0.01

00 00 00 22 44 66 88 1010 1212 1414 1616 1818 00 22 44 66 88 1010 1212 1414 1616 1818 WindWind Speed Speed (m/s) (m/s) WindWind Speed Speed (m/s) (m/s) ((aa)) ((bb))

0.2 0.2 0.220.22 HistogramHistogram HistogramHistogram 0.180.18 0.20.2 WeibullWeibull distribution distribution WeibullWeibull distribution distribution 0.18 0.160.16 LognormalLognormal distribution distribution 0.18 LognormalLognormal distribution distribution GammaGamma distribution distribution 0.16 GammaGamma distribution distribution 0.140.14 0.16 0.140.14 0.120.12 0.120.12 0.10.1 0.10.1 0.08 Probability 0.08 Probability Probability 0.08Probability 0.08 0.060.06 0.060.06 0.04 0.04 0.040.04 0.02 0.02 0.020.02

0 0 00 00 22 44 66 88 1010 1212 1414 00 22 44 66 88 1010 1212 WindWind Speed Speed (m/s) (m/s) WindWind Speed Speed (m/s) (m/s) ((cc)) ((dd))

Figure 5. Seasonal wind speed frequency distribution at offshore site C ((a) spring; (b) summer; (c) autumn; (d) winter. FigureFigure 5.5. SeasonalSeasonal windwind speedspeed frequencyfrequency distributiondistribution at offshore offshore site C C (( ((aa)) spring; spring; ( (bb)) summer; summer; ( (cc)) autumn; autumn; ( (dd)) winter. winter). Sustainability 2021, 13, 2862 10 of 18 Sustainability 2021, 13, x FOR PEER REVIEW 11 of 20 Sustainability 2021, 13, x FOR PEER REVIEW 11 of 20

FigureFigure 6. 6.Seasonal Seasonal windrose windrose compass compass distribution distribution at at offshore offshore site site A A (( ((aa))) spring; spring;spring; ( b((b))) summer; summer;summer; ( c(()c)) autumn; autumn;autumn; ( d((d)) winter)).) winter).winter).

Figure 7. Seasonal windrose compass distribution at offshore site B ((a) spring; (b) summer; (c) autumn; (d) winter)). Figure 7. Seasonal windrose compass distribution at offshore site B ((a)) spring;spring; ((b)) summer;summer; ((c)) autumn;autumn; ((d)) winter).winter). Sustainability 2021, 13, 2862 11 of 18 Sustainability 2021, 13, x FOR PEER REVIEW 12 of 20

FigureFigure 8. 8.Seasonal Seasonal windrosewindrose compass compass distribution distribution at at offshore offshore site site C C (( ((aa)) spring; spring; ( b(b)) summer; summer; ( c()c) autumn; autumn; ( d(d)) winter)). winter).

ToTo identifyidentify thethe mostmost effectiveeffective probabilityprobability densitydensity functionfunction (PDF)(PDF) ofof thethe windwind speed,speed, thethe sumsum ofof absoluteabsolute errorserrors (SAE)(SAE) ofof differentdifferent distributiondistribution isis computedcomputed forfor allall monitoringmonitoring sitessites andand tabulatedtabulated inin TableTable4 .4. As As shown shown in in this this Table, Table, the the most most suitable suitable wind wind speed speed for for allall monitoringmonitoring sitessites isis WeibullWeibull distribution,distribution, asas statedstated byby thethe InternationalInternational ElectrotechnicalElectrotechnical CommissionCommission standardstandard IECIEC 6400-26400-2 [[27].27]. Hence,Hence, WeibullWeibull distributiondistribution appliesapplies toto thethe windwind energyenergy resourceresource assessmentassessment atat monitoringmonitoring sites.sites.

TableTable 4. 4.Comparative Comparative analysisanalysis ofof differentdifferent wind wind speed speed probability probability density density functions. functions.

Distribution Astronomical Seasons Seasons Error Error GammaGamma LognormalLognormal WeibullWeibull SpringSpring 0.31130.3113 0.31150.3115 0.38250.3825 SummerSummer 0.51010.5101 0.57440.5744 0.43250.4325 SiteSite A A SAESAE AutumnAutumn 0.28510.2851 0.32010.3201 0.26240.2624 WinterWinter 0.65210.6521 0.70750.7075 0.59670.5967 SpringSpring 0.35910.3591 0.32250.3225 0.43320.4332 Summer 0.5246 0.6037 0.4887 Site B Summer SAE 0.5246 0.6037 0.4887 Site B AutumnSAE 0.3082 0.3294 0.2865 WinterAutumn 0.49230.3082 0.55800.3294 0.46900.2865 Winter 0.4923 0.5580 0.4690 Spring 0.2558 0.3532 0.1933 SummerSpring 0.45150.2558 0.45700.3532 0.43800.1933 Site C SAE AutumnSummer 0.70480.4515 0.74810.4570 0.64530.4380 Site C SAE WinterAutumn 0.89280.7048 0.90440.7481 0.88420.6453 WinterMean 0.47890.8928 0.51580.9044 0.45930.8842 Mean 0.4789 0.5158 0.4593 SustainabilitySustainability 20212021,, 1313,, xx FORFOR PEERPEER REVIEWREVIEW 1313 ofof 2020 Sustainability 2021, 13, 2862 12 of 18

TheThe windwind roserose asas aa graphicgraphic tooltool hashas beenbeen ususeded toto provideprovide aa succinctsuccinct viewview ofof howhow windwind The wind rose as a graphic tool has been used to provide a succinct view of how wind directiondirection andand windwind speedspeed areare usuallyusually distributeddistributed atat thethe selectedselected sites.sites. TheThe windwind roserose plots,plots, direction and wind speed are usually distributed at the selected sites. The wind rose plots, whichwhich werewere constructedconstructed usingusing thethe measuremenmeasurementsts ofof windwind directionsdirections andand correspondingcorresponding which were constructed using the measurements of wind directions and corresponding windwind speeds,speeds, givegive valuablevaluable informationinformation onon ththee availabilityavailability ofof directionaldirectional windwind speedspeed andand wind speeds, give valuable information on the availability of directional wind speed and prevailing wind direction at various wind speed intervals. The seasonal wind direction prevailingprevailing wind wind direction direction at at various various wind wind speed speed intervals. intervals. The The seasonal seasonal wind wind direction direction frequencies based on the mean wind speed data of the last five years for all sites are plot- frequenciesfrequencies based based on on the the mean mean wind wind speed speed data data of the of last the five last years five years for all for sites all are sites plot- are ted in Figures 6–8. A significant seasonal trend with intense wind speed during the sum- tedplotted in Figures in Figures 6–8. A6– significan8. A significantt seasonal seasonal trend trendwith intens with intensee wind windspeed speedduring during the sum- the mer and weaker wind speed during the winter are observed at all monitoring sites. The mersummer and weaker and weaker wind windspeed speed during during the winter the winter are observed are observed at all monitoring at all monitoring sites. The sites. seasonality effects are evident at all sites as the wind directions in the hot seasons are seasonalityThe seasonality effects effects are evident are evident at all at sites all sites as the as thewind wind directions directions in inthe the hot hot seasons seasons are are entirely different from the cold seasons’ dominant wind direction. During the autumn and entirelyentirely different different from from the the cold cold seasons’ seasons’ domi dominantnant wind wind direction. direction. During During the the autumn autumn and and winter, the wind direction at all three sites is dispersive as there are various dominant winter,winter, the the wind directiondirection atat all all three three sites sites is dispersiveis dispersive as thereas there are variousare various dominant dominant wind wind directions. The wind directions in the summer and spring tend to be more stable winddirections. directions. The wind The wind directions directions in the summerin the summer and spring and tendspring to betend more to stablebe more compared stable compared with the other seasons. comparedwith theother with seasons.the other seasons.

3.2.3.2.3.2. WindWind Wind PowerPower Power DensityDensity Density TheTheThe resultsresults results showshow show thatthat that sitesite site AA A hashas has thethe the maximummaximum maximum WPD,WPD, WPD, andand and sitesite site CC C hashas has thethe the minimumminimum minimum valuevaluevalue ofof of WPDWPD WPD (Figure(Figure (Figure 9).9).9). TheThe highest highest wind wind power density density (8332 (8332 W/mW/m22))2 happens)happens happens duringduring during JulyJulyJuly atat at sitesite site A.A. A. SiteSite Site BB B hashas has thethe the highesthighest highest annualannual annual averageaverage wind wind power power density,density, followed followedfollowedby byby site sitesite A AAand andand site sitesite C CC with withwith values valuesvalues of ofof 1393 13931393 W/m W/mW/m22,2,, 1279 12791279 W/m W/mW/m222,, and and 514514 W/mW/mW/m2,2, respectivelyrespectivelyrespectively (Figures 10 (Figures(Figuresand 11). 1010 andand 11).11).

80008000

70007000

60006000

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40004000

WPD (W/m²) 3000 WPD (W/m²) 3000

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00 JanJan Feb Feb Mar Mar Apr Apr May May Jun Jun Jul Jul Aug Aug Sep Sep Oct Oct Nov Nov Dec Dec Month Month FigureFigureFigure 9.9. 9. MonthlyMonthlyMonthly windwind wind powerpower power densitydensity density (W(W (WPD)PD)PD) atat at offshoreoffshore offshore sitesite site A.A. A.

80008000

70007000

60006000

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WPD (W/m²) 3000 WPD (W/m²) 3000

20002000

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00 JanJan Feb Feb Mar Mar Apr Apr May May Jun Jun Jul Jul Aug Aug Sep Sep Oct Oct Nov Nov Dec Dec Month Month FigureFigureFigure 10.10. 10. MonthlyMonthlyMonthly windwind wind powerpower power densitydensity density (W(W (WPD)PD)PD) atat at offshoreoffshore offshore sitesite site B.B. B. SustainabilitySustainability 20212021, 13, 13, x, 2862FOR PEER REVIEW 1413 of of 20 18

4000

3500

3000 Sustainability 2021, 13, x FOR PEER REVIEW 14 of 20 2500

2000

WPD (W/m²) 1500 4000 1000 3500 500 3000 0 2500 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month 2000 FigureFigure 11. 11. MonthlyMonthly wind wind power power density density (W (WPD)PD) at atoffshore offshore site site C. C.

WPD (W/m²) 1500 3.3. Capacity Factor 3.3. Capacity1000 Factor The seasonal energy capacity factors are presented in Figures 12–14. The results The seasonal energy capacity factors are presented in Figures 12–14. The results in- indicate500 that the most promising wind energy potential locations are offshore sites B and A. dicate that the most promising wind energy potential locations are offshore sites B and A. The next highest is onshore site D, and the least is site C, for all three wind turbines with The next0 highest is onshore site D, and the least is site C, for all three wind turbines with different characteristics at different hub height levels. It is clear that the capacity factor different Jancharacteristics Feb Mar Apr at May different Jun Jul hub Aug height Sep Oct levels. Nov DecIt is clear that the capacity factor increases when moving fromMonth onshore sites A and B to offshore locations as both offshore increases when moving from onshore sites A and B to offshore locations as both offshore sites have the highest capacity factor, and onshore site D has the lower for all months. Figuresites have 11. Monthly the highest wind capacitypower density factor, (W andPD) onshoreat offshore site site D C. has the lower for all months. The highest capacity factors for VESTAS (V164) wind turbines are observed at site 3.3.B Capacity on July andFactor August (100%) and the lowest at sites C on January (6.41%). The annual mean capacity factors of VESTAS (V164) at the four sites A, B, C, and D are 43.67%, 48.25%, The seasonal energy capacity factors are presented in Figures 12–14. The results in- 34.57%, and 36.03%, respectively. The capacity factors of the GE (3.6sl) wind turbine are dicate that the most promising wind energy potential locations are offshore sites B and A. higher than VESTAS (V164) wind turbine as the cut-off wind speed of GE (3.6sl) is higher The next highest is onshore site D, and the least is site C, for all three wind turbines with than that of VESTAS (V164). The capacity factors of the GE (3.6sl) wind turbine vary from different characteristics at different hub height levels. It is clear that the capacity factor 100% on July and August to 8.92% on November at site A; the factors differ from 100% in increasesJuly and when August moving 1 to 8.91%from onshore in November sites A at and site B B, to 94.55% offshore in Julylocations to 8.64% as both in February offshore at sitessite have C and the 96.49% highest on capacity July to 9.22%factor, onand January onshore at site site D D. has the lower for all months.

Figure 12. Monthly capacity factor of VESTAS (V164) wind turbine at monitoring sites.

FigureFigure 12. 12. MonthlyMonthly capacity capacity factor factor of VESTAS of VESTAS (V164) (V164) wind wind turbine turbine at atmonitoring monitoring sites. sites. SustainabilitySustainability 20212021, 13, 13, x, FOR 2862 PEER REVIEW 1514 of of20 18 Sustainability 2021, 13, x FOR PEER REVIEW 15 of 20

Figure 13. Monthly capacity factor of GE (3.6sl) wind turbine at monitoring sites. FigureFigure 13. 13. MonthlyMonthly capacity capacity factor factor of of GE GE (3.6sl) (3.6sl) wind wind turbine turbine at at monitoring monitoring sites. sites.

Site A 100 SiteSite B A 100 SiteSite C B 90 SiteSite D C 90 Site D 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May JunMonth Jul Aug Sep Oct Nov Dec Month FigureFigure 14. 14. MonthlyMonthly capacity capacity factor factor of ofSIEMENS SIEMENS (SWT (SWT113)113) wind wind turbine turbine at atmonitoring monitoring sites. sites. Figure 14. Monthly capacity factor of SIEMENS (SWT113) wind turbine at monitoring sites. TheAlthough highest thecapacity SIEMENS factors (SWT113) for VESTAS wind (V16 turbine4) wind has turbines the lowest are cut-offobserved wind at site speed, B onit July providesThe and highest August the highestcapacity (100%) capacity factors and the for factors lowest VESTAS atat allsites (V16 sites C4) on as wind January the cut-inturbines (6.41%). wind are speedTheobserved annual of thisat meansite wind B capacityonturbine July and factors lower August thanof VESTAS (100%) two other and (V164) wind the lowestat turbines. the fourat sites The sites C capacity onA, January B, C, factors and (6.41%). D for are SIEMENS The 43.67%, annual (SWT113)48.25%, mean 34.57%,capacitywind turbines and factors 36.03%, are of theVESTAS respectively. highest (V164) at site The at B capacity onthe August four factorssites (100%) A, of B, and the C, the GEand lowest(3.6sl) D are atwind 43.67%, site Cturbine on 48.25%, January are higher34.57%,(9.62%). than and The VESTAS 36.03%, annual (V164)respectively. mean wind capacity turbineThe factors capacity as th ofe SIEMENS factorscut-off windof (SWT113)the speed GE (3.6sl) of at GE sites wind (3.6sl) A, turbine B, is C, higher and are D thanhigherare that 48.17%, than of VESTAS VESTAS 53.26%, (V164). 41.81%,(V164) windThe and capacity 42.93%,turbine fact respectively.as thorse cut-offof the GE wind (3.6sl) speed wind of GE turbine (3.6sl) vary is higher from 100%than thaton July of VESTAS and August (V164). to 8.92% The capacity on November factors atof sitethe GEA; the (3.6sl) factors wind differ turbine from vary 100% from in 3.4. Output Energy July100% and on AugustJuly and 1 Augustto 8.91% to in 8.92% November on November at site B, at 94.55% site A; in the July factors to 8.64% differ in from February 100% atin siteJuly C and andThe August 96.49% cumulative 1on to July 8.91% annual to in9.22% wind November on energy January at output site at B,site and 94.55% D. an annual in July average to 8.64% capacity in February factor at of sitethree CAlthough and wind 96.49% turbines the onSIEMENS July at both to 9.22%(SWT113) offshore on andJanuary wind onshore turbin at site sitese hasD. are the tabulated lowest cut-off in Table wind5. In speed, general, it providestheAlthough output the energyhighest the SIEMENS of capacity all wind (SWT113)factors turbines at allwind are site higher turbins as the ate bothhascut-in the offshore wind lowest speed sites cut-off Aof and thiswind B wind andspeed, lowertur- it bineprovidesat thelower onshore the than highest sitetwo D.capacityother The wind annual factors turbines. energy at all siteThe outputs ascapacity the of offshorecut-in factors wind site for speed A SIEMENS is 1.34, of this 1.27 (SWT113) wind and tur- 1.24 windbinetimes lowerturbines higher than are than two the that highestother of onshorewind at site turbines. siteB on D Augu using Thest capacity VESTAS(100%) and factors (V164), the lowestfor GE SIEMENS (3.6sl) at site and C (SWT113) SIEMENSon Janu- arywind(SWT113) (9.62%). turbines windThe are annual turbines the highest mean respectively. capacityat site B on Similarly,factors Augu ofst theSIEMENS (100%) annual and (SWT113) output the lowest energy at sitesat at site offshoreA, C B, on C, Janu- siteand A Daryis are recorded(9.62%). 48.17%, The to 53.26%, be annual 21.2%, 41.81%, mean 14.75%, andcapacity and 42.93%, 12.2% factors respectively. more of thanSIEMENS the annual (SWT113) wind at energy sites A, output B, C, of and site DD are using 48.17%, VESTAS 53.26%, (V164), 41.81%, GE (3.6sl),and 42.93%, and SIEMENS respectively. (SWT113) wind turbines respectively. Sustainability 2021, 13, x FOR PEER REVIEW 16 of 20

Sustainability 2021, 13, 2862 15 of 18 3.4. Output Energy The cumulative annual wind energy output and an annual average capacity factor of three wind turbines at both offshore and onshore sites are tabulated in Table 5. In general, theTable output 5. Annual energy energy of all outputwind turbines and capacity are factorhigher of at selected both offshore wind turbines sites A at and monitoring B and lower sites. at the onshoreWind Turbine site D. The annual energy output Site of A offshore Site site B A is Site1.34, C 1.27 and Site 1.24 D times higher than that of onshore site D using VESTAS (V164), GE (3.6sl) and SIEMENS E (GWh) 36.33 40.15 28.76 29.98 (SWT113)VESTAS wind (V164) turbines respectively.out Similarly, the annual output energy at offshore site C (%) 43.66 48.24 34.56 36.02 A is recorded to be 21.2%, 14.75%,f and 12.2% more than the annual wind energy output E (GWh) 15.03 16.60 12.65 13.10 of site D usingGE (3.6sl) VESTAS (V164), outGE (3.6sl), and SIEMENS (SWT113) wind turbines respec- C (%) 47.68 52.66 40.12 41.55 tively. f E (GWh) 13.50 14.93 11.72 12.03 SIEMENS (SWT113) out Table 5. Annual energy output and Ccapacityf (%) factor of48.17 selected wind 53.26 turbines at 41.81 monitoring sites. 42.93

Wind Turbine Site A Site B Site C Site D The maximum annual output energy is estimated by VESTAS (V164) wind turbine at Eout (GWh) 36.33 40.15 28.76 29.98 site B withVESTAS 40.15 (V164) GWh/year while the lowest is recorded using SIEMENS (SWT113) at site C with 11.72 GWh/year. On the otherC hand,f (%) the highest43.66 annual 48.24 average 34.56 capacity 36.02 factor is Eout (GWh) 15.03 16.60 12.65 13.10 recorded usingGE (3.6sl) SIEMENS (SWT113) at site B with 53.26%, while the lowest is estimated by VESTAS (V164) at site C with 34.56%.Cf (%) This is because47.68 the52.66 SIEMENS 40.12 (SWT113) 41.55 wind turbine has the lowest rated powerE andout (GWh) cut-off wind13.50 speed, but14.93 the cut-in11.72 wind 12.03 speed of SIEMENS (SWT113) this wind turbine is also lower than otherCf (%) selected wind48.17 turbines. 53.26 The SIEMENS41.81 (SWT113)42.93 provides a higher capacity factor than other selected wind turbines as all sites’ wind speed distributionThe maximum mainly annual occurs output in low energy wind is speed estimated levels. by Compared VESTAS (V164) to other wind studied turbine wind at siteturbines, B with 40.15 SIEMENS GWh/year (SWT113) while is the the lowest most suitable is recorded candidate using sinceSIEMENS it provides (SWT113) the highestat site C annualwith 11.72 average GWh/year. capacity On factors the other for all hand, sites. the highest annual average capacity factor is recordedThe using annual SIEMENS generated (SWT113) output at power site B of with the 53.26%, SIEMENS while (SWT113) the lowest wind is estimated turbine for byoffshore VESTAS and (V164) onshore at site locations C with is34.56%. shown Th inis Figuresis because 15– the18. SIEMENS The obtained (SWT113) results wind show turbinethat the has generated the lowest output rated powerpower atand offshore cut-off siteswind A speed, and B but is higher the cut-in than wind onshore speed site of D thisduring windall turbine seasons. is also The lower seasonal than variationsother selected of wind wind power turbines. for theThe threeSIEMENS monitoring (SWT113) sites providesare noticeable a higher with capacity a significantly factor than high other output selected energy wind belt turbines during as the all warm sites’ months.wind speed distributionThe annual mainly electricity occurs in demand low wind in Oman speed islevels. highly Compared seasonal due to other to the studied subtropical wind cli- turbines,mate in SIEMENS this country. (SWT113) During theis the summer, most suitab the averagele candidate electricity since consumption it provides the is more highest than annualdouble average the average capacity electricity factors consumptionfor all sites. during the winter [28]. The peak of electricity demandThe annual happens generated in July due output to the power hot temperature, of the SIEMENS and consequently,(SWT113) wind significantly turbine for higher off- shoreair conditioning and onshore usage.locations In thisis shown study, in the Figure obtaineds 15–18. results The demonstrateobtained results that show offshore that windthe generatedturbines canoutput generate power more at offshore electrical sites energy A and than B is the higher land-based than onshore wind turbine site D duringduring peakall seasons.demand The in seasonal the summer. variations This specifies of wind thatpower the for offshore the three wind monitoring turbines notsites only are producenotice- higher energy but also produce more valuable electrical energy than the land-based wind able with a significantly high output energy belt during the warm months. turbines in the summer.

3200 3000

2500

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1500 Power (kW) 1000

500

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month

FigureFigure 15. 15. MonthlyMonthly output output power power of ofSIEMENS SIEMENS (SWT113) (SWT113) wind wind turbine turbine atat offshore offshore site site A. A. Sustainability 2021, 13, 2862 16 of 18 SustainabilitySustainabilitySustainability 20212021 2021,, , 13 1313,, , x xx FOR FORFOR PEER PEERPEER REVIEW REVIEWREVIEW 171717 of ofof 20 2020

320032003200 300030003000

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000 JanJanJan Feb Feb Feb Mar Mar Mar Apr Apr Apr May May May Jun Jun Jun Jul Jul Jul Aug Aug Aug Sep Sep Sep Oct Oct Oct Nov Nov Nov Dec Dec Dec MonthMonth Month FigureFigureFigure 16. 16.16. Monthly MonthlyMonthly output outputoutput power powerpower of ofof SIEMENS SIEMENSSIEMENS (SWT113) (SWT113)(SWT113) wind windwind turbine turbineturbine at at at offshore offshore offshore site site site B. B. B.

320032003200 300030003000

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000 JanJanJan Feb Feb Feb Mar Mar Mar Apr Apr Apr May May May Jun Jun Jun Jul Jul Jul Aug Aug Aug Sep Sep Sep Oct Oct Oct Nov Nov Nov Dec Dec Dec Month MonthMonth FigureFigureFigure 17. 17.17. Monthly MonthlyMonthly output outputoutput power powerpower of ofof SIEMENS SIEMENSSIEMENS (SWT113) (SWT113)(SWT113) wind windwind turbine turbineturbine at at at offshore offshore offshore site site site C. C. C.

320032003200 300030003000

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000 JanJanJan Feb Feb Feb Mar Mar Mar Apr Apr Apr May May May Jun Jun Jun Jul Jul Jul Aug Aug Aug Sep Sep Sep Oct Oct Oct Nov Nov Nov Dec Dec Dec MonthMonthMonth

FigureFigureFigure 18. 18.18. Monthly MonthlyMonthly output outputoutput power powerpower of ofof SIEMENS SIEMENSSIEMENS (SWT113) (SWT113)(SWT113) wind windwind turbine turbineturbine at at at onshore onshore onshore site site site D. D. D. 4. Discussion and Conclusions TheTheThe annual annualannual electricity electricityelectricity demand demanddemand in inin Oman OmanOman is isis highly highlyhighly seasonal seasonalseasonal due duedue to toto the thethe subtropical subtropicalsubtropical cli- cli-cli- matematemate Despite ininin thisthisthis country.country.country. that Oman DuringDuringDuring has athethethe long summer,summer,summer, shoreline thethethe extended averageaverageaverage electricityelectricityelectricity over 1700 consumptionconsumptionconsumption km, the wind isisisenergy moremoremore industry is still confined to onshore due to the lack of knowledge about offshore wind Sustainability 2021, 13, 2862 17 of 18

potential. This research is the first study that initiated knowledge about wind field char- acteristics and the offshore energy potential in Oman, using proxy data with hourly time resolution. The offshore wind power potential analysis in Oman territorial seas indicates that, in the most productive locations for offshore wind power development, the average annual wind speeds range from 19.91 to 7.44 (m/s) at 100 m above MSL. Due to Oman’s subtropical climate, the wind regime at monitoring sites is strongly affected, lower in cold seasons and considerably higher during hot seasons. The results show that the most promising offshore sites located in deep water will generate at least 1.34 times higher energy than the land-based site with the highest wind energy potential by the same commercial wind turbine. Therefore, harvesting higher offshore wind energy by moving from the coast to the deep-water requires floating . Based on obtained results, it can be concluded that offshore wind turbines generate higher energy and generate more valuable electrical energy than land-based wind turbines during peak demand in the summer. This specifies that offshore wind turbines provide great superiority over onshore wind turbines in Oman. Thus, offshore wind power development can be considered the primary option for expanding the share of renewable energy in Oman’s electric power generation. Overall, this work provides guidance in implementing and developing offshore wind power in Oman. Converting this offshore wind potential into real wind farm deployment still requires an in-depth analysis using bankable wind data. The reanalysis data utilized for this assessment of offshore wind energy potential contains uncertainty and should be completed by more reliable data. In recent years Floating LiDAR systems have emerged as effective wind resource assessment tools for offshore wind farms, with the potential to reduce installation costs compared to fixed met masts significantly. Floating LiDAR systems have become a promising alternative to fixed masts for offshore wind farms resource assessments with the potential to minimize costs relative to fixed masts. Floating LiDAR technology provides developers with a flexible way of understanding yield potential, enhancing investor confidence, and reducing financing costs. It requires the support of the national entities responsible for energy planning to develop knowledge about offshore wind energy needed to build a pipeline of bankable wind offshore projects for investors. Further analysis is also necessary to develop a complete understanding of potential at the country level, looking at challenges regarding grid capacity and integration issues, shipping lanes, migratory patterns, impacts on fisheries resources, and various logistical considerations.

Author Contributions: Conceptualization, A.A.-H. and S.H.A.K.; methodology, Y.C.; software, S.H.A.K.; validation, A.A.-H., and Y.C.; formal analysis, S.H.A.K.; investigation, A.A.-H.; resources, Y.C.; data curation, S.H.A.K.; writing—original draft preparation, S.H.A.K.; writing—review and editing, A.A.-H.; visualization, S.H.A.K.; supervision, A.A.-H. and Y.C.; project administration, A.A.-H.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Acknowledgments: This research was funded by Sultan Qaboos University (SQU) and British Petroleum (BP), under grant number BP/DVC/CESR/18/01. Conflicts of Interest: The authors declare no conflict of interest.

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