Energy Harvesting and Systems

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Energy Harvesting and Systems 2017; aop

Omid Alavi*, Ali Mostafaeipour, Ahmad Sedaghat and Mojtaba Qolipour

Feasibility of a Wind-Hydrogen Energy System Based on Wind Characteristics for Chabahar, Iran https://doi.org/10.1515/ehs-2017-0003

Abstract: The knowledge of wind speed characteristics of a region is among the most important aspect of wind turbines utilization for electricity production and assessing the cost of power generation. The wind spectrum and the wind power density for the city of Chabahar located in the southeastern part of Iran were modeled using Weibull distribution and power law estimation. An empirical approach was used to determine the shape parameter, k, and the scale parameter, c, of Weibull distribution function at different heights from 2014 to 2016 during two years period. Wind characteristics in Chabahar were extensively analyzed along with assessing the effects of parameters such as air humidity and temperature, surface roughness, turbulence, and wind velocity durations. The amount of wind power that can be produced by installation of eight wind turbines with different powers ranging from 2.5 kW to 8 MW at Chabahar were investigated. Additionally, the annual capacity factor for each turbine was determined. A wind-hydrogen system was considered in the analysis for evaluating the hydrogen production ability from wind energy in the station at Chabahar. The highest amount of hydrogen production was related to Vestas V164 with the yearly value of 194.36 ton-H2. Keywords: wind energy, Weibull distribution, hydrogen production, capacity factor, Chabahar city

1 Introduction Electricity supply has always been a major concern for modern society; therefore, people have always been looking for clean and unlimited sources of energy and actually have been quite successful in the last centuries to find a *Corresponding author: Omid Alavi, Department of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran, E-mail: [email protected] Ali Mostafaeipour, Industrial Engineering Department, Yazd University, Yazd, Iran Ahmad Sedaghat, Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran Mojtaba Qolipour, Industrial Engineering Department, Yazd University, Yazd, Iran

way for utilization of two widely used kinds of renewable energies of wind and solar. The first solar cell was invented in 1883, and the first use of wind turbine for the generation of electricity was realized in 1887 (Gevorkian 2007; Wikipedia - Wind turbine 2017). Wind is one of the manifestations of solar energy and is actually the flow of the air in the earth’s atmosphere; in fact, a small fraction of solar radiation that reaches into the atmosphere is continuously converted to the wind energy. The heating of the earth and its atmosphere in an unequal extent generates convective currents and the motion of the atmosphere relative to earth generates the wind. Humans have used wind energy in different ways for a long time. Persians were the first people who used windmills for grinding grains in about 200 BC and the remaining effects of this historical achievement can still be observed in the regions around Khaf and Taibad in the East of Iran. The ancient Egyptians also used wind power to propel their ships in the Nile River. Mounting sails on a central shaft created the possibility of using wind energy to draw water for irrigation, to grind grains and eventually to saw wood logs. In the seventeenth century, the Dutch improved the basic design of windmills. This improvement helped in turning the Netherlands into one of the richest and most industrialized countries in Europe. Many countries used windmills for grinding wheat and corn, pumping water and cutting wood logs (Metz et al. 2001). Wind catchers were used by Persians over 1,000 years ago and there are many wind catchers in the central parts of Iran to harness wind and use it to cool the air inside the buildings (Mostafaeipour 2010). Solar and wind are the most popular sources of renewable energies widely used in different countries in order to reduce environmental pollution. Wind turbines are supplying required electricity in suitable windy regions (Khorasanizadeh, Mohammadi, and Mostafaeipour 2014; Mohammadi, Mostafaeipour, and Sabzpooshani 2014; Mohammadi and Mostafaeipour 2013; Dinpashoh et al. 2014). There have been numerous studies investigating the wind energy potential in different parts of Iran, such as the cities of Zahedan and Binalood (Mostafaeipour et al. 2014; Mostafaeipour et al. 2013). Aerodynamic design, performance, and economic viability of horizontal and vertical axis wind turbines were evaluated recently with the

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O. Alavi et al.: Feasibility of a Wind-Hydrogen Energy System

aim of achieving better performance (Saeidi et al. 2013; Sedaghat, Assad, and Gaith 2014).

1.1 Potential of Different Countries in Harnessing Wind Energy In 1981, “International Institute for Applied Systems Analysis” estimated that the global technical potential for wind power is 480,000 TWh per year. Given the economic constraints, it is acceptable to assume that harnessing 4 % of this energy, which equals to 20,000 TWh/yr, can be achievable (equivalent to a 2.3 TWh power plant operating for 24 × 365 hours per year). According to the global wind map, it is estimated that 27 % of Earth’s land area (approximately 3 × 107 km2) is exposed to winds with an annual mean speed of more than 5.1 m/s, or 18.3 km/hr at the height of 10 meters above the ground (Metz et al. 2001). Table 1 shows the Earth’s land area exposed to the wind with different velocities of more than 5.1 m/s (Re-energy 2017). Table 1: Global distribution of suitable areas for wind turbines (Reenergy 2017). Region

North America Latin America Western Europe Eastern Europe Middle East and North Africa South Africa Pacific Ocean China Central and South Asia Total

The total land surface ×  (km)

Suitable area for wind turbines ×  (km)

, , , , ,

, , , , ,

, , , ,

, , , 

,

,

According to the statistics, each kilowatt-hour of electrical energy produced by wind can prevent approximately one kilogram of CO2 emissions produced by burning fossil fuel in power plants. While most of the electricity in Iran is produced by fossil fuels and the cost of investment in wind power plants is higher than that of the fossil fuel plants, with the advancement of technology in the construction of wind turbines and also considering its social and environmental benefits, the wider use of this energy in Iran has reached the stage of becoming economical. As we can see significant

expansion in number of wind power plants we can also observe reduced costs of producing electricity so that the price of per kilowatt-hour electricity reduced from 4–6 cents in 2000 to almost 3–4.5 cent/kWh in 2007 while it was 40 cents in 1979. Lack of fuel consumption, low operating costs, low maintenance and lack of environmental pollutions are the advantages of wind power plants. Studies show that the reduced costs of constructing wind power plants also decrease the cost of produced energy. Surely, in a near future, this energy can compete with the energy produced by fossil fuel power plants (Shaahid and El-Amin 2007). It is estimated that by 2035, about 25 % of the world’s electricity will be generated from renewable sources that will lead to creation of more jobs as well as reduction of CO2 emissions (Global Wind Report. Global Wind Energy Council 2017). In 2015, the energy produced by wind power constituted about 4.8 % of global production of electricity, and in seven countries (Denmark, Portugal, Ireland, Spain, Germany, Sweden, and UK) this percentage was more than 10 % (IEAWIND 2015). In 2016, more than 55 GW was added to the capacity of wind power production which shows a decrease of about 16 % compared to 2015. By the end of 2016, the number of countries with production capacity of more than 1,000 MW was 26, including South Africa in Africa & Middle East, 5 in Asia (China, India, Japan, Australia and South Korea), 16 countries in Europe, 3 in Latin America & Caribbean (Brazil, Chile, and Uruguay), and 3 countries in North America (Canada, Mexico, and the USA). Table 2 shows that by the end of 2016, only nine countries had a total capacity of more than 10,000 MW.

Table 2: The top ten global installed wind power capacity (Global Wind Report. Global Wind Energy Council 2017). Country

Total capacity (MW)

% Share

New installed capacity (MW)

PR China USA Germany India Spain United Kingdom France Canada Brazil Italy Rest of the world Total Top  World Total

, , , , , , , , , , , , ,

. . . . . . . . . . .  

, , , ,   ,  ,  , , ,

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O. Alavi et al.: Feasibility of a Wind-Hydrogen Energy System

The ten countries were able to install about 88 % of all the new capacity created in 2016, where China ranked first with 42.7 % and Canada ranked tenth with 1.3 %. In general, most countries are trying to increase their wind power capacity over the next few years. For example, Canada has several plans to expand its wind power capacity. Figure 1 illustrates the annual increase in wind power capacity from 2007 to 2016 (Global Wind Report. Global Wind Energy Council 2017). It shows that the wind power capacity which was 93,924 MW in 2007, reached up to 486,790 MW in 2016, indicating an increasing trend over time.

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for other countries that have a long way to go in this field. Many developing economic resources are located in Asia and growing economies of Asian countries such as Iran, have led these countries to produce electricity from non-fossil sources more than ever. Furthermore, the lack of nationwide power grid in many rural areas of Asian countries is itself an acknowledgment to the need for the systems of wind based electricity generation. At the same time, there are widely-known wind farms in Iran such as Manjil, Roudbar and Khorasan which have been used to generate electricity from wind energy (Ministry of Energy 2017). Figure 2 illustrates Iran’s energy production from 2005 to 2014 which shows increments from 2005 to 2008 while becomes almost constant in the period of 2009 to 2013, but again has a sharp increase since then.

Figure 1: Global wind power production capacity (Global Wind Report. Global Wind Energy Council 2017).

Iran has abundant fossil resources, but it has also concluded that it must increase the share of renewable sources, especially wind power in its total power production capacity, and has also made some efforts in that direction to further diversify its portfolio of energy production. The Iran’s wind power production capacity is approximately 153.5 MW and the efforts of Ministry of Energy in finding other potential sites for installing new wind turbines and also the attempts to localize this industry, show Iran’s attempt to improve the use of wind energy (Ministry of Energy 2017).

1.2 Wind Power in Iran Energy supply market is a competitive market where electricity generation by wind power plants has demonstrated new advantages over fossil fuel power. One advantage of wind power plants is that they will produce energy without the need for the expensive fuel, over the course of many years of their lifetime, while the cost of other energy sources will increase over these years. Extensive activities of many countries in expanding wind-based electricity generation can be a good example

Figure 2: Wind energy production capacity in Iran (Ministry of Energy 2017).

According to the Iran’s Ministry of Energy (Ministry of Energy 2017), in the year 2006, there was no private investment on the construction of 60 MW wind farm and investors only showed interest for construction and development of Manjil’s wind farm. One of the reasons behind the lack of proper investment in this sector could be the lack of sufficient awareness; therefore, proper measures should be taken to inform investors on the potential of wind-based power generation in Iran’s various regions. The Islamic Republic of Iran is located in the western part of the Iranian plateau in the South-West Asia and has a great climatic diversity. Northern regions of Iran have a temperate climate and considerable precipitation, especially in the western parts of Gilan province. Western regions of the country have a cold and wet weather in winters and a dry and temperate one in summers. In southern regions, the temperature and the humidity are higher, and very hot summers and temperate winters are the climatic characteristics of this region and the daily temperature variation is less pronounced. Eastern and south-eastern regions have desert climate with considerable temperature variation throughout the day. The

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O. Alavi et al.: Feasibility of a Wind-Hydrogen Energy System

existence of reliable wind data and also reliable assessments on wind potential are essential for the construction of wind-based power plants in a region. The presence of windy areas in Iran has created a suitable ground to expand the use of wind turbines. Iran’s wind power plant installation capacity has been growing since 2005, and has reached from 47,580 kW in 2005 up to 153,500 kW in 2014. World Wind Energy Association (WWEA) has planned a strategy to increase the share of wind power in supplying global energy consumption to about 15 % by 2020 (World Wind Energy Association 2015). According to the great potential of wind energy in Iran to generate electricity, more comprehensive studies for investigating the ability of wind turbines in the country are needed to be conducted. Today, due to the probabilistic behavior of the wind, storing power generated as hydrogen is a suitable solution for reducing the risk of absence of wind at all times. There are different studies on the hydrogen production from wind energy in various countries. AicheHamane et al. (Aiche-Hamane et al. 2009) studied the feasibility of hydrogen production from wind energy in the station at Ghardaia. In this analysis, a 5 kW electrolyzer supplied by a 10 kW small wind turbine was considered. They found that the hub height of wind turbines has a direct influence on the system output so that increasing hub height from 30 to 60 m leads to 31 % increase in hydrogen production. Zhou and Francois (Zhou and Francois 2009) investigated a control process for an active wind/hydrogen energy system. They concluded that the examined control method can control power and hydrogen flow simultaneously. García Clúa et al. (Clúa, Battista, and Mantz 2010) analyzed the control of a grid connected windhydrogen system considered for producing hydrogen. A robustness sliding mode method was proposed in the control design. In conclusion, this approach results in an improved wind-hydrogen conversion system. Mostafaeipour et al. (Mostafaeipour et al. 2016) assessed the suitability of wind energy for hydrogen production in the province of Fars, Iran. They estimated that a 900 kW wind turbine installed in the site at Abadeh can provide the required hydrogen fuel for 22 cars/week. Weidong and Zhuoyong (Weidong and Zhuoyong 2012) investigated the hydrogen production process using water electrolysis when a non-grid-connected wind turbine is utilized. They concluded that the large scale variations in the current density can affect on the gas quality. Additionally, they commented that the new system will develop the use of wind power in China. Loisel et al. (Loisel et al. 2015) evaluated the economic feasibility of a

tested wind-to-hydrogen system in France. The calculated hydrogen production cost for these projects was in the range of 4 to 13 €/kg-H2. Other studies on the hydrogen production from wind turbines have been conducted in different countries, such as Canada (Olateju, Kumar, and Secanell 2016), Argentina (Rodríguez et al. 2010; Sigal, Leiva, and Rodríguez 2014), Spain (Gutiérrez-Martín, Confente, and Guerra 2010), Turkey (Akyuz, Oktay, and Dincer 2012), Portugal (Parissis et al. 2011), and Norway (Ulleberg, Nakken, and Eté 2010; Greiner, Korpås, and Holen 2007). The rest of this paper is structured as follows: In Section 2, geographic characteristics are presented. In Section 3, methodology of this study is completely explained. In Section 4, results of the study work are presented. Conclusion is drawn in Section 5.

2 Geographic Characteristics of Chabahar Chabahar is located in Sistan and Baluchestan province in Iran (as shown in Figure 3), and has the only port in Iran with access to the ocean (Sistan and Baluchestan Province Web Portal 2017; Wikipedia – Chabahar 2017). Chabahar county has an area of 24,729 km2 and is located in the most southeastern part of Iran along the gulf of Oman and the Indian Ocean (Sistan and Baluchestan Province Web Portal 2017; Wikipedia – Chabahar 2017). This county has three main districts: Central, Dashtiari,

Caspian Sea

Persian Gulf

Chabahar Gulf of Oman

Figure 3: Map of Iran and location of Chabahar.

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O. Alavi et al.: Feasibility of a Wind-Hydrogen Energy System

and Polan. Given the Chabahar geographical position, it has always been under the influence of several climate systems such as Indian subcontinent monsoon (seasonal winds), tropical fronts and low-pressure center, and western fronts originating from the Mediterranean (Sistan and Baluchestan Province Web Portal 2017). Chabahar has always endured severe storms especially in Makran sea and its shore; these storms are caused by Indian monsoon winds (in summers, wind direction is from the south) and the occasional progress of low-pressure centers and tropical fronts from Indian Ocean to Makran Sea. The creation of low-pressure area in the southern Iranian plateau in summers causes winds coming from northwest direction in the afternoons. This county is bordered to the Iranshahr and Nikshahr counties in north, and is limited to gulf of Oman in south, to Pakistan border in east, and to Kerman and Hormozgan provinces in west. The Chabahar port, the county capital, has an area of over 11 km2 and is located at 60° 37ʹ eastern longitude and 25° 17ʹ northern latitude at an altitude of 7 meters above sea level. The Chabahar port’s strategic importance is because of its position as the nearest access road of land-enclosed Central Asian countries (Afghanistan, Turkmenistan, Uzbekistan, Tajikistan, Kyrgyzstan and Kazakhstan) to the high seas (Wikipedia – Chabahar 2017).

3 Methodology Knowing the wind speed distribution in a region has an essential role in determining and estimating wind potential of that region. If we manage to determine the wind speed distribution in the study region, we can easily calculate wind power and wind turbine’s economic factors. Given the broad parameters of wind energy analysis, only the key parameters for wind data should be analyzed. The easiest and the most practical method for the mentioned process is the use of distribution function. There are some mathematical functions that can be used to model the wind curve. These functions include Weibull, Rayleigh, Gamma, Beta, Gaussian and lognormal distribution functions.

3.1 Wind Speed Distribution Rayleigh and Weibull probability density functions are the most commonly used methods for the analysis of wind power. Given that Rayleigh method is a subset of

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Weibull, this study only used Weibull density function. Weibull distribution function has many advantages over other functions including: flexibility, depending on two parameters, simple calculations, suitable fitting with data (Carta, Ramirez, and Velazquez 2009). The main disadvantage of Weibull distribution function is that it cannot accurately predict the probability of wind in near-zero speeds. However, given the wind speed needed for the commercial turbines to start (2.5 to 3.5 m/s), the effects of mentioned inaccuracy is negligible (Keyhani et al. 2010). Accurate determination of Weibull probability density function needs the calculation of two parameters: (1) shape parameter (dimensionless), and (2) scale parameter (m/s). To calculate these parameters, we must first define, v and Γ(x), and σv functions. v is the average wind speed determined by (Mostafaeipour et al. 2014; Mostafaeipour et al. 2013): ! N 1 X v = (1) vj N j=1 Γ(x) is gamma function defined by: ∞ ð

Γ ðxÞ =

ux − 1 expð − uÞdx

(2)

0

σv is the standard deviation calculated according to eq. (3) (Keyhani et al. 2010): vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u N  u 1 X 2 σv = t vj − v N − 1 j=1

(3)

where N is the number of recorded data. Finally, the Weibull density function can be obtained using (Keyhani et al. 2010; Ucar and Balo 2009; Saeidi and Mirhosseini 2011; Sathyajith 2006; Mostafaeipour et al. 2011; Mirhosseini, Sharifi, and Sedaghat 2011):     k v  k − 1 v k fW ðv Þ = (4) exp − c c c Basically, the shape parameter (c) indicates how much windy an area is; k represents the shape parameter and defines the rate of maximization in wind speed distribution function. This means that as wind speed gets closer to a specific value, the value of k increases. A review on previous studies shows that shape parameter in Weibull distribution of most windy regions of the world is between 1.2 and 2.75 (Mirhosseini, Sharifi, and Sedaghat 2011). There are several methods for calculating the parameters of the Weibull function. The standard deviation method is used to determine the shape and scales

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O. Alavi et al.: Feasibility of a Wind-Hydrogen Energy System

parameters in this study. Using this method, the parameters k and c can be obtained from eqs. (5)‒(7) as (Mostafaeipour et al. 2014): k=

σ  − 1.086 v v

(5)

v  c=  Γ 1 + k1

(6)

c k 2.6674 = v 0.184 + 0.816k 2.73855

(7)

Using the Weibull distribution function we can also express the values of σv and v as follows (Mostafaeipour et al. 2014; Sathyajith 2006; Mostafaeipour et al. 2011): v =

∞ ð

 vfW ðvÞdv = cΓ 1 +

1 k

 (8)

0

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi u     2 ! u 2 1 − Γ 1+ σv = tc2 Γ 1 + k k

(9)

P 1 = ρ A 2

∞ ð

  1 3 3 v f ðvÞdv = ρc Γ 1 + 2 k 3

where ρ is the air density, which at standard conditions (i. e., mean temperature of 15 °C and pressure of 1 atmosphere) is equal to 1.225 kg/m3. Air density is a function of temperature and pressure and it changes with altitude. Therefore, following equation can be used to determine the air density at the height of Z above sea level: (Mostafaeipour et al. 2014; Keyhani et al. 2010; Mostafaeipour et al. 2011): ρ=

 P Rd T

 

v k FðvÞ = 1 − exp − c

(10)

3.2 Wind Power and Wind Energy Density Wind power density is another necessary factor for the analysis of wind resources in a region which indicates that how much wind energy can be converted into electricity. Wind power per unit can be calculated by (Keyhani et al. 2010; Ucar and Balo 2009; Saeidi and Mirhosseini 2011; Sathyajith 2006; Mostafaeipour et al. 2011):  P 1 3 = ρv W=m2 A 2

(11)

Based on the Weibull probability density function, wind power can be calculated by (Keyhani et al. 2010; Ucar and Balo 2009; Saeidi and Mirhosseini 2011; Sathyajith 2006; Mostafaeipour et al. 2011; Mirhosseini, Sharifi, and Sedaghat 2011):

(13)

 is the monthly average air pressure (Pa), T is In eq. (13), P the monthly average air temperature (K), Rd is the gas constant (with a value of 287 J/kgK for dry air). Furthermore, for the heights of less than 100 meters, wind power density above ground level can be obtained from (Ucar and Balo 2009): Ph = P10

Another important analytical parameter is the cumulative distribution function. The cumulative distribution function estimates the probability of wind speed being in a given range (The probability that the wind speed is less than or equal to the given wind speed v) as follows (Mirhosseini, Sharifi, and Sedaghat 2011):

(12)

c

 3α h 10

(14)

where P10 is the modified wind power at the height of 10 meters and α is the roughness factor that usually has a value between 0.05 and 0.5. Considering eq. (12), the wind energy density can also be obtained as (Saeidi and Mirhosseini 2011): E = PT    E P = ðN.Δt Þ A A

(15) (16)

where N is the number of measurement time intervals Δt. Equation (16) can be used for any time intervals. According to the Betz limit, a wind turbine cannot extract more than 59.3 % of the available wind power (Keyhani et al. 2010). Thus, the maximum power which can be extracted from the wind is the product of 0.593 and the computed wind power from eq. (12).

3.3 Wind Speed Extrapolation If we want to assess wind speed at the height of 10 meters, we should consider that wind speed increases with height, so we must recalculate analysis parameters for the desired height. In order to use Weibull distribution function, the shape parameter at the height of h which is represented by kh, and the scale parameter at the height of h which is represented by ch must be calculated by the

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O. Alavi et al.: Feasibility of a Wind-Hydrogen Energy System

values of the parameters at the height of 10 meters (Mostafaeipour et al. 2014): kh =

k10 1 − 0.088 lnðh=10Þ  n h ch = c10 10

(18)

(19)

In this study, wind data were used to calculate the Weibull parameters for Chabahar city at the heights of 10, 30 and 40 m from 1/2014 to 12/2015. The approach that is commonly used to extrapolate the wind speed to a greater height even more than 100 m, is the power law. The basis of this approach is complex nature of turbulent flows. In order to calculate the extrapolated wind speeds to a certain height by using this approach, the following equation should be used:  α z2 v2 = v1 (20) z1 where v1 and v2 are the velocity at heights of z1 (lower height) and z2 (upper height). The parameter of α is the wind shear exponent, which is dependent to elevation, time of day, season, temperature, terrain, and atmospheric stability. In this study, power law exponent coefficient (α) was equal to 0.0763.

3.4 Energy Pattern Factor The energy pattern factor is a parameter used in aerodynamic design of turbines and can be obtained by dividing the cube of the wind speed by the cube of mean wind speed (Keyhani et al. 2010; Sathyajith 2006): ke =

N 1 X v3 Γð1 + 3=kÞ vi3 = 3 = 3 v Nv i = 1 Γð1 + 1=kÞ

(21)

In eq. (21), N is the total number of data in one year. By using the Weibull parameters, the most probable wind speed, vmp (m/s), and the wind speed carrying the maximum energy, vmax (m/s), can be calculated, respectively as (Sathyajith 2006; Mostafaeipour et al. 2011; Mirhosseini, Sharifi, and Sedaghat 2011):  vF Max = c

k−1 k

 1 k+2 k k

(23)

(17)

where n is the power law exponent defined by (Mostafaeipour et al. 2014): n = ð0.37 − 0.088 lnðc10 ÞÞ

vE Max = c

k1 (22)

3.5 Probability of High Winds In this type of wind analysis, it is often necessary to determine the probability of wind speeds greater than 5 m/s. The probability of a wind speed occurring between two desired values can be computed by (Sathyajith 2006): ðb P ða < v < bÞ =

pðvÞdv

(24)

a

The total area of probability distribution curve is equal to 1. To calculate the probability of a wind speed greater than 5 m/s, following equation can be used: ∞ ð

P ð5 m=s < vÞ =

pðvÞdv

(25)

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3.6 Surface Roughness There are several methods to predict wind logarithmic profile (including the mixing length theory, eddy viscosity theory, and similarity theory). In 1982 Wortman defined Mixing length analysis in the following form (Saeidi and Mirhosseini 2011; Sathyajith 2006): vð z Þ = lnðz=z0 Þ= lnðzh =z0 Þ vðzh Þ

(26)

where v(z) is the wind speed at height z, v(zh) is the wind speed at a reference height zh, and z0 is the surface roughness length. By placing the mean wind speeds at the heights of 10, 30 and 40 m and fitting them with eq. (26), we found the surface roughness length for Chabahar, which was equivalent to 0.04 mm.

3.7 Capacity Factor Capacity factor is an important parameter in evaluating the performance of wind turbines. The capacity factor is defined as the ratio of actual energy production to expected total energy. It can be obtained from (Ucar and Balo 2009; Sathyajith 2006):

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O. Alavi et al.: Feasibility of a Wind-Hydrogen Energy System

CF =

ET T × PR

(27)

Equation (27) represents the fraction of the total energy delivered over a period, ET, divided by the maximum energy that could have been delivered if the turbine was used at maximum capacity over the entire period (T × PR). The capacity factor is usually expressed as an annual factor, and generally ranges from 0.25 to 0.4 for a site. The value of 0.4 or higher for the capacity factor indicates a very good performance of the turbine at the installation site (Sathyajith 2006). The capacity factor is used in the calculation of the energy produced by the turbine in a year as (Mostafaeipour et al. 2011): E = 8760 × CF × PR

3.8 Power and Energy Generated by Turbine The calculations of average power and energy which will be generated by the wind turbine are very important and necessary for each project. Average energy production of wind turbines (with the assumption of receiving total power) can be obtained by (Burton et al. 2011): ð E = T P ðvÞf ðvÞdU (29) where, T is the time interval, P(v) is the wind turbine power curve, and f(v) is the probability density function of wind speed.

(28)

where PR is the nominal power of the desired wind turbine. It should be noted that increase in the turbine tower height increases the capacity factor. However, in the process of making decision we must also consider the fact that tower constitutes about 20 % of the turbine’s costs and the cost for each 10-meter part of the tower is $15,000 (Sathyajith 2006). Generally, it can be mentioned that a higher capacity factor is better and in particular, more economical (Wind Energy Center 2013). The capacity factor of a wind turbine is a design decision, and influential factors on the capacity factor at a specific wind farm will definitely affect the optimal turbine selection. In the prior studies (e.g, Jangamshetti and Rau 2001), wind turbine performances were based on their capacity factor, because, in fact, the capacity factor indicates the percentage of electrical power, which a wind turbine generator can generate from the available wind.

4 Results In this study, we assessed and evaluated wind speed data at three heights of 10, 30 and 40 m gathered from 1/1/ 2014 to 12/31/2015. The study utilized Weibull parameters, mean wind speed, and mean wind power, as explained in the following sections.

4.1 Mean Wind Speed The monthly mean wind speeds (v) in the city of Chabahar at three heights of 10, 30, and 40 m, and also their corresponding standard deviations (σ) are presented in Table 3. According to Table 3, most of the speed variations at the height of 10 m are in the range of 4 to 6 m/s. The

Table 3: Mean wind speeds in Chabahar at heights of 10, 30, 40 m and standard deviations.  m

Months

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec All Data

 m

 m

v (m/s)

σ (m/s)

v (m/s)

σ (m/s)

v (m/s)

σ (m/s)

. . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

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highest probability at this height is approximately 28 % and belongs to the speeds between 3.5 and 5 m/s. The probability of speeds less than 2 m/s or greater than 8 m/s is negligible. In the studied period, the highest mean speed belongs to data recorded on 24/01/2015 and is equal to 15.2 m/s. The maximum speed also belongs to 24/01/2015 and is equal to 21.8 m/s. The lowest speed of the study is equal to 0 m/s and was usually recorded in August and September. Standard deviation values vary from 1.517 in December 2014 to 2.612 in July 2015. The monthly mean wind speeds at three heights of 10, 30, and 40 m are shown in Figure 4. According to Figure 4, the lowest value for monthly mean wind speed at 10 m is equal to 3.8 m/s, and belongs to December and its highest value is 5.98 m/s which belongs to May. According to the geographical location of Chabahar, the city almost constantly experiences a warm climate. This notion can be expressed with the fact that in around 99 % of the days per year the air temperature is around 15 °C or higher. In the studied period, this city’s coldest day of the year was on January 24th and the minimum temperature was also recorded on the same day and was about 10.5 °C. The city’s warmest day was in May and the highest temperature was 42.3 °C. If we assume the first six months of the year (September–March) as the warm months and the other six months as the cold months, the mean wind speed in warm and cold seasons at the height of 10 m were equal to 4.38 m/s and 5.51 m/s, respectively. The results presented in Table 3 also show that most of the speed variations at the height of 30 m are in the range of 3 to 6 m/s. The highest probability at this height is approximately 26.4 % and belongs to speeds between 4 and 5.5 m/s.

9

At 30 m height, the probability of speeds less than 2 m/s or greater than 8 m/s is also negligible. In the studied period, the highest mean speed belongs to data recorded on 3/3/ 2015 and is equal to 16.8 m/s. The maximum speed was recorded on 24/01/2015 and is equal to 23.3 m/s. The lowest wind speed of the study was equal to 0 m/s and was recorded in September. Standard deviation values vary from 1.654 in December 2014 to 3.11 in May 2015. According to Table 3, we can also see that most of the speed variations at the height of 40 m are in the range of 4‒7 m/s. The highest probability at 40 m height is approximately 33.6 % and belongs to speeds between 4 and 6 m/s. At the height of 40 m, the probability of speeds less than 2 m/s or greater than 8 m/s is also negligible. In the studied period, the highest mean speed belongs to data recorded on 24/01/2015 and is equal to 17.6 m/s. The maximum speed was recorded on 24/01/2015 and is equal to 24 m/s. The lowest speed of the study was equal to 0 m/s and was recorded in October and September. Standard deviation values vary in the range of 1.847 for August 2014 to 3.413 for May 2015.

4.2 Diurnal Wind Speed Variations Daily changes in wind speed are shown in Figure 5. According to Figure 5, changes in wind speed during the day and night are small. At 6:00, wind speed starts to increase and then reaches its peak value at 12:00. At this hour and at the height of 10 m the wind speed is normally about 5.24 m/s. The peak speed does not change until about 14:00, and then gradually decreases and reaches its minimum value at 18:00 to 19:00; this minimum value is 4.44 m/s

Figure 4: Monthly wind speed profile.

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Figure 5: Diurnal variation of wind speed.

at the height of 10 m. Given the small variation of wind speed during each day, its energy can be used for street lighting at night, and to generate electricity during days when the speed is higher.

4.3 Wind Gusts in Chabahar The gust wind speeds and the time of their occurrence, the energy pattern factor (ke) and the probability of wind speeds greater than 5 m/s at the heights of 10, 30 and 40 m are shown in Table 4. Table 4: Wind characteristics of Chabahar. Height Gust wind speed Energy pattern factor P(v >  m/s)

 m

 m

 m

 m/s : // .

. m/s : // .

. m/s : // .

.%

.%

.%

4.4 Weibull Distribution As discussed in the Methodology Section, Weibull probability density function is a useful criterion for assessing the potential of wind energy in a region. It has two parameters of k and c. The larger value of c indicates a more spread out distribution. Higher values of k (between 2 and 3) means that the distribution is more concentrated around high speeds. And its lower values (between 1 and 2) means that the distribution is more concentrated

around low speeds, which indicates the high probability of low speeds. However, both parameters affect the distribution curve (Mostafaeipour et al. 2011). The corresponding wind data and the best fits to the twoparameter Weibull distribution at the heights of 10, 30 and 40 m for Chabahar are shown in Figures 6, 7 and 8. According to Figure 6, the most probable wind speeds are between 3.5 and 5 m/s at the height of 10 m. Additionally, its whole frequency for these speeds is about 27 %. Figure 7 shows that the most probable wind speeds have a value between 4 and 6 m/s at the height of 30 m. At this height, total frequency of these speeds is about 34.8 %. However, Figure 8 shows that the most probable wind speeds have not changed at height of 40 m and are similar to data recorded at 30 m. But the frequency in this case has changed to 33.6 %. The monthly mean values of the parameters k and c for Chabahar, computed using eqs. (5)‒(7), are shown in Table 5. The parameter of k is dimensionless and c is expressed in terms of m/s. It can be seen that the values of k are lower than values of c. All calculated values of k are between 2 and 3 and their values vary between 2.209 and 2.868, and their annual mean value is 2.397. The values of c parameter also vary between 4.269 and 6.742 m/s and their mean value in this study is 5.44 m/s. The minimum value of c occurs in December and the maximum value happens in May. The important analytical parameter of cumulative distribution function, for the city of Chabahar needs to be drawn for three heights of 10, 30, and 40 m according to eq. (10). Figure 9 shows the cumulative distribution function at the three mentioned heights. It should be noted, for example, speeds greater than 4 m/s at three heights of 10, 30 and 40 m happen 62.59 %,

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10

Probability (%)

8

6

4

2

0

0

4

8

12

16

Wind Speed (m/s) Actual data

Best-fit Weibull distribution (k=2.39, c=5.44 m/s)

Figure 6: Annual frequency distributions of wind speed for Chabahar (10 m).

10

Probability (%)

8

6

4

2

0

0

5

10

15

20

Wind Speed (m/s) Actual data

Best-fit Weibull distribution (k=2.37, c=5.86 m/s)

Figure 7: Annual frequency distributions of wind speed for Chabahar (30 m).

68.68 % and 70.64 % per year, respectively. This speed threshold of 4 m/s is important, because of the cut-in speed of a typical turbine. Cut-out speed of turbines is usually about 20–25 m/s (Mirhosseini, Sharifi, and Sedaghat 2011).

4.5 Air Humidity and Temperature The information regarding the measured relative humidity and air temperature based on the used data sets of this study is presented in Table 6.

4.6 Wind Direction Wind direction is one of the important factors in wind energy conversion calculations. If a large proportion of wind energy is coming from a specific direction, it is absolutely necessary to avoid blocking the air flow through that direction. Changes in wind direction are mainly caused by the change in climate or season (Mirhosseini, Sharifi, and Sedaghat 2011). In this section, wind direction is expressed regardless of the wind distribution. The wind direction data in this study are specific to two heights of 30 and 37.5 m.

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O. Alavi et al.: Feasibility of a Wind-Hydrogen Energy System

10

Probability (%)

8

6

4

2

0

0

5

10

15

20

Wind Speed (m/s) Actual data

Best-fit Weibull distribution (k=2.30, c=6.08 m/s)

Figure 8: Annual frequency distributions of wind speed for Chabahar (40 m).

Table 5: Monthly mean values of Weibull parameters (k, and c), and characteristic speeds (at 10 m height, in m/s). Months

k

c

vFmax

vEmax

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec All Data

. . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

Table 6: Environmental characteristics of Chabahar.

Temp (°C) Air Density(kg/m) Average relative humidity

Mean

Max

Min

. . . %

. .

. . –

Figures 10 and 11 show the mean values and the monthly variations of wind direction at two heights of 30 and 37.5 m. For example, with regard to Figures 10 and 11, the annual mean value of wind direction for the city of Chabahar at heights of 30 and 37.5 m are 188.7° and 191.1°, respectively. It is important to mention that lower values of the standard deviation of wind direction, indicates better conditions for that site. As an example, in

Figure 9: Cumulative density at three heights of 10, 30, and 40 m.

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360 max daily high

Wind Direction (°)

300

mean daily low

240

min

180 120 60 0 Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Ann

Figure 10: Mean value of wind direction and its standard deviation (30 m).

360 max daily high

Wind Direction (°)

300

mean daily low

240

min

180 120 60 0

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Ann

Figure 11: Mean value of wind direction and its standard deviation (37.5 m).

this study, we have the best conditions for wind direction in the month of August.

4.7 Wind Rose A wind rose can demonstrate the information about wind direction and wind speed in a combined form. Wind rose is a diagram showing the wind distribution across different directions. This diagram is often plotted in 8, 12, 16 sectors. But in cases where wind direction data is expressed in 10-degree interval, it should be plotted in 36 sectors. The information that can be obtained from the wind rose diagram is the percentage of time for which we receive wind from a particular direction. This can show the direction from which we get most of our wind (Sathyajith 2006). Figures 12 and 13 show the wind rose plots at two heights of 30 and 37.5 m in Chabahar. In Figures 12 and 13, the longest column represents the dominant wind direction. For instance, at the height of 30 m, the wind which constitutes the highest proportion of total wind data (about 12.5 %), is blowing from 157.5° direction.

Figure 12: Wind rose of Chabahar (30 m).

4.8 Turbulence Wind speed is constantly changing because wind must pass through peaks and valleys, cliffs and buildings, and this creates turbulence in the wind. In fact, the turbulent wind is caused by the dissipation of wind kinetic energy

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measuring turbulence is to calculate turbulence intensity. Turbulence is usually obtained by dividing the standard deviation σ by the wind speed v, at 10-minute intervals. By placing the mean speed in turbulence equation, the turbulence intensity is obtained as (Brower 2012): TI =

σv v

(30)

Generally, as wind speed increases to about 7–10 m/s, turbulence intensity decreases. Turbulence intensity at the speeds of more than 10 m/s ranges from 0.1 to 0.25 (depending on circumstances). At the speeds higher than 15 m/s turbulence impact is zero (Brower 2012). The maximum, minimum and mean values of turbulence for the city of Chabahar at three heights of 10, 30, and 40 m are shown in Table 7. Figure 13: Wind rose of Chabahar (37.5 m).

into heat energy. Turbulence is generally associated with very rapid fluctuations in wind caused by variation of air pressure which can usually be seen in wind gusts. When a turbine absorbs wind energy, the area affected by turbine experiences a sudden loss of velocity and turbulence increases. Knowing the turbulence of a site is essential in assessing the installation of wind turbines. Turbulences in wind velocity and direction occur in time intervals between a few minutes to several hours (Brower 2012).

Table 7: Turbulence values for the city of Chabahar. Height

 m

 m

 m

Mean Max Min

. . .

. . .

. . .

The turbulence intensity at three heights of 10, 30 and 40 m are shown in Figures 14, 15 and 16.

4.9 Turbulence Intensity 4.10 Wind Speed Duration Curve As mentioned in the previous section, wind turbulence can be defined as “fast disturbances in wind velocity and direction that can have a significant impact on the performance and loading of turbines”. The most basic method of

Changes in wind speed range can affect the output power. In other words, averaging the changes can cause possible errors in assessing the output power. Another factor in

Figure 14: Turbulence intensity for Chabahar (10 m).

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O. Alavi et al.: Feasibility of a Wind-Hydrogen Energy System

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Figure 15: Turbulence intensity for Chabahar (30 m).

Figure 16: Turbulence intensity for Chabahar (40 m).

wind resource assessment is wind speed duration curve and it is a useful tool for comparing the wind potential of a region (Wadhwa 1989). The velocity duration curve is a graph with wind speed on the y-axis and the number of minutes for which the speed equals or exceeds each particular value on the x-axis (Mirhosseini, Sharifi, and Sedaghat 2011). In Figures 17, 18 and 19, wind speed duration curves for the city of Chabahar for three heights of 10, 30, and 40 m are presented.

4.11 Wind Power Density and Energy Calculation The wind power density will increase with the cube of velocity. But, it can also be calculated using the Weibull parameters. Wind power density and wind energy for the city of Chabahar in three different heights are presented in Table 8.

The highest wind power density at the height of 10 m belongs to May and is equal to 196.67 W/m2. The lowest wind power density is for December and equals to 51.12 W/m2. Meanwhile, the annual average wind power density is 107.55 W/m2. There are various classifications to determine the status of wind power in a given region. One such classification as listed in Table 9 shows that the city of Chabahar is ranked in class 2 [6]. According to following classification, the city of Chabahar is almost in poor class. P A P A P A

< 100 W=m2  400 W=m2 > 700 W=m2

Poor Good Great

Other classification is presented by the European Wind Energy Association (EWEA) in terms of wind profile, and is as follows (Mostafaeipour et al. 2011):

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O. Alavi et al.: Feasibility of a Wind-Hydrogen Energy System

Figure 17: Wind speed duration curve at 10 m.

Figure 18: Wind direction duration curve at 30 m.

Figure 19: Wind direction duration curve at 40 m.

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O. Alavi et al.: Feasibility of a Wind-Hydrogen Energy System

fairly good (6.5 m/s, ≈300–400 W/m2)

Table 8: Power density and energy density for Chabahar.  m

Months

 m

P/A (W/ E (kWh/ P/A (W/ m) m) m) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec All Data

. . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

 m

E (kWh/ P/A (W/ m) m)

E (kWh/ m)

. . . . . . . . . . . . .

. . . . . . . . . . . . .

. . . . . . . . . . . . .

Table 9: Classification for wind power by Elliot and Schwartz (Mohammadi, Mostafaeipour, and Sabzpooshani 2014). Power class       

Power density (W/ Power density (W/ m) at  m m) at  m  < P ≤   < P ≤   < P ≤   < P ≤   < P ≤   < P ≤   < P ≤ 

 < P ≤   < P ≤   < P ≤   < P ≤   < P ≤   < P ≤   < P ≤ 

17

Power density (W/m) at  m  < P ≤   < P ≤   < P ≤   < P ≤   < P ≤   < P ≤   < P ≤ 

good (7.5 m/s, ≈500–600 W/m2) very good (8.5 m/s, ≈700–800 W/m2)

According to the above classification, the city of Chabahar is not a suitable location for installation of wind turbines. Meanwhile, if we use the following classifications, the city of Chabahar falls in “fairly good” category. This classification is also expressed based on wind power density (Mostafaeipour et al. 2011): fair (P < 100 W/m2) fairly good (100 < P < 300 W/m2) good (300 < P < 700 W/m2) very good (P > 700 W/m2)

Figure 20 illustrates that there are many rapid changes in this parameter in different months, in a way that maximum value of wind power density is about 3.8 times greater than its minimum value. Considering these changes is important for evaluation and design of the projects (Keyhani et al. 2010). According to the above classifications, wind energy potential is limited in Chabahar, and its potential is more suitable for low-capacity turbines which would be used in villages and small business sectors. Thus, in terms of turbine performance, using the low-capacity turbines is better option (Mostafaeipour et al. 2014).

Figure 20: Monthly power density for Chabahar at three heights of 10, 30, and 40 m.

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18

O. Alavi et al.: Feasibility of a Wind-Hydrogen Energy System

4.12 Wind Turbine Energy Production Eight turbines with the rated powers ranging from 2.5 kW to 8 MW are selected to be assessed in terms of their performance in the city of Chabahar. Table 10 presents the technical specifications of the nominated wind turbines.

The annual capacity factor and energy output for all wind turbines installed at predetermined heights are shown in Table 11. Table 11: Annual capacity factor and output energy for the examined wind turbines. Turbine

Table 10: Specification of the nominated wind turbines (ECIWAS Services 2017). Proven .

Turbine

Proven . Proven  Bergey Excel-R Enercon E- Vestas V Vestas V AWE – AWE – AWE – DeWind D. DeWind D.

Power

Hub height

Rotor diameter

. kW  kW . kW  kW  kW  MW  kW  kW  kW  MW  MW

 m  m  m  m

. m . m . m . m

Power regulation Stall Stall Pitch Pitch

control control control control

 m

 m

Pitch control

 m  m

 m  m

Pitch control Pitch control

 m

 m

Pitch control

 m

 m

Pitch control

 m  m

 m  m

Pitch control Pitch control

Windographer software was used to determine the produced power and capacity index. All wind turbines considered in this study have been designed for operations at different heights. In this study, it is assumed that the selected wind turbines are installed at heights shown in Table 10. Wind data related to different heights were synthesized (extrapolated) in order to accurately estimate the performance of the turbines at specified altitudes eq. (20). The wind turbines selected in this study have different control systems.

Wind Turbine

~

Power Controller

AC Load

Electrolyzer

Rated

Cut-out

Capacity

Hub

Eout

speed

speed

speed

factor (%)

height

(MWh)

(m/s)

(m/s)

(m/s)

(m)







.



.

Proven  Bergey Excel-R

 .

 .

 –

. .

 

. .

Enercon E-



.





.



Vestas V







.





Vestas V







.



,

AWE –







.



,

AWE –







.



,

AWE –







.



,

DeWind D. DeWind D.

 

 

 

. .

 

, ,

Based on Table 11, it is clear that the Proven 2.5 kW turbine model has the highest capacity factor which is 0.268 for the height of 11 m. If we consider the annual energy output as the primary criterion, the Vestas V164 8 MW turbine has the highest annual energy output. Annual energy output of this turbine installed at the height of 120 m is 12,018 MWh. Therefore, it can be concluded that Vestas V164 8 MW is the best choice in terms of electricity generation for producing hydrogen.

4.13 Hydrogen Production by Wind Turbine A combined wind-hydrogen system is considered for investigating hydrogen production from wind energy. The scheme of this system is shown in Figure 21. The considered system is comprised of a wind turbine, power controller, a rectifier for regulating AC voltage to DC, an alkaline electrolyzer supplied by a DC source, and a storage tank.

Auxiliary Supply

AC/DC Converter

_______________ _______________ _______________ _______________ _______________ _______________ _______________ _______________ _______________ _______________

Cut-in

Storage Figure 21: The considered wind-hydrogen energy conversion system.

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O. Alavi et al.: Feasibility of a Wind-Hydrogen Energy System

The output of wind turbines is AC voltage; thus, the rectifier will change this voltage to the required and smaller DC voltage for supplying water electrolysis. During applying DC voltage to the two electrodes, hydrogen production process is carried out. To estimate the amount of hydrogen production during this process, the following equation can be used (Gupta 2009): EWT × ηrec ELec





(31)



where H2 is the amount of hydrogen production (Nm3), EWT is the wind turbine output energy (kWh). ηrec and ELec are the rectifier efficiency and the electrolyzer energy consumption (kWh/Nm3), respectively. By converting Nm3 to ton-H2 (1 kg-H2 is equal to 11.13 Nm3-H2), the amount of hydrogen production from the examined turbines will be as Table 12, when a rectifier with 95 % efficiency and an electrolyzer with the energy consumption of 5 kWh/Nm3 were used in this analysis.



H2 =



5 Conclusion Detailed statistical study of wind at 10, 30 and 40 m heights for the city of Chabahar is presented. The most important outcomes of the study can be summarized as follows: – The wind speeds were analyzed using the Weibull function and monthly values of Weibull parameters k and c. All calculated values of parameter k varied between 2.209 and 2.868 and its annual mean value was equal to 2.397. Values of parameter c varied between 4.269 and 6.742 and its mean value in this study was 5.44 m/s. Table 12: Annual hydrogen produced for the considered wind turbines. Turbine

Proven . Proven  Bergey Excel-R Enercon E- Vestas V Vestas V AWE – AWE – AWE – DeWind D. DeWind D.

Hub height (m)

Amount of hydrogen produced (ton-H)

          

. . . . . . . . . . .



19

Monthly mean wind speed was calculated. Most of the changes in the speed varied from 4 to 6 m/s. Monthly mean wind speed at the heights of 10, 30 and 40 m were estimated. Results of this study suggest that the city of Chabahar is not suitable for industrial turbines. The potential is however suitable for low-capacity turbines used in rural areas and small industries. Wind direction which is an important factor in the calculations of wind power was obtained for this study. Wind direction at two heights of 30 and 37.5 m were calculated. Wind Rose diagram was also drawn and the most probable angle of wind was about 157.5°. The results of wind power and wind energy calculations were calculated. The results showed that the highest wind power density at the height of 10 meters was for the month of May and was equal to 196.67 W/ m2. Wind power density was at its lowest in the month of December and was equal to 51.12 W/m2. The annual mean wind power density was 107.55 W/m2. Eight different wind turbines were studied to further evaluate the performance of these turbines in the studied locations. With respect to the wind power potential of the region, it is better to use wind turbines with less nominal power. One of the important factors in wind farm design is the capacity factor. The higher capacity factor shows that the wind turbine is better suited to the wind regime, and represents a great performance for the installed wind turbine. Here, the 2.5kW Proven wind turbine performed better than other selections in terms of the capacity factor. However, the Vestas V164 was recognized as the best choice for power generation because of its high rated power. With regard to the proposed wind-hydrogen energy conversion system, the amount of hydrogen production can be determined by assuming the values for the electrolyzer energy consumption and the utilized rectifier efficiency. The highest value of hydrogen production (194.36 ton-H2) using the considered energy conversion system was related to the Vestas V164.

References Aiche-Hamane, L., M. Belhamel, B. Benyoucef, and M. Hamane. 2009. “Feasibility Study of Hydrogen Production from Wind Power in the Region of Ghardaia.” International Journal of Hydrogen Energy 34:4947–4952.

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Akyuz, E., Z. Oktay, and I. Dincer. 2012. “Performance Investigation of Hydrogen Production from a Hybrid wind-PV System.” International Journal of Hydrogen Energy 37:16623–16630. Brower, M. 2012. Wind Resource Assessment: A Practical Guide to Developing A Wind Project. New York: John Wiley & Sons. Burton, T., N. Jenkins, D. Sharpe, and E. Bossanyi. 2011. Wind Energy Handbook. New York: John Wiley & Sons. Carta, J., P. Ramirez, and S. Velazquez. 2009. “A Review of Wind Speed Probability Distributions Used in Wind Energy Analysis: Case Studies in the Canary Islands.” Renewable & Sustainable Energy Reviews 13:933–955. Clúa, J.G.G., H.D. Battista, and R.J. Mantz. 2010. “Control of a GridAssisted Wind-Powered Hydrogen Production System.” International Journal of Hydrogen Energy 35:5786–5792. Dinpashoh, Y., R. Mirabbasi, D. Jhajharia, H.Z. Abianeh, and A. Mostafaeipour. 2014. “Effect of Short-Term and Long-Term Persistence on Identification of Temporal Trends.” Journal of Hydrologic Engineering 19:617–625. ECIWAS Services. 2017. [online]. Accessed October 26 2017. http:// www.eciwas.com/wind_products.php. Gevorkian, P. 2007. Sustainable Energy System Engineering: The Complete Green Building Design Resource. Oxford: McGraw Hill Professional. Global Wind Report. Global Wind Energy Council. 2017. [online]. Accessed October 26 2017. www.gwec.net. Greiner, C.J., M. Korpås, and A.T. Holen. 2007. “A Norwegian Case Study on the Production of Hydrogen from Wind Power.” International Journal of Hydrogen Energy 32:1500–1507. Gupta, R.B. 2009. Hydrogen Fuel: Production, Transport, and Storage. Boca Raton: Taylor & Francis Group. Gutiérrez-Martín, F., D. Confente, and I. Guerra. 2010. “Management of Variable Electricity Loads in Wind-Hydrogen Systems: The Case of a Spanish Wind Farm.” International Journal of Hydrogen Energy 35:7329–7336. IEAWIND. 2015. [online]. Accessed October 26 2017. http://www.iea wind.org/annual_reports.html. Jangamshetti, S.H., and V.G. Rau. 2001. “Optimum Siting of Wind Turbine Generators.” IEEE Transactions on Energy Conversion 16:8–13. Keyhani, A., M. Ghasemi-Varnamkhasti, M. Khanali, and R. Abbaszadeh. 2010. “An Assessment of Wind Energy Potential as a Power Generation Source in the Capital of Iran, Tehran.” Energy 35:188–201. Khorasanizadeh, H., K. Mohammadi, and A. Mostafaeipour. 2014. “Establishing a Diffuse Solar Radiation Model for Determining the Optimum Tilt Angle of Solar Surfaces in Tabass, Iran.” Energy Conversion and Management 78:805–814. Loisel, R., L. Baranger, N. Chemouri, S. Spinu, and S. Pardo. 2015. “Economic Evaluation of Hybrid Offshore Wind Power and Hydrogen Storage System.” International Journal of Hydrogen Energy 40:6727–6739. Metz, B., O. Davidson, R. Swart, and J. Pan. 2001. Climate Change 2001: Mitigation. Contribution of Working Group III to the Third Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge and New York: Cambridge University Press. Ministry of Energy. 2017. [online]. Accessed October 26 2017. http:// isn.moe.gov.ir.

Mirhosseini, M., F. Sharifi, and A. Sedaghat. 2011. “Assessing the Wind Energy Potential Locations in Province of Semnan in Iran.” Renewable & Sustainable Energy Reviews 15:449–459. Mohammadi, K., and A. Mostafaeipour. 2013. “Economic Feasibility of Developing Wind Turbines in Aligoodarz, Iran.” Energy Conversion and Management 76:645–653. Mohammadi, K., A. Mostafaeipour, and M. Sabzpooshani. 2014. “Assessment of Solar and Wind Energy Potentials for Three Free Economic and Industrial Zones of Iran.” Energy 67:117–128. Mostafaeipour, A. 2010. “Historical Background, Productivity and Technical Issues of Qanats.” Water History 2:61–80. Mostafaeipour, A., M. Jadidi, K. Mohammadi, and A. Sedaghat. 2014. “An Analysis of Wind Energy Potential and Economic Evaluation in Zahedan, Iran.” Renewable & Sustainable Energy Reviews 30:641–650. Mostafaeipour, A., M. Khayyami, A. Sedaghat, K. Mohammadi, S. Shamshirband, M.A. Sehati, and E. Gorakifard. 2016. “Evaluating the Wind Energy Potential for Hydrogen Production: A Case Study.” International Journal of Hydrogen Energy 41:6200–6210. Mostafaeipour, A., A. Sedaghat, A.A. Dehghan-Niri, and V. Kalantar. 2011. “Wind Energy Feasibility Study for City of Shahrbabak in Iran.” Renewable & Sustainable Energy Reviews 15:2545–2556. Mostafaeipour, A., A. Sedaghat, M. Ghalishooyan, Y. Dinpashoh, M. Mirhosseini, M. Sefid, and M. Pour-Rezaei. 2013. “Evaluation of Wind Energy Potential as a Power Generation Source for Electricity Production in Binalood, Iran.” Renewable Energy 52:222–229. Olateju, B., A. Kumar, and M. Secanell. 2016. “A Techno-Economic Assessment of Large Scale Wind Hydrogen Production with Energy Storage in Western Canada.” International Journal of Hydrogen Energy 41:8755–8776. Parissis, O.S., E. Zoulias, E. Stamatakis, K. Sioulas, L. Alves, R. Martins, A. Tsikalakis, N. Hatziargyriou, G. Caralis, and A. Zervos. 2011. “Integration of Wind and Hydrogen Technologies in the Power System of Corvo Island, Azores: A Cost-Benefit Analysis.” International Journal of Hydrogen Energy 36:8143–8151. Re-energy. 2017. [online]. Accessed October 26 2017. http://www.reenergy.ca. Rodríguez, C.R., M. Riso, G. Jiménez Yob, R. Ottogalli, R. Santa Cruz, S. Aisa, G. Jeandrevin, and E.P.M. Leiva. 2010. “Analysis of the Potential for Hydrogen Production in the Province of Córdoba, Argentina, from Wind Resources.” International Journal of Hydrogen Energy 35:5952–5956. Saeidi, D., and M. Mirhosseini. 2011. “Feasibility Study of Wind Energy Potential in Two Provinces of Iran: North and South Khorasan.” Renewable & Sustainable Energy Reviews 15:3558–3569. Saeidi, D., A. Sedaghat, P. Alamdari, and A.A. Alemrajabi. 2013. “Aerodynamic Design and Economical Evaluation of Site Specific Small Vertical Axis Wind Turbines.” Applied Energy 101:765–775. Sathyajith, M. 2006. Wind Energy: Fundamentals, Resource Analysis and Economics. Berlin: Springer Science & Business Media. Sedaghat, A., M.E.H. Assad, and M. Gaith. 2014. “Aerodynamics Performance of Continuously Variable Speed Horizontal Axis Wind Turbine with Optimal Blades.” Energy 77:752–759.

Authenticated | [email protected] author's copy Download Date | 12/6/17 6:53 AM

O. Alavi et al.: Feasibility of a Wind-Hydrogen Energy System

Shaahid, S., and I. El-Amin. 2007. “Dissemination of Off-Grid Hybrid Wind-Diesel-Battery Power Systems for Electrification of Isolated Settlements of Hot Regions.” International Journal of Sustainable Energy 26:91–105. Sigal, A., E.P.M. Leiva, and C.R. Rodríguez. 2014. “Assessment of the Potential for Hydrogen Production from Renewable Resources in Argentina.” International Journal of Hydrogen Energy 39:8204–8214. Sistan and Baluchestan Province Web Portal. 2017. [online]. Accessed October 26 2017. http://www.sbportal.ir. Ucar, A., and F. Balo. 2009. “Evaluation of Wind Energy Potential and Electricity Generation at Six Locations in Turkey.” Applied Energy 86:1864–1872. Ulleberg, O., T. Nakken, and A. Eté. 2010. “The Wind/Hydrogen Demonstration System at Utsira in Norway: Evaluation of System Performance Using Operational Data and Updated Hydrogen Energy System Modeling Tools.” International Journal of Hydrogen Energy 35:1841–1852.

21

Wadhwa, C.L. 1989. Generation, Distribution and Utilization of Electrical Energy. New Delhi: New Age International Publishers. Weidong, G., and Y. Zhuoyong. 2012. “Research on Non-GridConnected Wind Power/Water-Electrolytic Hydrogen Production System.” International Journal of Hydrogen Energy 37:737–740. Wikipedia – Chabahar 2017 [online]. Accessed October 26 2017. http://en.wikipedia.org/wiki/Chabahar. Wikipedia - Wind turbine. 2017. [online]. Accessed October 26 2017. http://en.wikipedia.org/wiki/Wind_turbine. Wind Energy Center. 2013. “Wind Power: Capacity Factor, Intermittency, and What Happens When the Wind Doesn’t Blow?” Technical report, Wind Energy Center, University of Massachusetts Amherst. World Wind Energy Association. 2015. [online]. Accessed October 26 2017. http://www.wwindea.org. Zhou, T., and B. Francois. 2009. “Modeling and Control Design of Hydrogen Production Process for an Active Hydrogen/Wind Hybrid Power System.” International Journal of Hydrogen Energy 34:21–30.

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