Windy Sites Prioritization in the Saudi Waters of the Southern Red Sea

Abstract

Offshore wind power resources in the Red Sea waters of Saudi Arabia are yet to be explored. The objective of the present study is to assess offshore wind power resources at 49 locations in the Saudi waters of the Red Sea and prioritize the sites based on wind characteristics. To accomplish the set objective, long-term hourly mean wind speed (WS) and wind direction (WD) at 100 m above mean sea level, temperature, and pressure data near the surface were used at sites L1-L49 over 43 years from 1979 to 2021. The long-term mean WS and wind power density (WPD) varied between 3.83 m/s and 66.6 W/m², and 6.39 m/s and 280.9 W/m² corresponding to sites L44 and L8. However, higher magnitudes of WS >5 m/s were observed at 34 sites and WPD of > 200 W/m² at 21 sites. In general, WS, WPD, annual energy yield, mean windy site identifier, plant capacity factor, etc. were found to be increasing from east to west and from south to north. Similarly, the mean wind variability index and cost of energy were observed to be decreasing as one moves from east to west and south to north in the Saudi waters of the Red Sea.

1. Introduction

Noticeable transformation of renewable energy has been observed in the wind power sector in general and offshore in particular during the last two decades. Offshore wind power development has grown at a relatively fast pace from its infancy portfolio in the early 2000s, to a well-recognized sector driven by fast technological advancements and commercial acceptance [1,2]. Furthermore, the availability of advanced and technologically more mature large capacity offshore wind turbines has boosted the sector’s growth [3].

Earlier research on the recognition of untapped offshore wind potential by Archer and Jacobson in 2005 [4] was instrumental in pointing out that harnessing even a small fraction of this resource could significantly contribute to global energy demands. With the opening of the largest offshore wind farm with an installed capacity of 630 MW in 2013, offshore wind power became a reality [5]. In 2010, global offshore wind power capacity was approximately 3 GW [6]. By the year 2018, it increased to 18 GW [7]. With the Walney offshore wind farm extension of 657 MW capacity, the UK became the world’s largest offshore wind farm (OWF) developer [8]. Cumulative offshore wind power (OWP) capacity reached 35 GW at the end of 2021 [9]. In the UK and Germany, OWP growth has been quite fast where their energy mix portfolio is concerned, and a lot of research effort has been put into various aspects of the growth of this sector, as per Sacco et al. [10]. The OWP-related levelized cost of energy (LCOE) has seen a significant decline, dropping to below 50 USD/MWh in some regions [11]. The GWEC has forecast a cumulative global capacity buildup of 234 GW by 2030 [12].

As observed from the literature, the UK remains a global leader in OWP capacity buildup with over 13 GW installed by the end of 2023 [13,14]. China has expanded its OWP capacity to comply with its planned roadmap of 18 GW and 30 GW capacity buildup by 2025 and 2030, respectively [15]. Guangdong province in China has become a hub for OWP development with the Yangjiang Shapa Phase 2 project adding 400 MW to the grid [16]. The United States has also planned for OWP development of 30 GW by 2030 [17]. The first large-scale OWF in Vineyard is set to contribute 800 MW by 2024 [18]. Germany has set a target to boost its OWP capacity to 7.5 GW by 2023. Denmark, as a pioneer in OWP development, has planned to build up a capacity of 2.5 GW by 2023 [19]. The Hollandse Kust Zuid OWF will contribute 1.5 GW power by the end of 2024 to the national energy mix portfolio and the Dutch government is expected to achieve a capacity of 21 GW by 2030 [20].

In the context of Saudi Arabian waters in the Red Sea, substantial but not yet explored and largely untapped potential exists for OWP development [21]. In this context, the Gulf of Suez has been identified with good annual mean wind speeds of 7.5 to 8.5 m/s 100 m above mean sea level (AMSL) [22]. The main hurdle behind slow growth of the wind power sector in this region is due to the prime national focus on oil and gas reserves [23].

The slow growth is further compounded by the region’s harsh environmental conditions leading to higher maintenance costs and technical complexities [24]. With all of these odds, efforts are being made by the Kingdom of Saudi Arabia to increase the renewable energy contribution in its existing energy mix portfolio. As part of Saudi Arabian Vision 2030 and national strategies, ambitious renewable energy targets have been set to be accomplished. A wind power resource assessment by the Saudi Ministry of Energy estimated the technical potential of more than 10 GW along the Saudi Red Sea coast [25]. For example, the Damut Aljandal and Duba onshore wind power projects of 400 and 300 MW installed capacities are first initiatives in Saudi Arabia [26]. The geographical and meteorological characteristics along the Saudi Arabian Red Sea region make it suitable for OWP development.

It has been emphasized that OWP deployment will be very useful and effective to fulfill the energy requirements of the coastal regions, as per Musial et al. [27]. With regards to OWP potential in the Gulf of North Suez, Rehman et al. [28] reported a WPD range of 300 to 500 W/m² at 100 m AMSL, which is an important indicator of potential yield. Nearshore areas exhibit a gradual slope with water depths ranging from 20 to 50 m within a distance of 10 to 20 km from the shoreline [29]. These conditions are conducive for the deployment of fixed-bottom wind turbines, which currently dominate the offshore wind market.

Due to its long shoreline, the Red Sea offers a natural wind corridor, making it a possible area for wind farm development. Since the Red Sea coast is very long, the present scope of work conducts the resources assessment between latitudes 16.50° and 18.25° to identify feasible sites. The increasing need for clean and self-renewing sources of energy has made wind energy utilization essential for sustainable development. To determine the best places for offshore wind energy production, this study aims at ranking the windy spots in the southern Red Sea’s Saudi waters. Consistent with Saudi Arabia’s Vision 2030 plan, the present investigation’s results will support the country’s objective of increasing its share of renewable energy.

This study’s key innovation is the evaluation of offshore wind power resources across 49 locations in Saudi waters of the Red Sea, with site prioritization based on wind characteristics. To accomplish this, it leverages long-term hourly data on mean wind speed and direction at 100 m above mean sea level, as well as temperature and surface pressure data, gathered from sites L1–L49 over a 43-year span from 1979 to 2021. Though straightforward, the proposed method thoroughly considers site-specific criteria, notably extreme wind conditions, depth, and surface characteristics. This comprehensive approach leverages essential criteria to assess the suitability of offshore wind farm locations, offering valuable insights for investors and stakeholders.

2. Site and Data Description

2.1. Site Description

An accurate WPRA is the key to any profitable and attractive wind power project. In this study, a total of 49 sites (L1 to L49) are considered in the Saudi waters of the southern Red Sea to study the WS and power characteristics in order to prioritize the windy locations based on long-term annual mean WS, WPD, annual energy yield (AEY), PCF, monthly wind variability index (MWVI), mean windy site identifier (MWSI), and wind duration, beside others. The offshore locations along the water depth and the distance from the Saudi shoreline are depicted in Figure 1a,b, respectively. The water depth varies from a minimum of <5 m at L5, L8, L19, and L27, to a maximum of 1582.36 m at L44. Similarly, the distances from the coast vary from <5 km (L8, L15, L29, and L37) to more than 150 km (L1, L9, L22, L30, L31, and L38), as in Figure 1b. The geographical coordinates, the water depth, and the distance from the coast (DCoast) are also provided in

Table 1. Geographical coordinates of the offshore sites under investigation.

Location			Depth	DCoast	Location			Depth	DCoast
Name	Lat, °N	Lon, °E	(m)	(km)	Name	Lat, °N	Lon, °E	(m)	(km)
L1	16.50	41.00	−878.04	156.81	L26	17.40	41.40	−22.70	63.23
L2	16.50	41.25	−1135.72	132.38	L27	17.40	41.70	−2.06	45.91
L3	16.50	41.50	−424.03	108.96	L28	17.40	42.00	−43.42	23.45
L4	16.50	41.75	−102.43	87.42	L29	17.40	42.30	−11.17	2.23
L5	16.50	42.00	−4.18	69.53	L30	17.70	39.90	−610.39	175.45
L6	16.50	42.25	−69.93	49.76	L31	17.70	40.20	−1171.65	149.16
L7	16.50	42.50	−34.93	23.79	L32	17.70	40.50	−476.09	120.67
L8	16.50	42.75	−4.87	0.13	L33	17.70	40.80	−74.37	93.99
L9	16.80	40.80	−1284.92	155.21	L34	17.70	41.10	−39.86	66.01
L10	16.80	41.10	−916.00	136.08	L35	17.70	41.40	−33.62	40.75
L11	16.80	41.40	−39.04	105.96	L36	17.70	41.70	−13.06	17.88
L12	16.80	41.70	−31.71	75.20	L37	17.70	42.00	−14.68	0.50
L13	16.80	42.00	−7.48	46.09	L38	18.00	39.90	−1277.47	158.54
L14	16.80	42.30	−32.94	25.20	L39	18.00	40.20	−1272.07	130.60
L15	16.80	42.60	−6.97	3.40	L40	18.00	40.50	−358.83	104.82
L16	17.10	40.80	−927.94	132.18	L41	18.00	40.80	−59.35	77.75
L17	17.10	41.10	−47.32	109.17	L42	18.00	41.10	−30.91	49.00
L18	17.10	41.40	−28.57	91.58	L43	18.00	41.40	−48.09	22.23
L19	17.10	41.70	−3.56	68.55	L44	18.30	39.90	−1582.36	145.15
L20	17.10	42.00	−62.04	37.60	L45	18.30	40.20	−715.73	115.50
L21	17.10	42.30	−27.38	6.69	L46	18.30	40.50	−279.74	86.96
L22	17.40	40.20	−1064.20	165.25	L47	18.30	40.80	−74.11	59.30
L23	17.40	40.50	−1100.56	137.80	L48	18.30	41.10	−9.42	37.21
L24	17.40	40.80	−529.01	111.02	L49	18.30	41.40	−9.10	7.71
L25	17.40	41.10	−18.48	86.66

for all the locations under consideration. This information is necessary for COE estimation and from a logistics point of view.

2.2. Data Description

The reanalysis data, developed by the European Centre for Medium-Range Weather Forecasting (ECMWF), is widely used by environmentalists, meteorologists, researchers, scientists, and agencies/organizations. The wind industry and researchers rely on meteorological reanalysis data sets, like ERA5 [30,31]. Widespread utilization of these data sets is due to their proven accuracy, open source, and the continuity of long-term availability (from 1979 to date) [32,33]. At offshore locations, the terrain-induced orographic forcing does not exist, hence, these reanalysis data sets result in higher correlation [34]. The data include wind speed (WS), wind direction (WD), ambient temperature, and surface pressure. The data set ERA5 is the fifth generation from the ECMWF with improvements over previously available ERA-Interim data sets [35]. In the ERA5 data set, the hourly mean WS and WD are available at 100 m with a geographical resolution of 31 km [36], which is relatively good compared to other globally available reanalysis data sets [37]. The present study utilizes the reanalysis ERA5 data set for the wind power resource assessment in the southern Red Sea. A total of 43 years hourly mean WS, WD, ambient temperature, and pressure values between 1979 and 2021 were downloaded for 49 sites [38].

3. Methodology

The aim of the present offshore wind power resources assessment (OWPRA) in the Saudi waters of the southern Red Sea was to find potential windy sites for OWF development in the near future. The adopted methodological approach is shown in the flowchart in Figure 2. The ERA5 data were downloaded for all locations in the Saudi waters of the southern Red Sea. The wind speed characteristics, such as long-term, annual, monthly, and diurnal variations; prevailing WD; frequency analysis; and MWVI are calculated. The MWSI values are obtained using the approach given in [39,40,41,42]. Next, WP, AEY, PCF, COE, etc. are determined for selected offshore wind turbines. Based on WS, AEY, PCF, MWSI, COE; etc., suitable sites are identified and prioritized for OWF development. The annual, monthly, and diurnal trends of WS and WPD are critical for proper wind power resource assessment at the hub height of the wind turbine. The WS is extrapolated to the hub height in Equation (1) and the WPD is calculated from Equation (2):

$$V_{h2}=V_{h1} \ast {\left({\displaystyle \frac{h2}{h1}}\right)}^{\alpha }$$

(1)

$$WPD={\displaystyle \frac{1}{2}} \rho V^3$$

(2)

where $V_{h1}$ and $V_{h2}$ are the wind speeds in m/s at heights h₁ and h₂, α is the wind shear exponent, $\rho$ is the air density in kg/m³, and V is the hourly mean wind speed in m/s. In the present case, a value of α is set at 0.14 [28]. Two significant wind speeds, the most probable (v_mp) and maximum energy carrying (v_maxE), are calculated as follows [28,43,44]:

$$v_{mp}=c {\left({\displaystyle \frac{k-1}{k}}\right)}^{{\displaystyle \frac{1}{k}}}$$

(3)

$$v_{maxE}=c {\left({\displaystyle \frac{k+2}{k}}\right)}^{{\displaystyle \frac{1}{k}}}$$

(4)

The maximum energy-carrying wind speed (v_maxE) is defined as the speed that carries the maximum amount of energy [44,45]. Furthermore, it is the WS value that has high frequency and high magnitude, so possessing relatively higher kinetic energy.

The percent frequency of occurrence of WPD above 200 W/m², F, which is used in WSI calculations is provided in

Table 2. Site-specific summary of WS, WPD, etc. at 120 m AMSL (1979–2021).

Sites	WS (m/s)	WD (°)	c (m/s)	k	WPD (W/m2)	F (%) > 200 W/m2	Temp (°C)	Wind Duration (%)	MWVI	MWSI	${\mathit{v}}_{\mathit{m}\mathit{p}}$	Vmax,E
L44	6.39	14.2	7.20	1.97	280.59	40.29	27.93	77	1.84	6.14	5.023	10.288
L38	5.91	357.5	6.64	1.74	253.04	34.47	28.00	70	1.87	4.67	4.068	10.287
L30	5.81	11.9	6.36	1.77	236.49	33.39	28.01	70	2.93	0.72	3.987	9.738
L45	5.72	325.2	6.41	1.65	246.74	31.97	27.99	67	2.35	3.36	3.642	10.363
L39	5.68	339.9	6.37	1.67	237.89	31.81	28.02	67	2.03	3.73	3.692	10.194
L31	5.67	356.4	6.36	1.70	231.39	31.88	28.03	68	1.67	4.43	3.780	10.042
L1	5.66	56.5	6.35	1.70	233.52	31.25	28.04	68	1.26	5.78	3.771	10.016
L22	5.64	15.5	6.34	1.73	223.73	31.66	28.07	68	2.46	1.01	3.856	9.865
L9	5.57	45.9	6.25	1.72	220.19	30.46	28.04	67	1.11	6.04	3.766	9.796
L23	5.55	357.1	6.22	1.69	219.05	30.48	28.04	66	2.65	0.59	3.675	9.860
L2	5.55	314.8	6.23	1.72	217.65	30.09	28.04	67	1.26	5.21	3.753	9.750
L32	5.53	341	6.20	1.68	220.63	30.07	28.06	66	1.80	3.68	3.604	9.913
L16	5.53	28.1	6.21	1.72	215.13	30.26	28.10	67	1.26	5.78	3.738	9.736
L40	5.52	327.4	6.18	1.65	223.05	29.71	28.05	65	2.14	3.10	3.517	9.993
L10	5.52	19.6	6.19	1.71	214.82	30.00	28.06	67	1.14	5.64	3.706	9.734
L46	5.48	315.1	6.13	1.63	223.17	29.10	28.07	63	2.42	2.69	3.426	10.018
L34	5.43	344.2	6.10	1.72	203.98	29.20	28.02	66	1.00	5.96	3.668	9.565
L17	5.43	344.2	6.10	1.72	203.98	29.20	28.02	66	1.26	5.21	3.668	9.565
L24	5.41	334.1	6.06	1.69	204.19	28.77	28.09	65	1.28	5.54	3.572	9.621
L3	5.39	267.9	6.06	1.75	195.14	28.38	28.08	67	1.22	4.53	3.744	9.348
L33	5.34	323.9	5.99	1.68	199.54	27.84	28.12	64	1.85	3.00	3.497	9.554
L41	5.30	316	5.95	1.67	197.45	27.14	28.13	64	2.15	2.49	3.445	9.525
L25	5.25	314.5	5.90	1.72	184.35	26.90	28.11	65	1.43	4.67	3.553	9.243
L11	5.24	270.2	5.89	1.77	176.79	26.78	28.08	65	1.08	4.38	3.672	9.039
L47	5.21	308.7	5.84	1.66	189.21	25.76	28.11	63	2.35	2.08	3.351	9.392
L4	5.16	255.4	5.81	1.79	167.42	25.78	28.10	65	1.16	3.73	3.670	8.840
L26	5.08	285	5.71	1.77	162.25	23.70	28.09	65	1.51	3.89	3.568	8.754
L42	5.05	308	5.68	1.71	166.47	23.99	28.10	63	2.06	1.94	3.405	8.912
L12	5.04	259.5	5.67	1.79	155.39	24.37	28.11	64	1.43	2.66	3.586	8.630
L35	5.00	287.6	5.62	1.75	158.56	22.36	28.16	64	2.49	1.43	3.459	8.698
L27	4.98	274	5.60	1.74	159.05	21.47	28.06	64	1.62	3.06	3.428	8.698
L19	4.96	264.9	5.58	1.77	149.87	23.02	28.17	63	1.00	5.96	3.491	8.537
L18	4.96	264.9	5.58	1.77	149.87	23.02	28.17	63	0.98	6.67	3.491	8.537
L48	4.93	301.4	5.54	1.71	155.80	22.33	28.10	61	2.18	1.59	3.310	8.706
L5	4.88	248.5	5.49	1.81	139.67	22.16	28.11	63	1.30	2.38	3.516	8.289
L43	4.74	286.6	5.33	1.74	136.04	18.89	28.71	61	2.52	1.02	3.253	8.293
L13	4.74	253.6	5.34	1.78	131.64	20.36	28.19	61	1.84	1.45	3.350	8.156
L20	4.68	258.2	5.27	1.74	131.82	18.85	28.17	60	1.96	1.76	3.232	8.162
L49	4.62	281.1	5.19	1.73	124.98	16.93	28.65	60	2.39	0.89	3.161	8.078
L36	4.61	272.5	5.16	1.67	132.95	16.99	28.87	59	2.90	0.78	2.981	8.286
L28	4.55	258.3	5.10	1.65	130.69	16.10	28.76	58	2.29	1.68	2.895	8.249
L6	4.48	247.6	5.05	1.82	108.55	16.74	28.13	59	1.57	1.16	3.255	7.588
L14	4.40	253.4	4.95	1.77	107.75	15.27	28.20	57	2.24	0.74	3.100	7.588
L21	4.37	255.6	4.92	1.72	110.95	14.20	28.06	57	1.96	1.76	2.960	7.700
L37	4.17	252.4	4.66	1.59	106.69	12.08	29.90	53	3.18	0.41	2.492	7.784
L7	4.16	253.9	4.69	1.85	85.95	11.78	28.12	55	1.72	0.59	3.081	6.971
L15	4.02	255.5	4.53	1.79	81.09	9.93	28.27	53	2.14	0.38	2.865	6.886
L29	4.01	241.7	4.49	1.60	93.84	10.69	29.97	51	2.68	1.27	2.435	7.437
L8	3.83	260.2	4.31	1.83	66.60	8.50	28.25	49	1.41	0.40	2.796	6.466

for all the sites. The plant capacity factor is calculated as follows:

$$PCF={\displaystyle \frac{AEY \left(\mathrm{kWh}/\mathrm{yr}\right)}{Name Plate Capacity \left(\mathrm{kW}\right)\ast 8760 \left(\mathrm{h}\right)}}$$

(5)

4. Results and Discussion

The WS and WP characteristics, PCF, GHG emissions, and COE at all the sites, are discussed in Section 4.1 and Section 4.2. The long-term, site-specific, mean WS and WPD values vary between 3.83 m/s and 6.39 m/s, and 66.60 W/m² and 280.59 W/m², corresponding to sites L8 and L44 (Table 2), respectively. The annual mean values of WS are ≥5.0 m/s at 33 out of 49 sites (L1–L4, L9–L12, L16–L19, L22–L27, L30–L35, L38–L42, and L44–L48), as observed from the long-term site-specific statistical results given in Table 2. The vectoral variation of long-term mean WS and the prevailing WD is shown in Figure 3. In general, the wind blows outwards from inland to the sea and then changes direction southwards and northwards. Deep out to sea, the wind blows southwards and also inwards from the sea at the Saudi water boundary. It is evident that higher wind is observed deep out to sea and its magnitude increases from south to north. The Weibull scale parameter c is always >5.0 m/s except for a few sites (L7, L8, L14, L15, L21, and L37) and the shape parameter k varies between a minimum of 1.59 and a maximum of 1.85.

Higher values of WPD, > 200 W/m², are observed at 20 sites (L1, L2, L9, L10, L16, L17, L22–L24, L30–L34, L38–L40, and L44–L46). This is an indication of higher wind power potential at these sites. Moreover, the frequency (F) of occurrences of WPD greater than or equal to 200 W/m² is found to be ≥ 25% at 27 offshore sites (L1–L4, L9–L12, L16, L17, L22–L25, L30–L34, L38–L41, and L44–L47). The maximum value of 40.29% of F is seen at L44. This simply hints at better sites from a wind energy harnessing point of view. Other factors like MWSI are calculated using F values, as discussed in the methodology section, and are also helpful in providing additional justification or insight in prioritizing the wind potential at the sites under consideration. The magnitudes of the MWVI are ≥2.0 at most of the sites near the coast (L15, L21, L29, L37, L43, and L49). In general, MWVI values are observed to be higher near the coast and decrease as one moves westward out to sea. Lower values of MWVI indicate relatively less wind turbulence and are preferred.

Another important aspect of windy site selection is the wind duration factor. It indicates how long the wind speed is available above the cut-in speed of the wind turbine which is 3.5 m/s in the present case for the selected offshore machine of 6.2 MW rated capacity from REPower. The hub height of the chosen wind turbine is 120 m. The wind duration varies between 49% at L8 and 77% at L44 with an overall average availability of 63.2%. The most probable WS, (V_mp) is found to be less than 4.0 m/s except at sites L38 and L44 where it was more than 4.0 m/s. However, the maximum energy-carrying WS (V_max,E) remained above 8.0 m/s at most of the sites, as indicated in the last column of Table 2. However, it varied between a minimum of 6.466 m/s at L8 and a maximum of 10.288 m/s at L44.

4.1. Variability of Wind Speed and Wind Power Density on Annual, Monthly, and Hourly Scales

Long-term site-specific WS characteristics over the entire data collection period of 43 years are discussed below, in

Table 3. Long-term linear WS trends at all the offshore sites under consideration.

Site	Y = mx + c	R2	Site	Y = mx + c	R2	Site	Y = mx + c	R2
L1	0.0027x + 5.5985	0.0230	L18	0.0015x + 4.9236	0.0104	L35	0.0003x + 4.9887	0.0007
L2	0.0022x + 5.4988	0.0165	L19	0.0015x + 4.9236	0.0104	L36	0.0007x + 4.5867	0.0031
L3	0.0019x + 5.3490	0.0144	L20	0.0015x + 4.6487	0.0112	L37	0.0013x + 4.1352	0.0164
L4	0.0020x + 5.1176	0.0171	L21	0.0013x + 4.3425	0.0128	L38	0.0028x + 5.8456	0.0206
L5	0.0022x + 4.8279	0.0221	L22	0.0024x + 5.5904	0.0169	L39	0.0029x + 5.6193	0.0235
L6	0.0020x + 4.4351	0.0247	L23	0.0021x + 5.5013	0.0148	L40	0.0023x + 5.4648	0.0171
L7	0.0021x + 4.1164	0.0430	L24	0.0015x + 5.3735	0.0090	L41	0.0017x + 5.2620	0.0118
L8	0.0026x + 3.7716	0.1717	L25	0.0010x + 5.2312	0.0046	L42	0.0012x + 5.0210	0.0076
L9	0.0027x + 5.5112	0.0245	L26	0.0003x + 5.0667	0.0005	L43	0.0012x + 4.7129	0.0105
L10	0.0023x + 5.4669	0.0184	L27	0.0003x + 4.9743	0.0005	L44	0.0030x + 6.3195	0.0221
L11	0.0018x + 5.1989	0.0136	L28	0.0010x + 4.5220	0.0079	L45	0.0031x + 5.6483	0.0277
L12	0.0019x + 4.9988	0.0161	L29	0.0019x + 3.9651	0.0483	L46	0.0023x + 5.4244	0.0175
L13	0.0019x + 4.7004	0.0173	L30	0.0026x + 5.7510	0.0182	L47	0.0019x + 5.1592	0.0143
L14	0.0017x + 4.3630	0.0184	L31	0.0026x + 5.6148	0.0193	L48	0.0015x + 4.8876	0.0131
L15	0.0018x + 3.9810	0.0387	L32	0.0022x + 5.4833	0.0161	L49	0.0020x + 4.5705	0.0366
L16	0.0022x + 5.4865	0.0164	L33	0.0016x + 5.3062	0.0100
L17	0.0017x + 5.3967	0.0117	L34	0.0017x + 5.3967	0.0117

. The data show annual increasing rates of the WS at all the locations under investigation and vary from 0.0003 m/s at L26–L27 and L35 to 0.0031 m/s at L45. However, at most locations, the annual WS increasing rate is observed to be > 0.002 m/s. The linear trends at some locations, for example L16–L30, are also shown in Figure 4. Similar ranges of linear trends are observed at other sites. The annual WPD trends, follow almost the same trends as those of the WS.

Among sites L1–L15, the monthly mean WS values of >5.0 m/s were observed at L1–L5 and L9–L13 in January to April, June, and November to December with all-time high values in July at all sites over the entire data analysis duration. In general, WPD values were found to be higher > 200 W/m² during January to April, July, and November to December at L1–L4 and L9–L11 while the lowest occurred in May and September at L1–L15; see Figure 5a. The long-term monthly mean WS remained >5.0 m/s at L16–L19, L22–L26, and L30 from January to April and June to July, with the lowest values in May, September, and October. As shown in Figure 5b, the WPD was seen to be more than 200 W/m² at L16, L17, L22–L25, and L30 during January to April, June, and December with the highest in July and lowest in May, September, and October. From January to April, WSs of >5.0 were observed at L31–34, L38–L42, and L44–L47, while speeds between 5.0 and 8.0 m/s during June to August and intermittently ≤5.0 m/s during September to December were observed at L31–L49 locations. WPD values of >200 Wm/² were found at L31–L33, L38–L41, and L44–L46 from January to March, while WPD between 200 and 600 W/m² from June to August was seen at L31–L49 with few exceptions; Figure 5c. WPD values of <200 W/m² were observed in May and from November to December at almost all the sites (L31–L49). Generally speaking, the WS and WPD magnitudes were found to increase as one moves from the coast to the west out to sea, and from south to north.

The diurnal variability of WS and WPD facilitates the availability and the magnitude of wind power during the day on an hourly basis, as in the present case. This information helps the grid operators and the utilities in the proper management of wind power utilization. At all the sites, increasing trends of WS were noticed from 00:00 h to 12:00 h, then decreasing towards the end of the day, as shown in Figure 6 for some sites. The diurnal ranges (maximum–minimum) of WS of > 2.0 m/s were found at L6–L8, L14–L15, L20–L21, L26–L30, L35–L37, L42–L43, and L47–L49 while less at the rest of the locations. This is a good indicator that out of 49 sites, 30 come across diurnal changes of <2.0 m/s. Lower diurnal ranges of WS are indicators of continuous and more stable power production from wind turbines. Additionally, higher magnitudes of WSs during the daytime generate more power which fulfills the high-power demand. The diurnal changes in WPD values followed almost the same trend at all the sites as with WS; see Figure 7. The WPD values increased from midnight until a little after noon and then a decreasing trend was observed towards the end of the day. The highest WPD values were observed around noon time at all the sites.

4.2. Wind Power Generation and Plant Capacity Factor Analysis

After going through the detailed long-term evaluation of wind speed characteristics at 120 m and preliminary identification of the preferred windy sites in terms of WS, WPD, MWVI, MWSI, and wind duration values, further analysis was carried out concerning wind power output, AEY, and PCF to confirm the suitability of the potential wind sites. For power generation and capacity yield estimation, a 6.2 MW rated capacity offshore wind turbine, from REPower, was chosen. The cut-in, the rated, and the cut-out speeds of this WT are 3.5, 10.0–11.5, and 30.0 m/s while the rotor diameter is 152 m. The wind power curve is shown in Figure 8. For net power, AEY, and PCF calculations, a total of 12.35% losses, comprised of availability, wake, and turbine performance losses of 3% each and electrical and environmental losses of 2.0% each, were considered.

The mean wind power and AEY varied from 380 to 1200 kW and from 5.0 to 12.0 GWh as the location changed from the coast (L8) to westwards into the water (L1), as can be observed in Figure 9. Similar trends, with varying magnitudes, were seen at other coastal to Saudi water limits. Maximum magnitudes of mean power of 725 kW to 1600 kW and AEY of 6.35 GWh to 14.14 GWh were observed corresponding to sites L49 and L44. The PCFs at coastal sites L8, L15, L21, L29, L37, L43, and L49 had the lowest values of 6.19, 7.14, 10.00, 8.12, 9.17, 12.77, and 11.78%, while near Saudi water limiting areas L1, L9, L16, L22, L30, L38, and L44, the highest values of 21.03, 20.25, 20.00, 20.83, 22.00, 22.98, and 26.24%, respectively, were observed; see Figure 10. The wind power, AEYs, and PCFs showed decreasing trends from Saudi waters’ limiting boundary towards the Red Sea coastal sites. However, slightly increasing trends of these parameters were also observed from southernmost to northern sites.

The COE is calculated based on the weighted average offshore installation cost of 4720.25 USD/kW reported for 2020 to 2021, (Renewable Energy Agency, 2022). However, it varied from 2858 USD/kW to 5584 USD/kW depending on the geographical region. As expected, the COE has the opposite trend to that of PCF; Figure 10. For example, among L1–L8 sites, PCF was the highest and COE was the lowest at L1 and PCF lowest and COE highest at L8. Similar variations can be observed at sites L9–L15, L16–L21, L22–L29, L30–L37, L38–L43, and L44–L49; of course, with different magnitudes. In general, COE increases from the western water boundary to the eastern coastal area. It can be summarized that COE varies from a minimum of 2.07 USD/kWh to 8.78 USD/kWh corresponding to sites L44 and L8 with an overall mean of 3.72 USD/kWh.

The chosen WT tends to produce the rated power for a maximum duration of 8.78% at L44 and a minimum of 0.16% at L7 with an overall average of 3.05%; Figure 11. Only at 23 sites could the WT produce the rated power for >3.0% of the time annually and remained idle for 30.79% of the time. Higher-rated power production is observed at sites farthest from the coast; Figure 11. The GHG emissions that could be avoided from being added into the local atmosphere varied between 651.19 and 2761.22 tons, corresponding to L8 and L44 sites with a mean value of 1710.70 tons annually; Figure 12. Similarly, as a result of installation of only one WT each at L8 and L44, approximately 405 and 1716 households could be served power. On average, 1063 houses can be provided power produced from the chosen WT at each site based on per capita electricity consumption of 8239.13 kWh in Saudi Arabia [45].

Among the sites L1–L44, the suitable option from wind resources and COE points of view, L44 was found to be the best and most economical. However, the COE of 2.07 USD/kWh at L44 is 17 times higher than the COE of 0.1234 USD/kWh reported for New York State [46]. For the Balearic Islands and the North Adriatic, Tyrrhenian, and Levantine Seas, a COE of 0.268 USD/kWh (1/7th of the COE of L44) was reported, while for Sardinia, Sicily and Malta, the Alboran Sea, and in the South Adriatic, a COE of 0.193 USD/kWh (of around 1/11th of the COE of L44) was reported by [47]. Compared to all the reported values, the COE in the present case is many times higher, which may be attributed to the assumed average total installation cost of 4720.25 USD/kW, the local wind resources, and the type of wind turbine.

5. Conclusions

The scope of the present study included a wind power resources assessment for 49 offshore sites in the Saudi waters of the Red Sea between 16.50° and 18.25° latitudes. The long-term, covering a period of 43 years from 1979 to 2021, hourly mean wind speed and direction data at 100 m AGL and temperature and pressure near the water surface were used for the assessment. Specifically, long-term annual, monthly, and diurnal trends of the wind speed, wind power density, wind frequency, wind duration, wind power, plant capacity factors, mean wind variability index, mean windy site identifier, cost of energy, most probable wind speed, and maximum energy carrying wind speed were analyzed for all the sites for identifying the best possible offshore windy sites in the region for clean power production. The following are the conclusive highlights of the study:

• Overall, the long-term annual mean wind speeds varied between 3.83 and 6.39 m/s at L8 and L44 sites while the respective wind WPD values were estimated to be 66.6 and 280.0 W/m². The prevailing wind directions were found to be from the north and northwest, meaning less turbulence, veering, and backing effects; assuring longer life of the wind turbines.
• Higher values of mean power of 725 kW to 1600 kW and AEY of 6.35 GWh to 14.14 GWh were observed at L49 and L44. The PCFs at coastal sites L8, L15, L21, L29, L37, L43, and L49 had the lowest values of 6.19, 7.14, 10.00, 8.12, 9.17, 12.77, and 11.78%, while near Saudi water limiting areas L1, L9, L16, L22, L30, L38, and L44 the highest values of 21.03, 20.25, 20.00, 20.83, 22.00, 22.98, and 26.24% were observed, respectively.
• The Weibull shape and scale parameters ranged from 1.59 (L37) to 1.97 (L44) and 4.31 m/s (L8) and 7.2. m/s (L44).
• Lower values of mean wind variability indices (MWVI) are preferred due to being representative of the least turbulent nature of the winds, which assures a longer working life of the WTs. In the present case, MWVI varied between 0.98 and 3.18 at L18 and L37 sites. At ten potential windy sites (L44, L38, L30, L45, L39, L31, L1, L22, L9, and L23), MWVI values were around 1.0 and 3.00 which simply means that winds are relatively turbulent but still good for the longer working life of wind turbines. Next, higher values of MWSI are opted for while selecting a potential windy site. In the present case, MWSI values of 0.38 to 6.67 were obtained corresponding to L15 and L18 sites. At the first 10 preferred sites, MWSI values remained >3.0 except sites L30, L22, and L23 where these are <1.0.
• For the chosen 6.2 MW rated power offshore wind turbine with a cut-in speed of 3.5 m/s, the wind duration was found to be between 49% at L8 and 77% at L44 with an overall average of 63.2%. This high availability of wind in the region under investigation, is a good indicator for promoting the offshore wind farms deployment.
• Decreasing trends of wind power, AEYs, and PCFs were observed from Saudi waters’ limiting boundary towards the Red Sea coastal sites. However, slightly increasing values of the above measures were seen while moving from the southernmost part to the northern sites.
• It was noticed that COE increases from the western water boundary to the eastern coastal area and slightly decreases from south to northwards. In this region, the wind power can be produced at a COE of 2.07 USD/kWh to 8.78 USD/kWh corresponding to sites L8 and L44 with an overall mean of 3.72 USD/kWh.
• The chosen WT could produce the rated power >3.0% of the time annually at 23 sites, while on average, did not produce any power for 30.79% of the time.

For future research, it is highly recommended to deeply conduct techno-economic-environmental analyses and optimal sizing of grid-connected architecture with a cluster of offshore wind farms on the optimal identified location with various hybrid renewable resources, and complicating control strategies. Furthermore, evaluating the infrastructure availability for connecting an offshore wind farm to the national power grid involves assessing the proximity and capacity of existing substations, transmission lines, and grid stability, as well as considering potential upgrades or expansions required to integrate additional power effectively; all of which are of great interest for future works.