Offshore Wind Power Resource Assessment in the Gulf of North Suez

Abstract

Growing population, industrialization, and power requirements are adversely affecting the environment through increased greenhouse gases resulting from fossil fuel burning. Global greenhouse gas mitigation targets have led nations to promote clean and self-renewable sources of energy to address this environmental issue. Offshore wind power resources are relatively more attractive due to high winds, less turbulence, minimal visualization effects, and no interaction of infrastructure. The present study aims at conducting an offshore wind power resource assessment (OWPRA) at some locations in the Gulf of North Suez. For this purpose, the long-term hourly mean wind speed (WS) and wind direction above mean sea level (AMSL), as well as temperature and pressure data near the surface, are used. The data is obtained from ERA5 (fifth generation global climate reanalysis) at six (L1–L6) chosen offshore locations. The data covers a period of 43 years, between 1979 and 2021. The WS and direction are provided at 100 m AMSL, while temperature and pressure are available near water-surface level. At the L1 to L6 locations, the log-term mean WS and wind power density (WPD) values are found to be 7.55 m/s and 370 W/m², 6.37 m/s and 225 W/m², 6.91 m/s and 281 W/m², 5.48 m/s and 142 W/m², 4.30 m/s and 77 W/m², and 5.03 and 115 W/m² and at 100 m AMSL, respectively. The higher magnitudes of monthly and annual windy site identifier indices (MWSI and AWSI) of 18.68 and 57.41 and 12.70 and 42.94 at the L1 and L3 sites, and generally lower values of wind variability indices, are indicative of a favorable winds source, which is also supported by higher magnitudes of mean WS, WPD, annual energy yields, plant capacity factors, and wind duration at these sites. The cost of energy for the worst and the best cases are estimated as 10.120 USD/kWh and 1.274 USD/kWh at the L5 and L1 sites, corresponding to wind turbines WT1 and WT4. Based on this analysis, sites L1, L3, and L2 are recommended for wind farm development in order of preference. The wind variability and windy site identifier indices introduced will help decision-makers in targeting potential windy sites with more confidence.

1. Introduction

Growing global concern over adverse climatic changes due to increasing levels of greenhouse gas (GHG) emissions caused by fossil fuel burning for power generation are being addressed by enforcing the reduction-of-GHG-emissions targets on national levels. It is evident that these issues can be tackled by reducing the burning of fossil fuels, which means utilizing clean and self-renewing sources of energy. Such sources include wind, solar, geothermal, ocean, hydro, and biomass. Of these, wind, hydro, and solar are commercially acceptable and technologically matured. The present scope of the study is limited to offshore wind power resources in this part of the world which are yet to be explored.

For the successful deployment of wind power, onshore or offshore, comprehensive and accurate understanding of its potential is critical [1]. As the world is transiting towards cleaner and sustainable sources of electricity generation, offshore wind power has emerged as a potential solution [2,3]. Offshore wind farms have attracted the interest of legislators, energy companies, and environmentalists as a result of their vast untapped potential to generate substantial quantities of renewable energy [1]. Accordingly, in recent times, offshore wind power installed capacities have expanded from 3.056 GW to 54.259 GW, corresponding to the years 2010 to 2021, and reached 63.2 GW in 2022 [4]. Global wind power capacity increased by more than 93 GW in 2020, a phenomenal 53% growth over 2019 [5]. With this new addition, the overall global cumulative capacity has reached 743 GW [6].

Fast technological development in the wind power sector, such as the availability of efficient and larger-capacity wind turbines, improved offshore construction facilities, and advanced foundation designs, have contributed to offshore wind power expansion [7,8]. Compared to earlier years, offshore wind power generation has become economically more viable thanks to decreasing capital, engineering, installation, and maintenance costs [7,9]. A computer program has been developed to calculate the scantlings of the floating foundation; following DNV rules, a computer program was developed, and 3D Finite Element Analysis was conducted to check the adequacy of the estimated scantlings in the Red Sea [10]. The other drivers for investors and developers are government incentives, subsidies, and favorable policies towards the growth of offshore wind generation. Examples of these schemes include grid connectivity guarantees, power purchase agreements, tax incentives, renewable energy goals, and feed-in tariffs [9,11,12]. Growing concerns over adverse climate changes and the need to find sustainable energy sources have increased the demand for offshore wind power initiatives [13,14].

For the Goto Islands, Japan, offshore wind and tidal energy resources were assessed based on the annual energy shortage and surplus and battery capacity. In the absence of storage, an energy mix of 47% offshore wind, 31% solar, and 22% tidal was found to provide a minimum energy shortage of 29.26% and a surplus of 29.26% [15]. Using the same hybrid power system with the addition of battery storage (30 MW), the tidal energy system was the main contributor to the reduction of energy shortages and surplus values of up to 23.58% and 19.60%, respectively. A study on system-strength evaluation based on the interaction of wind farms and inverter resources was conducted via current fault detection at bus level [16].

Wind speed data analysis is the primary objective of wind power resources evaluation [17]. In order to understand wind characteristics such as annual, monthly, and diurnal WS, WPD, wind turbulence, wind frequency distribution, prevailing wind direction, Weibull distribution, etc., at potential locations, measured meteorological data are required [18]. This is essential for assuring wind-farm energy output and maintaining economic viability [19]. A Boolean mask model coupled with the Geographical Information System was used to identify offshore windy sites in the Red Sea area off the Saudi Arabian Coast [20]. The study identified 10 potential windy sites for the development of the first offshore wind farm in the KSA. The study further indicated that a 5 MW rated capacity wind turbine can generate more than 12.3 GW power. To comply with environmental regulatory issues for future wind farm development, the developer has to comply with environmental concerns, such as the existence of endangered species, the birds’ migratory paths, strategic offshore installations, and any adverse impacts on marine ecosystems [21]. Last but not the least, the competitive cost of energy per kilowatt hour is the key for the success of a wind power project or any power generation project.

The present study aims to assess the availability of wind power resources at six offshore locations in the Gulf of North Suez. Data for WS, wind direction, temperature, and pressure over a 43-year period (1979–2021) is obtained from the long-term fifth generation ERA5 dataset.

2. Important Aspects of Offshore Wind Power Development

Specific to offshore deployments, the most important factors which govern the cost of wind energy are the distance from the coast, the depth of water at each wind turbine location, marine life, offshore installation, shipping routes, and birds’ migratory paths. In Europe, offshore wind farm sizes previously averaged 25 MW and were located at a water depth of 7 m and approximately 5 km away from the coastline [22]. However, by 2021, these figures have shown average wind farm sizes of 591 MW in Europe at weighted average water depths of 39 m and around 23 km away from the shoreline. This technological advancement has resulted in an increased power output due to the availability of larger wind turbine-swept areas and higher hub heights over the period of 2010–2021. During this period, the wind turbine rotor diameters have experienced an increase of around 43% (from a weighted average value of 112 m to 160 m). On the other hand, the hub heights increased by 27% during the same period from a weighted average of 83 m to 105 m [22].

The weighted minimum, average, and maximum values of globally existing offshore wind levelized cost of energy, (LCOE), in USD/kWh and the plant capacity factors (PCFs) are shown in Figure 1a,b, respectively. Globally, the weighted average offshore LCOE has decreased by almost 60% between 2010 and 2021 (Renewable Energy Agency, 2022 [22]). The LCOE was 0.188 USD/kWh in 2010 and declined to 0.075 USD/kWh in 2021, as shown in Figure 1a. A continuous decrement in LCOE is observed from 2014 till 2021. Another attraction of OWP deployment is its increasing plant capacity factor (PCF) which increased from 38% in 2010 to 45% in 2013 and regained after touching a lowest value of 35% in 2014, Figure 1b.

3. Literature Review

As said earlier, OWP deployment is increasing, and for its profitable assessment, new and sophisticated tools are being developed and used. In Samothraki Island, an OWP resource assessment was conducted using Region of Interest (ROI) tool and Geographical Information System (GIS) software Version 4.12 [23]. The results showed good wind power availability in the southwest area of the island for the development of bottom-fixed and floating-type WT foundations. In 2008, the first offshore wind farm went into operation in China [24,25]. In Honk Kong, the southeastern offshore area is found to be suitable for wind farm deployment and can produce annually 112.81 × 10⁸ kWh of energy, which accounts for 25.06% of total annual consumption [26].

For Indian coastal regions, a mean WS and WPD of 7.0 to 10.0 m/s and 500 to 600 W/m² were reported, with the Tamil Nadu coastal areas having an exploitable wind frequency availability of >60% [27]. India has an estimated annual offshore net energy capacity of 365 GWh and 516 GWh based on two offshore wind turbines with rated capacities of 3.0 MW and 5.0 MW at a hub height of 80 m [28]. For Latin America, an annual technical offshore potential of 1759 W/m² was reported at 80 m AGL [29]. Offshore wind power densities of 80 W/m² and 160 W/m² were reported 10 m and 100 m above the mean sea level [30]. MERRA-2 and ERA5 data were used and offshore plant capacity factors of 32%, 37%, and 46% were reported for Tabasco, Campeche, and Yucatán locations in Mexico [31]. Similar types of offshore wind power resources have been reported for Oman [32] and Portugal [33].

Based on WS data at 100 m AGL during a 62-year period from 1959 to 2020 in the Mediterranean and Black Sea areas, an average offshore WPD of 373 W/m² was reported [34]. Another study [35] reported an LCOE of 123.4 USD/MWh by considering the contributions of distance from shoreline, the seasonal operational framework, rated power, hub height, wind farm installed capacity, and the operational life of the wind farm. However, relatively moderate LCOEs of 130–180 €/MWh (139.34–192.94 USD/MWh) were reported for Sardinia, Sicily and Malta, the Alborán Sea, and in the South Adriatic [36].

4. Materials and Methods

4.1. Site Desrciption

Accurate wind power resource assessment is key for the success of any wind power project. A total of six sites (L1 to L6) are considered in the Gulf of North Suez to study the wind speed and wind power characteristics, so as to prioritize the locations based on annual energy yield, plant capacity factor, cost of energy, annual and monthly wind variability, and windy site indices. The locations under consideration are shown in Figure 2. The latitude, longitude, water depth, and the distance from the Sinai coast are given in Table 1. A maximum of 66 m water depth is found for location L1, while there is a minimum of 2 m at L6, which is quite close to the shoreline. Location L3 is the farthest site from the Sinai coastline (32.86 km), while L6 is the nearest (0.681 km). The other locations are between 2 and 19 km distant from the Sinai shoreline.

4.2. Data Description

The European Centre for Medium-Range Weather Forecasting (ECMWF) has developed reanalysis data, and it is widely used by researchers, scientists, and agencies/organizations. Its widespread utilization is accounted for by its free availability, proven accuracy, being open source, and having long-term availability with continuity (since 1979 to date). The data include various meteorological parameters (WS, WD, ambient temperature, and surface pressure). Last but not least, the data have been proven to be accurate globally after comparative studies [23,37,38]. ERA5 is the fifth-generation dataset from ECMWF, with a significant improvement compared to the previously available ERA-Interim dataset [39]. In the ERA5 product, the hourly mean WS and WD is available at 100 m with a geographical resolution of 31 km [40] which is a relatively fine resolution compared to other globally available reanalysis datasets [41].

Some studies have reported bias in the performance of such datasets with WS intensity [42], seasons [43], and the regions [44]. Another study [44] compared the accuracy of third-generation reanalysis datasets CFSR, ERA-Interim, MERRA, and MERRA-2 to determine the suitability of the data for the Southwest 2.5 GW capacity offshore wind farm in South Korea. In terms of coefficient of determination, the authors reported that MERRA-2 was better than CFSR, which performed almost the same as MERRA, while MERRA was still better than ERA-Interim. The reanalysis data is employed as a long-term reference along with the actual measurements on islands or onshore locations. At offshore locations, the terrain-induced orographic forcing does not exist, hence, these reanalysis datasets result in a higher correlation [45].

The ERA5 reanalysis data was reported to be the best performer when compared with the measurements of the Climate Forecast System Version 2 (CFSv2), Cross-Calibrated Multi-Platform (CCMP), and ECMWF [46]. Over the period, there has been a noticeable improvement in the reanalysis datasets, such as the spatial and temporal resolutions improving to 0.25° and to 1 h, respectively. The present study utilizes the reanalysis (ERA5) dataset for the wind power resource assessment in the Gulf of North Suez. A total of 43 years of hourly mean WS, WD, ambient temperature, and pressure data spanning between 1979 and 2021 has been obtained for the six sites and analyzed. The WS and WD data is provided at 100 m above sea level, while the temperature is at 2 m, and the pressure at sea level.

The long-term site-specific mean WS values at 100 m AGL vary between a minimum of 4.304 m/s and a maximum of 7.547 m/s, corresponding to the L5 and L1 sites, with maximum all-time values of >15.0 m/s at all the sites. The cube-root-mean-cubed (CRMC) WS varies from 5.104 m/s at L5 to 8.616 m/s at L1. The minimum and maximum WPD values of 77 W/m² and 370 W/m² are estimated for the L5 and L1 offshore sites, respectively. The wind power classes (WPC) for all the sites are also included in Table 2 (row 15) based on WPD at 50 m AGL. At the L1 and L3 sites, the WPC is observed as 2-Marginal, while, at the remaining sites, it is 1-Poor. In general, highest values of all parameters are observed at the L1 site and the lowest at L5. The long-term annual mean values of temperature vary between 20.97 and 22.14 °C, those of mean pressure between 99.8 and 100.9 kPa, and those of air density between 1.160 and 1.17 kg/m³. The ambient temperature and the pressure are used to obtain the local site-dependent air density. The last row in Table 2 provides the frequency (F) of occurrences of WPD greater than or equal to 250 W/m². These values of F are used in the calculations of MWVI, AMWI, MWSI, and AWSI values.

4.3. Methodology

The main objective of the present OWPA in the Gulf of North Suez is to identify potential windy sites for offshore wind farm development in the near future. As a first step, as summarized in the work flowchart of Figure 3, the ERA5 data is downloaded for six locations in the Gulf of North Suez. Next, wind speed characteristics such as long-term, annual, monthly, and diurnal variations; prevailing WD; frequency analysis, annual and monthly wind variability indices; and windy site identifiers are estimated. After this, wind power, wind energy, plant capacity factors, etc. are determined for selected offshore wind turbines. Based on variability indices, the energy output, and the plant capacity factors, the potential windy sites’ suitability for offshore wind farm development are identified. Finally, the cost of wind energy generation per kilowatt hour is determined for the three most suitable sites, considering the additional construction costs of water-depth and the distance from the shoreline. The mathematical equations of the wind power resource assessment parameters are provided in Section 4.3.1, Section 4.3.2 and Section 4.3.3.

4.3.1. Wind Speed Characteristics

The annual, monthly, and diurnal trends of $\overline{V}$ and WPD are critical for proper wind power resources assessment. The mean $\stackrel{=}{V}$, the associated standard deviation, and the wind speed extrapolation are obtained from Equations (1)–(3).

(1)$\overline{V}=\frac{1}{N} \sum _{i=1}^N{V}_i$

(2)$\sigma =\sqrt{\frac{1}{N-1} \sum _{i=1}^N{\left(V_i-\overline{V}\right)}^2}$

(3)$V_{h2}=V_{h1}\times {\left(\frac{h2}{h1}\right)}^{\alpha }$

where h1 and h2 are the heights corresponding to measured wind speeds $V_{h1}$ and $V_{h2}$ and α is the wind shear exponent. In the present case, a value of α = 0.14 is used. The WPD, which is independent of the WT specifications, provides the power availability per unit of the rotor-swept area and is given by the following Equation (4):

(4)$WPD=\frac{1}{2} \rho V^3$

where N is the number of data points, WPD is the wind power density in W/m², $\rho$ is the air density in kg/m³, $\sigma$ is the standard deviation, and V is the hour mean wind speed in m/s. The annual (AWVI) and monthly (MWVI) wind variability indices are calculated as following [47].

(5)$AWVI=\frac{{WPD}_{MEY}-{WPD}_{LEY}}{{WPD}_{MEP}}$

where ${WPD}_{MEY}, {WPD}_{LEY}$, and ${WPD}_{MEP}$ are the WPDs for most energetic year (2000 for L1, L3, and L4; 1989 for L2 and L5; and 1995 for L6), least energetic year (2018 for L1 to L5 and 2014 for L6), and mean over the entire period of data collection (1979 to 2021), respectively. Similarly, the monthly wind variability indices are calculated using the following equation:

(6)$MWVI=\frac{{WPD}_{MEM}-{WPD}_{LEM}}{{WPD}_{MEP}}$

where ${WPD}_{MEM}, {WPD}_{LEM}$, and ${WPD}_{MEP}$ are the wind power densities for the most energetic month (June for L1 to L4 and L6 and September for L5), least energetic month (January for L1 to L5 and November for L6), and mean over the entire period of data collection (1979 to 2021), respectively. Lower values of these indices are preferred to minimize the wind turbulence and hence prolong the life of wind turbines. An additional index, the windy site identifier (WSI), is determined using the percentage frequency occurrences of WPD above 250 W/m². The WPD variability and consistency above a certain magnitude is important for continuous power production at a site. As mentioned in [48], exploitation of wind power is reliable above a WPD of 200 W/m². However, in the present case, this value is taken as 250 W/m², considering the local meteorological conditions, and it is calculated using the following equation [49]:

(7)$WSI=\frac{{WPD}_{MEP}\times F}{MWVI}$

where F is the percent frequency of WPD occurrences above 250 W/m² over the entire data collection period at each site (see Table 2 for details).

4.3.2. Wind Power and Energy Estimation

The wind power produced by a wind turbine and the annual energy yield (AEY) are calculated by as follows:

WP = WPD × A(8)

AEY = WP × 8760(9)

where A is the wind turbine rotor-swept area in m², 8760 is the number of hours in a year, WP and AEY are the gross power and annual energy yield. The corresponding plant capacity factor can be calculated as follows:

(10)$PCF=\frac{AEY (\mathrm{k}\mathrm{W}\mathrm{h}/\mathrm{y}\mathrm{r})}{Name Plate Capacity \left(\mathrm{k}\mathrm{W}\right)\times 8760 \left(\mathrm{h}\right)}$

4.3.3. Wind Energy Cost Estimation

Levilized Cost of energy (LCOE in USD/kWh) is calculated using the following simple formula:

(11)$LCOE=\frac{Total Investment \left(USD\right)}{AEY}$

The total investment cost includes the capital, installation, civil work, engineering, management, operation and maintenance, etc.

5. Results and Discussion

The WS data is analyzed and its characteristics, the effect of hub height on power and energy yields, plant capacity factor, GHG emissions, and LCOE, at all the sites, are discussed in Section 5.1, Section 5.2, Section 5.3, Section 5.4 and Section 5.5.

5.1. Variability of Wind Speed and Wind Power Density

The long-term site-dependent values of wind speed, CRMC wind speed, Weibull parameters, wind power density, mean energy content, energy pattern factors, annual and monthly wind variability indices, and windy site identifiers have been summarized in Table 1 and discussed earlier. The L1 site is situated at a water depth of 66 m and 18.51 km away from the Sinai coastline; it observes the maximum annual mean WS, WPD, scale parameter, mean energy content, and MWSI and AWSI; and it has reasonably acceptable values of MWVI and AWVI, which means stable WS availability during the year. A highest of 8.18 m/s and a lowest of 3.98 m/s annual mean WSs are observed at the L1 and L5 locations, respectively (Figure 4). Next, at the L3 site, the annual mean WS values remain around 6.91 m/s throughout the data reporting period. Annual mean WSs values of 5.03 to 6.37 m/s are observed at the L6 and L2 locations, which means that these two sites have almost the same wind resources but are far from each other. It is noticed that the annual WS trends at all these sites show decreasing trends, (Table 3), of −0.0064, −0.0046, −0.0054, −0.0032, −0.0021, and −0.0019 m/s per year with low values of coefficient of determination (R²) of <7%. The decreasing WS trends are insignificant, and the wind farms’ outputs will not be affected over the working life of a wind farm of 20 years or even more. Moreover, the wind patterns usually repeat after each decay. The WPD always remains above 300 W/m² at L1 (Figure 5). However, the annual trends of WPD are almost similar at all the sites, as can be seen from Figure 5. In general, minimum WS and WPD values occurred in 1997 and 2014 and the maximum in 2010, 2000, 1995, and 1992 at all the sites.

Monthly mean values of WS (not shown here) and WPD (Figure 6), of (>6.0 m/s and 300 W/m²), respectively, are observed during summertime (March-October) at L1. Increasing trends of WS and WPD are observed from January to June and then a decreasing towards the end of the year (Figure 6), at all the locations. The availability of higher winds in summer months is advantageous from a higher power demand point of view in summertime. Diurnal WS and WPD trends provide an insight into the availability of power during the day–night cycle and are critical for load-matching and grid stability, especially in the case of large wind farms of tens of hundreds MW size. In the present scenario, site L6 observes the largest diurnal difference in hourly mean WS values of 2.59 m/s, while L2 has the smallest of 1.38 m/s (Figure 7). Similarly, the highest diurnal cyclic changes of 186.0 W/m² (471.0 W/m² at hour 15:30 and 285.0 W/m² at hour 06:30) are observed at the L1 site, while the lowest of 120.7 W/m² (158.74 W/m² at hour 18:30 and 37.9 W/m² at hour 06:30) are observed at L5, as shown in Figure 8.

Large diurnal cyclic differences between the highest and lowest values of hourly mean WS and WPD values may be a matter of concern from a load-balancing and grid stability point of view, which may be addressed by power system studies. However, at all the sites, higher values of hourly mean WS and WPD are observed starting from midnight (00:00 h), and then decreasing trends are observed as time progresses till 06:00 h and then trends continue to increase till 18:00 h and finally decrease towards the end of the day (Figure 7 and Figure 8).

Windrose diagrams are used to identify the prevailing wind direction, and the directional dependence of the WS intensity and of the magnitude of the wind power availability. It is evident that at the L1 to L3 sites, the wind flows mostly (60%, 50%, and 45%) from the northwest direction (Figure 9). However, the prevailing WD at L4 is seen from the north. Overall, at all the sites, the wind blows prevalently from the north and the northwest directions. This characteristic is advantageous where WT structural stability and a longer working life are concerned. Statistically, the WS data are well-described by two-parameter (scale and shape) Weibull distribution (Hulio et al., 2019 [50]; Baseer et al., 2017 [51]; Bagiorgas et al., 2016 [52]; Bassyouni et al., 2015 [53]; Rehman et al., 2012 [54]). The Weibull scale parameter (A) at 100 m AGL is found to vary between 4.849 m/s and 8.453 m/s corresponding to the L5 and L1 locations, while the shape parameter is between 2.305 and 2.643, corresponding to the L5 and L3 sites (refer to Table 2). Weibull fits, obtained using these parameters calculated by four methods (maximum likelihood, least square, WAsP, and openwind), are compared with the measured data and are depicted for the worst case (location L1) in Figure 10a with R² values of <80% and the best case (location L5) in Figure 10b with R² values of >97% for all the methods. At all other locations, the R² values are between 80% and 99%. This confirms the suitability of the Weibull distribution function for the description of wind speed values.

Wind frequency or wind duration distribution (Figure 11) in different WS bins provides valuable information about the availability of useful wind above the cut-in speed of the WT. This ensures power production over a certain percent of time during the year. In the present case, the WS above 3.25 m/s and 3.75 m/s is observed to be available for 86.8 and 84.4%, 83.3 and 79.7%, 85.5 and 82.9%, 80.5 and 75.4%, 64.9 and 55.4%, and 76.0 and 69.4% of the timesat the L1, L2, L3, L4, L5, and L6 offshore sites under the focus of the present scope of work (Figure 11). From this analysis, it can be understood that sites L1, L3, and L2 can produce power for more than 80% of the time during the entire year from WTs having cut-in speeds of 3.25 and 3.75 m/s. This observation further guarantees the suitability of these sites for wind power deployment.

5.2. Wind Variability Indices and Windy Site Identifier

The annual and monthly mean wind variability indices (AWVI, MWVI, AWSI, and MWSI), as explained in Section 4.3, are calculated using Equations (5)–(7), and the resulting values are shown in Figure 12 and Figure 13. In general, the lower values of AWVI and MWVI indicate that the wind is less turbulent at all the sites and can be preferred for the prolonged working life of the wind turbines. Although the minimum values of AWVI and MWVI of 0.60 and 0.28 correspond to the sites L5 and L4 (Figure 12), they are not the preferred choices because the WS and WPD values are not suitable for wind power generation. On the other hand, locations L1, L2, and L3, with AWVI and MWVI indices of around 1.0 and 0.30, are preferred for wind power realization because of high annual, monthly, and diurnal mean WS, WPD, and wind duration values. Higher values of another useful index, the annual and monthly windy site identifier (AWSI and MWSI), are preferred for potential wind farm development. The AWSI and MWSI values are compared for all the sites in Figure 13. High values of AWSI and MWSI of 57.41 and 18.68 correspond to site L1, and the next preferred values of these indices of 42.94 and 12.70 are found for site L3. Based on these indices, it can be said that L1, L3, and L2 are the preferred sites for the development of wind farms in the Gulf of North Suez. The other three sites, L4 to L6, having lower values of AWSI and MWSI and of WS, WPD, and wind duration, are not preferred.

5.3. Wind Power Generation and Plant Capacity Factor Analysis

After the detailed analysis of wind speed characteristics at 100 m and the preliminary identification of the preferred windy sites in terms of WS, WPD, AWVI, MWVI, AWSI, MWSI, and wind duration values, more analysis is carried out with regards to wind power output, annual energy yield, and plant capacity factor to confirm the suitability of the potential wind sites. For this purpose, five offshore WTs of 3 to 6 MW rated capacities, 112 to 152 m rotor diameters, 3.0 to 4.0 m/s cut-in speed, and 12 to 15 m/s rated speed, are chosen (Table 4). The power curves of the chosen WTs are provided in Figure 14 while the losses considered for net power and PCF calculations are summarized in Table 5. The long-term average net wind power, AEY, and PCF values obtained using the selected WTs and the losses are given in Table 6.

Wind turbine WT2, with a rated capacity of 5.0 MW, produced the maximum power and energy at all the sites, as can be seen from Table 6 (rows 4 and 9), while WT3 and WT5, with rated capacities of 5.75 MW and 6.0 MW, produced less power. This may be accounted for by WT2 having the largest rotor diameter and a higher range of near-rated capacity realization at lower rated wind speeds compared to WT3 and WT5. Wind turbines WT2, WT3, and WT5 can produce 16.039, 15.413, and 15.396 GWh of energy annually at the L1 site, while, at the L3 and L2 sites, these values are 13.037, 12.338, and 11.962 GWh and 10.677, 10.013, and 9.528 GWH, respectively (Table 6). However, the net PCF is the highest, ranging from 9.56% to 42.28% corresponding to WT4 (rated capacity 3.0 MW) for sites L5 and L1. With regards to PCF, WT4 performed the best at all the sites, as can be observed from the data provided in Table 2 (row number 16). Hence, WT4 can be regarded as the most efficient turbine, WT2 is the next best option, and WT3 is the third possibility. In respect of potential wind sites, the wind power, AEY, and PCF values suggest L1, L3, and L2 as the best sites in order of preference, while the others can be regarded as relatively unfavorable. In Egypt, the per capita electricity consumption is 1343.17 kWh (Energy Consumption in Egypt, 2023 [55]). Accordingly, a single wind turbine (WT2), if installed at site L1, can serve energy to approximately 11,941 households and can displace 3132.5 tons of CO₂ equivalent GHG, annually (Table 7). This simply means that around 574 cars and light trucks are not used. At the L3 and L2 sites, with the installation of a single WT2 turbine, approximately 2553.2 and 2085.3 tons of GHG can be prevented from entering into the local atmosphere and 9733 and 7949 households can be served power, annually. A comparative representation of households served with respect to type of wind turbines and locations is displayed in Figure 15.

5.4. Effect of Hub Height on Annual Power Yield and PCF

In general, WS increases with height and results in a higher power yield. A numerical experiment is conducted to see the effect of hub height on annual power production and the PCF. The annual power production and the PCF are calculated at 100 m and at extrapolated WSs at 120 m and 140 m for all the chosen WTs. Obvious increased annual power yields and PCFs are observed at all the sites. A typical pair of such cases for the L1 and L3 locations are depicted in Figure 16. It is observed that power production and PCFs increases visibly at a hub height of 120 m compared to that of 100 m. However, this increase is seen to be less significant at a 140 m hub height compared to that at 120 m. On average, a 6.0 to 9.0% increase in wind power output is obtained with an increase of 20 m in hub height from 100 m to 120 m. For a further addition of 20 m in hub height (from 120 m to 140 m), a power output enhancement of 1.0 to 2.5% is observed. Similarly, the PCF values increased between 0.5 to 2.0% for an increase of an initial 20 m in hub height and 0.15 to 0.5% for another 20 m hub-height increment. So, it can be said that an initial increase of 20 m in hub height is better than opting for a 40 m increment in hub height due to certain technical and economic reasons.

5.5. Levelized Cost of Energy (LCOE—USD/kWh)

The total cost of offshore wind farms includes wind turbines, installation, electrical infrastructure, distance from the coast, depth of water, and the power evacuation system. The weighted average offshore wind power installation costs varied from 2858 USD/kW to 5584 USD/kW with an overall mean of 4720.25 USD/kW during 2010 to 2021. This average installation cost is not limited to a specific location, rather it includes various geographical areas/regions. In the present case, the average installation cost of 4720.25 USD/kW is used to calculate the LCOE at all the sites. In general, the lowest LCOEs are observed at L1 and the highest at the L5 site for all the chosen wind turbines (Figure 17). In terms of LCOE, L3 is the next best site, while L2 is the third-best option. With regards to wind turbine performance, WT4 consistently performed the best at all the sites, while WT2 is the next best, and WT1 is the third best.

The LCOE values at the L1 site vary between 1.274 and 1.857 USD/kWh corresponding to wind turbines WT4 and WT1 of 3.0 and 5.0 MW rated capacities (see Figure 17). Although being the best option from a wind resource point of view, the LCOE at L1 is 10- to 15-fold higher compared to the LCOE of 0.1234 USD/kWh reported for New York State [35]. In the Balearic Islands and the North Adriatic, Tyrrhenian and Levantine Seas, a LCOE of 0.268 USD/kWh was reported (1/5th of the LCOE in the present case), while for Sardinia, Sicily and Malta, in the Alborán Sea, and in the South Adriatic; a LCOE of 0.193 USD/kWh (around 1/7th of the LCOE in the present case) was reported by [36]. Compared to all the reported values, the LCOE in the present case is many fold higher, which may be attributed to the assumed average total installation cost of 4720.25 USD/kW, the local wind resources and type of wind turbine/s.

6. Conclusions

The present study conducted an offshore wind power resource assessment for offshore sites in the Gulf of North Suez. The long-term hourly average wind speed and direction data at 100 m AGL and temperature, pressure, and relative humidity near the water surface has been used for the assessment. Meteorological data for the six sites, covering a period of 43 years (1979 to 2021), has been used. Specifically, the long-term diurnal, monthly, and annual trends of the wind speed, wind power density, wind frequency, wind duration, wind power, plant capacity factors, and LCOE have been analyzed for all the sites with the aim of identifying the best possible offshore windy sites in the region for clean and renewable power production. To further support the decision, monthly and annual wind speed variability and windy site identifier indices are introduced. Based on this detailed analysis, the following conclusive remarks are made:

• The long-term mean wind speeds vary between 4.304 m/s and 7.547 m/s corresponding to the L5 and L1 sites, while the respective wind WPD values are estimated to be 77 W/m² and 370 W/m². The prevailing wind directions are found to be from the north and northwest. This means that less wind turbulence and wind-veering and backing effects result in less stress on the wind turbine structures, assuring a longer life for the wind turbines.

• The Weibull shape and scale parameters ranged between 2.305 (L5) and 2.643 (L3 and L4) and 4.849 m/s (L5) and 8.453 m/s (L1). Similarly, the mean energy content and energy pattern factors were of 677 kWh/m²/yr at L5 and 3240 kWh/m²/yr at L1 and 1.476 at L3 and 1.668 at L5, respectively.

• Lower values of annual and monthly wind variability indices (AWVI and MWVI) are preferred as being representative of the less turbulent nature of the winds, which assures a longer working life for the WTs. In the present case, AWVI varies from 0.28 to 0.35 at L4 and L1, while MWVI varies between 0.60 and 1.09 at the L6 and L1 sites. At potential windy sites (L1, L3, and L2), AWVI and MWVI values are around 0.30 and 1.00, which simply means that winds at these sites are less turbulent and will be good for wind turbines’ longer working life. Finally, higher values of AWSI and MWSI are opted when selecting a potential windy site. In the present case, the highest value of 57.41 is obtained corresponding to the L1 site, while 42.94 and 24.41 correspond to L3 and L2. Similarly, monthly windy site identifier (MWSI) values of 18.68, 12.7, and 8.08 are estimated for the L1, L3, and L2 sites, respectively. These values assure the suitability of sites in order of preference as L1, L2, and L3. The remaining L4 to L6 sites are not suitable compared to the L1 to L3 sites.

• Higher PCFs of 42.8%, 36.62%, 34.67%, 29.29%, and 29.02% are found at the L1 site corresponding to wind turbines WT4, WT2, WT3, WT5, and WT1, while the minimum of <10% is found at L5. The L3 and L2 sites are rated to be the next best in terms of PCFs of 35.12%, 29.85%, 27.75%, 22.76, 22.19% and 28.85%, 24.38%, 22.52%, 18.13, 17.58 with respect to the order of turbine mentioned above. Based on PCF values, WT4 is proved to be the best performer at all the sites, while WT2 and WT3 are the second and the third best.

• Based on a weighted average total installation cost of 4720.25 USD/kW, during 2010 to 2021, the LCOE values for the worst and the best cases are found to be 1.274 USD/kWh and 10.120 USD/kWh corresponding to the L1 site with WT4 wind turbine and the L5 site with WT1 turbine. In general, the lowest values of LCOE are registered at L1, the next lowest at L3, and the third at L2. At L4 to L6, the LCOE values are very high and are not recommended.

Author Contributions

Conceptualization, S.R.; Writing—original draft, S.R., K.I., N.I.I., M.A.M. and A.A. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

Authors would like to acknowledge the support provided by the IRC for Renewable Energy and Power Systems (IRC-REPS), King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia under the grant number INRE 2224.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. (a) Offshore globally weighted average levelized cost of energy (LCOE) (Renewable Energy Agency, 2022 [22]). (b) Offshore globally weighted average plant capacity factor, (Renewable Energy Agency, 2022 [22]).

Figure 1. (a) Offshore globally weighted average levelized cost of energy (LCOE) (Renewable Energy Agency, 2022 [22]). (b) Offshore globally weighted average plant capacity factor, (Renewable Energy Agency, 2022 [22]).

Figure 2. Geographical locations of the selected offshore sites in the Gulf of Suez (North).

Figure 3. Flow diagram of the methodology used in the present analysis.

Figure 4. Annual mean WS trends at selected sites (1979–2021).

Figure 5. Annual trends of WPD (1979–2021).

Figure 6. Monthly mean variation of WPD (1979–2021).

Figure 7. Diurnal variation of mean WS (1979–2021).

Figure 8. Diurnal variation of WPD (1979–2021).

Figure 9. Prevailing wind directions at the offshore sites under assessment.

Figure 10. Weibull fit distributions (a) the worst and (b) the best case.

Figure 10. Weibull fit distributions (a) the worst and (b) the best case.

Figure 11. Frequency distribution at the offshore locations under assessment.

Figure 12. Monthly (MWVI) and annual (AWVI) wind variability indices.

Figure 13. Monthly (MWSI) and annual (AWSI) windy site identification indices.

Figure 14. Wind power curves of the chosen offshore wind turbines.

Figure 15. Number of households served wind energy annually depending on the wind turbine type and the location.

Figure 16. Variation of annual power and PCFs with hub heights at (a,b) L1 and (c,d) L3 sites.

Figure 17. Cost of wind energy at all the sites with respect to wind turbine.

Table 1

Geographical coordinates of the selected offshore sites.

Location			Bathymetry			Distance from Coast
Name	Lat, °N	Lon, °E	Lat, °N	Lon, °E	Depth, m	km
L1	28.80	33.00	28.804	33.0041	66	18.51
L2	29.10	32.70	29.1041	32.7041	33	3.37
L3	29.10	33.30	29.1041	33.0041	43	32.86
L4	29.40	32.70	29.4041	32.7041	47	14.01
L5	29.70	32.40	29.7041	32.4041	30	2.34
L6	29.70	32.70	29.7041	32.6875	2	0.681

Table 2

Long-term (1979–2021), site-specific summary of WS, WPD, etc.

Variable	L1	L2	L3	L4	L5	L6
Latitude (N)	28.80	29.10	29.10	29.40	29.70	29.70
Longitude (E)	33.00	32.70	33.00	32.70	32.40	32.70
Mean wind speed (m/s)	7.547	6.371	6.905	5.483	4.304	5.030
Max wind speed (m/s)	17.560	17.840	18.030	17.690	15.250	18.620
CRMC wind speed (m/s)	8.616	7.303	7.863	6.267	5.104	5.835
Mean Wind Direction (°)	341.1	340.4	342.7	350.4	341.3	357.0
Weibull k	2.604	2.582	2.643	2.643	2.305	2.497
Weibull A (m/s)	8.453	7.147	7.730	6.150	4.849	5.655
Mean WPD (W/m²)	370	225	281	142	77	115
Mean energy content (kWh/m2/yr)	3240	1972	2465	1241	677	1003
Energy pattern factor	1.488	1.507	1.476	1.493	1.668	1.561
Frequency of calms (%)	10.58	13.07	11.66	14.89	26.1	18.01
WPD at 50 m (W/m²)	276	168	210	106	58	86
Wind power class	2 (Marginal)	1 (Poor)	2 (Marginal)	1 (Poor)	1 (Poor)	1 (Poor)
Mean Temperature (°C)	21.92	21.54	22.14	21.09	21.36	20.97
Mean Pressure (kPa)	100.6	100.4	100.9	99.8	100.8	99.9
Mean Air Density (kg/m3)	1.165	1.164	1.167	1.160	1.170	1.161
WPD frequency occurrences above 250 W/m2	183,713	207,343	137,826	174,391	63,458	23,875

Table 3

Long-term linear trends of wind speed.

Location	Linear Equation	R2
L1	Y = −0.0062 × WS + 7.6828	0.0664
L2	Y = −0.0046 × WS + 6.4718	0.0561
L3	Y = −0.0054 × WS + 7.0251	0.0703
L4	Y = −0.0032 × WS + 5.5540	0.0569
L5	Y = −0.0021 × WS + 4.3492	0.0450
L6	Y = −0.0019 × WS + 5.0711	0.0286

Table 4

Technical specifications of chosen offshore wind turbines.

Wind Turbine	Rated Capacity (MW)	Rotor Diameter (m)	Cut-In Speed (m/s)	Rated Speed (m/s)	Cut-Out Speed (m/s)
WT1	5.00	116	4.0	13.0	25.0
WT2	5.00	132	3.0	15.0	30.0
WT3	5.75	126	3.0	13.0	30.0
WT4	3.00	112	3.0	14.0	25.0
WT5	6.00	128	3.5	12.0	30.0

Table 5

Assumed losses used for net power and PCF estimations.

Loss Item	Loss (%)
Availability	3
Wake effect	5
Turbine performance	4
Electrical	2
Environmental	0
Curtailment	0
Other	0
Overall	13.3053

Table 6

Net wind power, AEY and PCF at 120 m hub height for all the sites.

HH = 120 m	Hub Height	L1	L2	L3	L4	L5	L6
	WS (m/s)	7.75	6.54	7.09	5.63	4.42	5.16
WT1	Net Wind Power (MW)	1.451	0.879	1.110	0.533	0.266	0.418
WT2		1.831	1.219	1.492	0.774	0.410	0.615
WT3		1.760	1.143	1.408	0.729	0.390	0.582
WT4		1.269	0.866	1.054	0.553	0.287	0.437
WT5		1.758	1.088	1.366	0.659	0.325	0.513
WT1	Net AEY (GWh)	12.710	7.699	9.719	4.669	2.332	3.661
WT2		16.039	10.677	13.073	6.784	3.589	5.385
WT3		15.413	10.013	12.338	6.384	3.412	5.100
WT4		11.112	7.583	9.230	4.848	2.513	3.825
WT5		15.396	9.528	11.962	5.773	2.845	4.498
WT1	Net PCF (%)	29.02	17.58	22.19	10.66	5.32	8.36
WT2		36.62	24.38	29.85	15.49	8.19	12.3
WT3		34.67	22.52	27.75	14.36	7.67	11.47
WT4		42.28	28.85	35.12	18.45	9.56	14.55
WT5		29.29	18.13	22.76	10.98	5.41	8.56
WT1	Percentage of time at Zero Power	13.04	16.44	14.25	19.25	34.48	23.66
WT2		5.55	6.66	6.39	7.35	11.86	8.42
WT3		10.33	12.77	11.4	14.57	25.38	17.58
WT4		10.27	12.71	11.33	14.48	25.21	17.47
WT5		10.43	12.91	11.5	14.73	25.71	17.8
WT1	Percentage of time at Rated Power	1.66	0.13	0.29	0.07	0.04	0.13
WT2		0.04	0.00	0.03	0.01	0.00	0.02
WT3		0.78	0.06	0.14	0.05	0.02	0.09
WT4		0.32	0.02	0.07	0.04	0.01	0.06
WT5		0.74	0.06	0.14	0.05	0.02	0.09

Table 7

GHG emission-reduction at proposed offshore sites.

WTs	Location	L1	L2	L3	L4	L5	L6
WT1	Annual GHG emissions (tons)	2482.3	1503.6	1898.2	911.9	455.5	714.9
WT2		3132.5	2085.3	2553.2	1324.9	700.9	1051.7
WT3		3010.1	1955.6	2409.5	1246.8	666.3	996.1
WT4		2170.3	1480.9	1802.6	946.8	490.9	746.9
WT5		3006.7	1860.9	2336.1	1127.4	555.7	878.4
WT1	Households servedpower	9463	5732	7236	3476	1736	2725
WT2		11,941	7949	9733	5051	2672	4009
WT3		11,475	7455	9185	4753	2540	3797
WT4		8273	5645	6872	3609	1871	2847
WT5		11,462	7094	8905	4298	2118	3348
WT1	Cars and light trucks not used	455	275	348	167	83	131
WT2		574	382	468	243	128	193
WT3		551	358	441	228	122	182
WT4		397	271	330	173	90	137
WT5		551	341	428	206	102	161

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