The increasing use of fossil fuels has a significant impact on the environment and ecosystem, which increases the rate of pollution. Given the high potential of renewable energy sources in Yemen and the absence of similar studies in the region, this study aims to examine the potential of wind energy in Socotra Island. This was done by analyzing and evaluating wind properties, determining available energy density, calculating wind energy extracted at different altitudes, and then computing the capacity factor for a number of wind turbines and determining the best. The average wind speed in Socotra Island was obtained from the Civil Aviation and Meteorology Authority data, only for the five-year data currently available. The results showed high wind speeds from June to September (9.85–14.88 m/s) while the wind speed decreased for the rest of the year. The average wind speed in the five years was 7.95 m/s. The average annual wind speed, wind energy density, and annual energy density were calculated at different altitudes (10, 30, and 50 m). According to the International Wind Energy Rating criteria, the region of Socotra Island falls under Category 7 and is classified as ‘Superb’ for most of the year. This study provides useful information for developing wind energy and an efficient wind approach.
The increase in population and rate of industrialization has led to a rise in energy demand. Fossil fuels cannot meet this demand because they negatively affect the environment and ecosystem, causing a significant increase in pollution. In other words, the energy industry and the environment are in significant crises today. Today in modern societies, energy is the most important indicator of economic growth and many countries worldwide are taking steps toward achieving a renewable energy model to solve this crisis [
As seen in literature [
In 2009, the Yemeni government approved the National Renewable Energy and Efficiency Strategy, which aims to increase 15% of energy efficiency (EE) in the energy sector by 2025, and target renewable energy (RE) capacity (Geothermal energy 160 megawatts, concentrated solar power 100 megawatts, solid biomass 6 megawatts, solar photovoltaic system 8.25, and wind power 400 megawatts) of total electricity by 2025. The Yemeni energy sector consists of oil, natural gas, and biofuel production. Energy production in 2012 was “15.109 kilotons of oil equivalent (ktoe), while consumption was 6,923 kilotons” [
Solar irradiance ranges between 5.2–6.8 kWh/m2/day, and the average annual sunshine is between 7.3 and 9.1 h/day, even in winter. The average daily solar hours are between 8 and 16 h per day [
This study investigates the potential of wind energy in Socotra Island by analyzing and evaluating the wind characteristics, determining the available energy density, and calculating the wind energy extracted at different altitudes (10, 30, 50 m). This study also discussed one of the methods of selecting the suitable wind turbine for the studied site, which is the calculation of the capacity factor. The current study provides useful information for government departments concerned with developing wind energy in Yemen.
The rest of the paper has five more sections. Section 2 provides the background. Section 3 provides the system model. Section 4 presents the basics calculations of the proposed system model. Section 5 describes the simulation, results and discussion, and Section 6 offers conclusions.
This section provides a brief concise background on renewable energy, the reality, and the statement of the energy and power system problem in the Republic of Yemen.
Yemen has a very good potential for using renewable energy. Still, the problem is the state of the energy sector in Yemen because it relies heavily on conventional energy (fossil fuels, petroleum, and its derivatives). We will note that one of the practical solutions and alternative sources of electricity and the economy in the country is the use of renewable energy [
Weaknesses and Strengths of Renewable energy in Yemen in
Source | Strengths | Weaknesses | |
---|---|---|---|
Solar electric | Renewable resource. | Depending on sunshine levels. | |
A clean source of energy. | High capital costs. | ||
Long lifetime. | Requires storage system. | ||
Wind | Renewable resource. | Renewable resource. | |
A clean source of energy. | Not reliable. | ||
Sufficient level of maturity. | Causes visual impact, noise, and electromagnetic interference. | ||
Competitive in cost. | Ecological impact geothermal. | ||
Geothermal | Stable. | Requires complex management system. | |
A clean source of energy. | Not sustainable. | ||
Biomass/Biofuels | Available and free resource. | Competing land use. | |
Availability of conversion technologies. | Requires complex management system. |
Source | Theoretical potential (MW) | Technical potential | |
---|---|---|---|
Practicable (MW) | Gross (MW) | ||
Solar electric | 2,446,000 | 1,426,000 | 18,600 |
Wind | 308,722 | 123,429 | 34,286 |
Geothermal | 304,000 | 29,000 | 2,900 |
Biomass (landfill gas) | 10 | 8 | 6 |
Existing dams | 1 | – | – |
Major wadis | 12-31 | 11-30 | – |
Domestic (SWH) | 3,014 MW thermal | 278 MW thermal | 278 MW thermal |
In 2009, the Government of Yemen approved the national strategy for RE and energy efficiency, aiming to increase 15% of energy efficiency (EE) in the power sector by 2025 [
(Geothermal 160 MW, Concentrated Solar Power (CSP) 100 MW, Solid Biomass 6 MW, Solar PV 8.25, Wind 400 MW).
The Yemeni energy sector consists of oil, natural gas, and biofuels. Energy production in 2012 was “15,109 kiloton of oil equivalent (ktoe) while the consumption was 6,923 ktoe” [
The main components of the proposed system model are shown in
Statistical analysis of wind velocity data and average wind parameters of the two commonly used functions are also provided to fit the probability distribution of wind velocity measured at a given location over a given period in this section. The functions are the Weibull and Rayleigh distributions. In this section, the capacity factors of several famous wind turbines are also calculated based on Weibull parameters and the speed characteristics of each of these turbines. The second section analyzes wind energy evaluation and finds the wind speed extrapolation, wind power density, and energy density for three heights (10, 30, and 50 m).
Socotra Island, situated in the northwestern Indian Ocean, is located near the equator (which makes its climate generally tropical) between latitudes, 53.19 and 54.33 east of the Greenwich International Line and between spaces 128 and 42.12 north of the equator. Socotra Island has a total land area of 3625 km2, a coastline of 300 km, and a population of nearly half a million people. The island has a hot marine climate with the maximum temperature ranging from 26–28°C and the lowest temperature between 19°C and 23°C. The annual mean temperature is between 27 and 29°C. It was named “the world’s strangest region” and was classified by the New York times as the world’s most beautiful island in 2010 [
The main aspects of literature regarding wind are on wind speed density and functional variations, and they have a wide range of known applications. Some of the functions commonly used to distribute the probability of measured wind velocity at a given location over a given time are the Weibull and Rayleigh distributions. The probability density function for the Weibull distribution is given by
where f(v) is the probability of observing wind speed; v and k are the dimensionless Weibull shape parameter (k helps in finding how frequently wind speeds are close to some measured speed); c is the Weibull scale parameter with a unit equal to the wind speed unit (k and c characterize the wind potential of the sites under study).
The corresponding cumulative probability function of the Weibull distribution is given by
The Rayleigh distribution is a special case of the Weibull distribution in which the shape parameter k takes the value 2.0. From
The two parameters of the Weibull distribution are probability functions, k and c, which can be related to the mean wind speed Vm and standard deviation σ as shown in
The Rayleigh distribution shape parameter k takes the value 2.0. From
The mean value Vm and standard deviation σ of the Weibull distribution can then be computed as shown in
where Γ is the gamma function (standard formula) and using the stirling approximation the gamma function of (x) can be given by
The square of the correlation coefficient (R2), chi-square (x2), and root mean square error analysis (RMSE) were used to evaluate the performance of the Weibull and Rayleigh distributions [
where yi is the first measured data, zi is the mean value, xi is the first predicted data with the Weibull or Rayleigh distribution, N is the number of observations, and n is the number of constants.
The most common equation used for the variation of wind speed with height is the power law expressed as shown in
where v1 is the actual wind speed recorded at height h1 (m), (m/s) and v2 is the wind speed at the required or extrapolated height h2 (m), (m/s).
The exponent
It is well known that the power of the wind at speed v (m/s) through a blade sweep area A (m2) increases as the cube of its velocity and is given by
where ρ (kg/m3) is the mean air density with value of 1.220 kg/m3. This depends on the altitude, air pressure, and temperature. The expected monthly or annual wind power density per unit area of a site based on the Weibull probability density function can be expressed as shown in
The total wind power density P/A is the total available power per unit area given by
where n is the number of days in a month.
Before calculating the average wind power density, vi3 of each day for the extrapolated height at 50 m was calculated
The electrical energy produced by a turbine over the year is given by the following relationship as shown in
where C
The available mean wind power density Pd, and the overall wind energy density Ed, of a wind turbine for a period of time T will be calculated as shown in
Most wind turbines have power curves in their technical notes. This makes it easier to estimate the energy production of any wind turbine when a series of measurements are made at the studied site.
However, sometimes only a probability distribution function may be available. In this case, the wind turbine power output can be expressed as shown in
where f(v) is the Weibull distribution given by
where the curve increases semi-linearly, starting from the cut-in speed
The curve can be divided into two areas, the first is confined between
Substituting
Capacity factor that was used to choose a suitable wind turbine, is defined as the ratio of average power output
From
The capacity factor is proportional to C and inversely to k and when fixing the values of C and k, we notice that CF is affected inversely by the difference between the
The average wind speed of Socotra was obtained from the recorded data of the Civil Aviation and Meteorological Authority (CAMA), only for the data available within five years from 2005–2009 (due to the current war and the political situation in Yemen). The wind rose is a primary source for assessing wind energy due to its brief view of how wind velocity is distributed and how it remains distributed in the desired location according to the area’s topographical influences. The island is exposed to strong southwesterly winds peaking in early June until late August and then gradually declines until it reaches average speed in the beginning of October. When the speed decreases to 10 knots. The southwest winds in June, July, and August have an actual speed of about 40 to 50 knots, and in some parts of the island may reach more than 55 knots, accompanied by severe disturbance of the sea.
Year | 2005 | 2006 | 2007 | 2008 | 2009 | Whole year | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Month | Vm | σ | Vm | σ | Vm | σ | Vm | σ | Vm | σ | Vm | σ |
Jan | 4.90 | 1.77 | 8.29 | 2.87 | 8.41 | 2.94 | 6.41 | 2.67 | 7.95 | 1.49 | 7.20 | 2.78 |
Feb | 3.34 | 1.89 | 5.38 | 2.23 | 4.10 | 1.23 | 8.04 | 2.62 | 6.21 | 1.63 | 5.41 | 2.57 |
Mar | 2.87 | 1.11 | 4.32 | 1.36 | 3.64 | 1.42 | 3.85 | 1.38 | 5.23 | 1.60 | 3.98 | 1.59 |
Apr | 3.14 | 1.40 | 2.91 | 0.77 | 3.01 | 0.78 | 3.09 | 0.72 | 4.33 | 0.78 | 1.06 | |
May | 3.09 | 1.39 | 5.55 | 3.44 | 5.61 | 3.36 | 7.31 | 2.18 | 5.42 | 2.59 | 5.40 | 3.02 |
Jun | 11.53 | 3.10 | 12.82 | 3.16 | 12.81 | 4.10 | 12.12 | 1.91 | 11.28 | 1.69 | 12.11 | 3.00 |
Jul | 15.95 | 3.32 | 15.75 | 2.24 | 11.76 | 1.36 | 14.71 | 2.00 | 16.22 | 1.48 | 2.74 | |
Aug | 13.28 | 3.72 | 14.71 | 2.52 | 12.70 | 1.53 | 14.08 | 2.34 | 15.21 | 1.41 | 14.00 | 2.61 |
Sep | 8.04 | 3.23 | 10.45 | 1.51 | 10.46 | 2.12 | 9.65 | 2.45 | 10.65 | 3.19 | 9.85 | 2.76 |
Oct | 3.99 | 1.68 | 5.24 | 1.75 | 4.25 | 1.07 | 4.28 | 2.55 | 8.18 | 1.77 | 5.19 | 2.40 |
Nov | 4.24 | 1.90 | 6.32 | 1.31 | 5.54 | 1.41 | 6.21 | 1.50 | 7.15 | 1.91 | 5.89 | 1.89 |
Dec | 7.54 | 1.38 | 7.67 | 2.65 | 7.31 | 2.29 | 8.42 | 2.13 | 10.10 | 1.50 | 8.21 | 2.29 |
Yearly | 6.83 | 4.89 | 8.28 | 4.66 | 7.47 | 4.13 | 8.18 | 4.25 | 9.00 | 4.10 | 4.48 |
As shown above, in five years the average wind speed was 7.95 m/s. For the entire period, the maximum monthly wind speed was 14.88 m/s in July, while the minimum value was 3.3 m/s in April. It was observed that the smaller the standard deviation, the less regular the speed samples became. This indicates that the current region in our study is very suitable for wind energy.
The variation of wind speed is often described using the Weibull density function. It is a widely accepted statistical tool for evaluating local wind probabilities and is considered a standard approach.
Year | 2005 | 2006 | 2007 | 2008 | 2009 | Whole year | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Month | C | K | C | K | C | K | C | K | C | K | C | K |
Jan | 5.49 | 3.02 | 9.27 | 3.16 | 9.40 | 3.13 | 7.22 | 2.59 | 8.56 | 6.15 | 8.08 | 2.81 |
Feb | 3.76 | 1.86 | 6.05 | 2.60 | 4.54 | 3.69 | 8.95 | 3.38 | 6.83 | 4.29 | 6.11 | 2.24 |
Mar | 3.23 | 2.80 | 4.80 | 3.51 | 4.08 | 2.78 | 4.30 | 3.05 | 5.80 | 3.62 | 4.48 | 2.72 |
Apr | 3.54 | 2.40 | 3.20 | 4.26 | 3.30 | 4.35 | 3.37 | 4.85 | 4.65 | 6.46 | 3.67 | 3.42 |
May | 3.49 | 2.38 | 6.22 | 1.68 | 6.30 | 1.74 | 8.10 | 3.73 | 6.12 | 2.23 | 6.08 | 1.88 |
Jun | 12.69 | 4.17 | 14.03 | 4.58 | 14.25 | 3.44 | 12.91 | 7.42 | 11.98 | 7.87 | 13.26 | 4.56 |
Jul | 17.28 | 5.49 | 16.69 | 8.32 | 12.34 | 10.43 | 15.56 | 8.71 | 16.86 | 13.46 | 16.00 | 6.28 |
Aug | 14.65 | 3.99 | 15.75 | 6.81 | 13.35 | 9.97 | 15.05 | 7.01 | 15.82 | 13.19 | 15.06 | 6.19 |
Sep | 9.04 | 2.69 | 11.09 | 8.20 | 11.31 | 5.66 | 10.58 | 4.43 | 11.80 | 3.70 | 10.87 | 3.98 |
Oct | 4.50 | 2.57 | 5.84 | 3.29 | 4.66 | 4.46 | 4.81 | 1.76 | 8.89 | 5.26 | 5.86 | 2.31 |
Nov | 4.79 | 2.39 | 6.85 | 5.54 | 6.08 | 4.43 | 6.79 | 4.68 | 7.87 | 4.20 | 6.56 | 3.43 |
Dec | 8.10 | 6.30 | 8.56 | 3.17 | 8.12 | 3.53 | 9.24 | 4.44 | 10.73 | 7.93 | 9.05 | 4.01 |
Yearly | 7.53 | 1.44 | 9.33 | 1.87 | 8.42 | 1.90 | 9.23 | 2.04 | 10.16 | 2.35 |
It is known that there are many distribution functions used to describe the wind speed frequency curve, but for this study, the Weibull function, which is the most widely used and accepted in specialized research journal, was used. The yearly wind speed probability density and cumulative probability distributions derived from Socotra Island’s measured data for the study period are shown in
Most distribution functions can be determined according to the highest value of R2 and the lowest values of RMSE and x2. It was noted from previous analysis that the Weibull distribution fits the domain data better than the five-year Rayleigh distribution. The Weibull distribution gives a good approximation for estimating wind energy density in Yemen. In addition, the monthly distribution of wind velocity probability density derived from the data measured from Socotra Island for five years is shown in
The probability density and Weibull probability density distributions for each of the five years were analyzed. The distributions obtained are illustrated in
The results shown in
Since the wind speed changes with altitude and actual wind turbines are placed at different altitudes more than 10 m from the earth surface, the average monthly and annual wind speeds were calculated at different heights (10, 30 and 50 m) to simulate the appropriate height for wind turbines using
In this section, ten types of wind turbines will be compared, whose power ranges between 200 and 250 kw, as shown in
The selected turbines are typical from the point of view of their current characteristics and performance at various locations around the world.
The ten turbines are evaluated by calculating the capacity factor CF for each of them according to
To calculate the average monthly wind power per unit of the turbine cross-section with an air density of 1.225 kg/m3, the energy density was calculated at different heights (10, 30, and 50 m), as shown in
Month | Heights | ||
---|---|---|---|
10 m | 30 m | 50 m | |
Jan | 228.6 | 860.5 | 1575 |
Feb | 97 | 363 | 669.3 |
Mar | 38.6 | 146 | 268.9 |
Apr | 22 | 81.2 | 153.2 |
May | 96.4 | 363 | 153.2 |
Jun | 1087.8 | 4069.9 | 7549.9 |
Jul | 2018 | 7549.9 | 13882.4 |
Aug | 1680.7 | 6258.7 | 11658.4 |
Sep | 585.3 | 2193.7 | 4069.9 |
Oct | 85.6 | 325.5 | 594.3 |
Nov | 125.2 | 461.6 | 860.5 |
Dec | 338.9 | 1254.6 | 2325.3 |
Resource potential | 30 m height | 50 m height | |||
---|---|---|---|---|---|
Wind speed (m/s) | Wind power (W/m²) | Wind speed (m/s) | Wind power (W/m²) | ||
1 | Poor | 0–5.1 | 0–160 | 0–5.6 | 0–200 |
2 | Marginal | 5.1–5.9 | 160–240 | 5.6–6.4 | 200–300 |
3 | Moderate | 5.9–6.5 | 240–320 | 6.4–7.0 | 300–400 |
4 | Good | 6.5–7.0 | 320–400 | 7.0–7.5 | 400–500 |
5 | Excellent | 7.0–7.4 | 400–480 | 7.5–8.0 | 500–600 |
6 | Outstanding | 7.4–8.2 | 480–640 | 8.0–8.8 | 600–800 |
7 | Superb | 8.2–11.0 | 640–1600 | 8.8–11.9 | 800–2000 |
According to the international wind power classification standard, Socotra Island area falls under class 7 and is classified as ‘Superb’ for most of the year because it has an average wind power density of 3689.7 W/m2 at 50 m height and an average wind speed of 15.2 m/s at 50 m height.
Using
Month (kWh/m2/month) | Heights | ||
---|---|---|---|
10 m | 30 m | 50 m | |
Jan | 170.1 | 640.2 | 1171.8 |
Feb | 65.2 | 243.9 | 449.8 |
Mar | 28.7 | 108.6 | 200.1 |
Apr | 15.8 | 58.5 | 110.3 |
May | 71.7 | 270.1 | 498 |
Jun | 783.2 | 2930.3 | 5435.9 |
Jul | 1501.4 | 5617.1 | 10328.5 |
Aug | 1250.4 | 4656.5 | 8673.8 |
Sep | 421.4 | 1579.5 | 2930.3 |
Oct | 63.7 | 242.2 | 442.2 |
Nov | 90.1 | 332.4 | 619.6 |
Dec | 252.1 | 933.4 | 1730 |
Since wind power is proportional to the axis height, the average annual wind energy density was 4675.2 KWh/m2/year at 10 m, 17467.4 KWh/m2/year at 30 m, and 32321.8 KWh/m2/year at 50 m, respectively. It can be seen that the variation of the wind energy intensity pattern follows the average wind velocity.
In this research, wind speed data were collected for five years on Socotra Island-Yemen and the wind energy potential of the site was studied based on the Weibull model. Monthly and annual wind data analysis was performed to verify wind characteristics on Socotra Island, such as monthly and annual wind speeds, probability density distributions, and cumulative distributions.
The capacity factor of 10 selected turbines from several international companies was also calculated from a Weibull model resulting from analyzing the annual wind speed data for the site on Socotra Island.
The most important results obtained are as follows:
The analysis showed that the Weibull distribution fits the field data better than the Rayleigh distribution for five years. The five-year average value of the scale and shape parameters were 6.37 and 3.18, respectively. The average yearly wind speed was calculated at different heights, and the results were 8 m/s at 10 m, 12.3 m/s at 30 m, and 15.2 m/s at 50 m. Capacity factor analysis showed that the turbine with the highest capacity factor value CF = 0.6366 was the one manufactured by the German b.ventus company. The average yearly wind power density was calculated at different heights, and the results were 533.7 W/m2 at 10 m, 1994 W/m2 at 30 m, and 3689.7 W/m2 at 50 m. The average yearly energy density was calculated at different heights, and the results were 4675.2 KWh/m2/year at 10 m, 17467.4 KWh/m2/year at 30 m, and 32321.8 KWh/m2/year at 50 m.
Socotra Island area falls under ‘Class 7’ and is classified as ‘Superb’ for most of the year according to the international wind power classification. The current work is a preliminary study that only assessed the potential of Socotra Island’s wind energy to give useful insights to engineers and experts dealing with wind energy.