Turbine-Site Matching For Reliable Wind Power Application in Iraq

Turbine-Site Matching For Reliable Wind Power Application in Iraq

Abstract

Matching between site and wind turbine characteristics for three selected sites in Iraq was conducted and revealed. The analysis was based on wind velocities data that were recorded for a period of five successive years at sites located in Baghdad, Nasiriyah, and Basrah cities. The objective was to address a candidate from the available sites for potential wind energy applications. MATLAB model based on the measured data, Weibull distribution function, and wind turbines characteristics was developed and employed for siting analysis of wind turbines. Unlike Baghdad and Nasiriyah, Basrah site ranked class (5-Excellent) of the international system of wind classification. It was concluded that there are several wind turbines of specific hub heights can match the wind power densities of this site. Capacity factor and the possible energy capture for a set of selected wind turbines were computed. Among the wind turbine models tested, results showed that General Electric GE 1.6-100 wind turbine model is the most suitable model based on capacity factor. Additionally, Nordex N90 Beta wind turbine model is the best-matched wind turbine based on energy capture. This study can assist in the planning and development of large-scale wind power generation sites in Iraq.

Keywords: wind energy potential, Weibull parameters, capacity factor, and wind turbine performance.

1. Introduction

The successive depletion of conventional fuel resources in addition to the growing energy demand due to the growth of world population requires us to move to renewable and environmental-friendly alternatives like solar and wind.  This move could greatly mitigate the consequences of greenhouse gases emission and the growing concern of global warming. In this regard, wind power harvesting is considered a dependable technology that can effectively reduce the environmental pollution, conventional fuel consumption, and overall costs of electricity generation. In Iraq, securing electricity for all community groups stills a challenge. The current dependence on conventional-fuel power plants to meet the growing power demand approved to be unreliable solution. Thus, the move to increase the share of renewable-based electricity is highly recommended. Renewable energy systems including solar power plants and wind farms could play a key role to cover the present-day deficit of electric power generation. Globally, wind energy projects are noticeably growing as reliable fossil-fuel alternative technologies. Many studies have been conducted around the world to assess and analyze wind energy potential in order to suitably select candidate sites for wind energy applications.

Asl et al. [1] investigated wind energy potential at five sites in Kurdistan-Iran. The monthly and annual parameters were estimated. Then after, a rank of each site was presented. Fazelpour et al. [2] reported on the wind energy potential for Ardabil city-Iran using a long-term data source and concluded that the site is suitable to wind plant installation. Bekele and Palm [3] investigated the wind energy potential for four sites in Ethiopia using HOMER software. It was found that three sites of them were practical for wind energy application. Fyrippis et al. [4] presented the wind power potential of Naxos Island, Greece. It was found that Weibull distribution fitted to actual data better than Rayleigh distribution. The results showed that the selected site has class 7 of wind power classification. Oyedepo et al. [5] pointed out that wind speed characteristics and wind turbine characteristics are recommended for matching sites to wind energy potential in Nigeria. Rasham [6] assessed the annual wind energy potential at three selected sites in Iraq. Namely were: Baghdad, Najaf and Kut Al-Hai. Wind class for each site based the international wind classification system was presented and discussed.

Capacity factor of wind turbines was commonly used for pairing candidate sites with wind turbines in many studies such as [7-13]. The aim was to suitably identify wind turbines for the candidate sites. The models of capacity factors used in previous studies were: linear model, quadratic model, cubic model, and general model. Sathyajith [14] matched between the candidate sites and wind turbines via the capacity factor. The wind turbine of capacity factor ranged

(0.2-0.4)considered practically effective, and

≥0.4considered very efficiently interacting. Abul’Wafa [15] used turbine performance index in coincidence with minimum deviation ratio between rotor-rated speed and optimal speed for generating higher capacity factor with a minimum cost of energy. El-Shimy [16] developed a formulation of the capacity factor for site-wind turbines matching and validated it via long-term performance measurements at Zafarana wind farms-Egypt.

Chang et al. [17] presented suitability matching between the wind site and pitch-controlled wind turbine of quadratic model based on the Weibull probability distribution function.  The same authors [9] estimated the capacity factors of linear, quadratic, cubic, and general models of power curve models for pitch-controlled wind turbines. The validity of these models was checked and the quadratic model showed the best agreement with manufacturers’ power curves. Chermitti et al. [8] used the capacity factor of wind turbines in addition to the site effectiveness approach to assess wind potential and identify the suitable wind energy applications for the sites. Al Jowder [13] investigated the wind power potential for the Kingdom of Bahrain. The matching between the wind sites and wind turbines was achieved by estimating the capacity factor of wind turbines. Ucar et al. [10-12] analyzed the wind characteristics, wind energy potential, and wind turbines characteristics of different sites in Turkey. The best wind turbine was identified via capacity factor and energy output of wind turbines. Ayodele et al. [18] investigated 20 commercially wind turbines of different specifications via estimating the capacity factor of wind turbines. The sites of the highest and lowest capacity factor were presented according to wind power potential. Albadi and El-Saadany [19] presentedand verified a new formulation of linear, quadratic, and cubic models for the capacity factor of any pitch-regulated wind turbines.

Up to the authors’ knowledge, site-turbine matching for wind energy applications in Iraq has not been extensively investigated yet. An early study by Darwish and Sayigh [20] concluded that wind farms are feasible as one sixth of Iraqi lands enjoys annual wind speed greater than 5 m/s. Kazem and Chaichan [21] recommended investigating offshore of the Gulf near Basrah as one of potential candidates for wind power generation.   In the present study, three different sites: Baghdad (23.26N, 44.23E), Nasiriyah (31.02N, 46.23E), and Basrah (30.37N, 48.25E) were selected to investigate the wind power potential at hub heights of (60m, 80m, and 100m).  Six commercially-available wind turbine models were investigated and analyzed. The most-matched model was determined via estimating the capacity factor of each wind turbine at the selected sites. The study is the first of its kind regarding the field of wind energy applications in Iraq. It can be used to help policy makers to inspire further investments in renewable energy utilization.

2. Site information and data sources

In the aim to identify potentially suitable areas for installation of wind farms or small stand-alone applications in Iraq, three selected sites were considered. These sites namely are: Baghdad (23.26N, 44.23E), Nasiriyah (31.02N, 46.23E), and Basrah (30.37N, 48.25E).  Baghdad is the capital of Iraq. It is located along Tigris River at an elevation of 35 meters above the sea level. The climate is hot and dry in summer, cool and wet in winter. Nasiriyah is a city in the south-east of Iraq on the Euphrates River. It is located at an elevation of 5 meters above the sea level with a desert climate. Basrah is located in southern Iraq, bordering Kuwait to the south and Iran to the east on the West Bank of Shatt al-Arab. Basrah has a hot desert climate due to its location near the coast. It is located at elevation 3 meters above sea level.

Usually, wind sites with an annual average wind speed of (≥ 5 m⁄s) are regarded feasible candidates for wind farm installation. In this study, wind speed data for five successive years (2011-2015) for the aforementioned sites were collected from the metrological weather website [22]. The monthly average, annual average and overall average of the five years collected data were estimated at the stations elevation. Then, an extrapolation of the stations elevation and wind velocities was performed to estimate wind velocities at specified hub heights via wind shear power law. This law was employed to estimate the shape factor, scale factor, wind power density, and wind energy potential at the hub heights of the selected wind turbine models.

3. Theoretical Analysis

Weibull distribution is the most commonly used distribution in wind energy analysis. The other distribution i.e. Rayleigh distribution is a special case of Weibull distribution when the shape factor equal to (2). Weibull probability distribution function and Weibull cumulative distribution were used to characterize wind profile of the sites in this present study. The standard deviation method of Weibull distribution was used to calculate the shape and scale factors at stations elevation. This method poses relatively low difference between the measured and estimated Weibull parameters via estimating the determination factor and root mean square error. At the new selected heights i.e. hub heights of selected wind turbines, the wind velocity, air density, shape factor, scale factor, wind power density, and wind energy density were all corrected and estimated. The matching between the effective wind turbines and wind sites of higher wind energy potential was achieved by estimating the capacity factor of the wind turbine based on the wind site and wind turbine characteristics.

1.  Wind Energy Analysis

Like solar energy, wind is considered stochastic quantity randomly varies with time. Thus, the average value of wind velocity needs to be used in the wind energy analysis. The average for both measured wind velocities and Weibull estimated wind velocities were calculated as [14]:

V̅=∑i=1NVi3/N13

(1)

Here

(Vi)is the individual wind velocity and

(N)is the number of collected data.

Deviation of individual wind velocities from the average wind velocity is called the standard deviation (σ) [14]:

σ= ∑i=1NVi-V̅2/N

(2)

Air density is affected by the values of pressure and temperature at altitudes above the sea level. The expression that commonly used for air density correction in cases when variations in temperature, pressure and elevation exist is:

ρ=ρo-[1.194×10-4×H]

(3)

Here

(ρo=1.225 kg/m3) represents the air standard density at

(T=15℃, P=1 atm), and

(H )is wind station elevation or hub height of the examined wind turbines.

The shape and scale of Weibull distribution is characterized by the Weibull shape and scale factors. At the stations elevation of the selected sites, the shape factor

(K1)and scale factor

(C1)of Weibull distribution can be expressed as  [14]:

K1=σ/V̅-1.090

(4)

C1=V̅K12.6674/0.184+0.816 K12.73855

(5)

Extrapolation of wind velocities with height to estimate the wind velocity at new heights can be calculated via wind shear law as [23]:

V2=V1H2/H1α

(6)

Where:

V1

is the wind velocity at stations elevation

H1, V2is the wind velocity at selected heights

H2,and

αis the wind shear power exponent.

The wind shear power exponent (α) depends on the roughness height of the terrain. It can be formulated as [24]:

α=[0.096 log10Zo+0.016 log10Zo2+0.24]

(7)

Here

Zois ground roughness height.

The variation of the Weibull shape and scale parameters with height at new selected heights can be evaluated as [1]:

K2=K11-0.0881 lnH2/H1-1

(8)

n=0.37-0.0881ln⁡C1

(9)

C2= C1H2/H1n

(10)

Here

(K2)  is the corrected shape factor at newly selected height,

(C2)is the corrected scale factor at newly selected height, and

(n)is an exponent.

The Weibull probability density function f(V) and the cumulative distribution function F(V) at the stations elevation or new selected heights can be defined as:

fV=K/CV/CK-1e-(V/C)K

(11)

FV=∫0∞fV dV=1-e-(V/C)K

(12)

Where

(K)is the shape factor at stations elevation or at new heights (60 m, 80 m, 100 m), and

(C)is the scale factor at stations elevation or at new heights (60 m, 80 m, 100 m).

According to Weibull distribution, the average wind speed and its standard deviation at stations elevations or at selected heights can be estimated as:

V̅=C Γ 1+1/K

(13)

σ=C Γ 1+2/K-Γ21+1/K1/2

(14)

Here

Γ  is gamma function, which is defined as:

Γ(x)=∫0∞t(x-1)e-tdt

(15)

Where t is a parameter equal to

V/CK,V is the wind velocity, and

(x)is a positive number.

The individual measured wind power density

(PDmi)and wind energy density

(EDmi)at any height can be calculated as [2]:

PDmi=1/2 ρ V̅i3

(16)

EDmi=PDmi×T

(17)

Here

Tis the time factor that can be considered as the hour’s number for the monthly and annual durations.

Accordingly, the total average of measured wind power density

(PDmt)and wind energy density

(EDmt)can be written as [2, 6]:

PDmt=(( ∑i=1N1/2 ρ V̅i3)/N)

(18)

EDmt=PDmt ×T

(19)

In Weibull distribution analysis, the Weibull estimated wind power density

(PDEW)and wind energy density

(EDEW)at any height can be expressed as:

PDEW=1/2 ρ C3Γ1+3/K

(20)

EDEW=1/2 ρ C3Γ1+3/K T

(21)

The accuracy of Weibull distribution performance in estimating the sites actual parameters with the predicted Weibull results can be check via the determination factor (

R2)and the root mean square error (

RMSE)as [25]:

R2=∑i=1Nyi-zi2-∑i=1Nxi-yi2/∑i=1Nyi-zi2

(22)

RMSE=∑i=1Nyi-xi2/N12

(23)

Here

(yi),

(xi), and

(zi)is the actual data, predicted Weibull results, and mean of actual data respectively.

2.  Wind Turbines Analysis

Six models of wind turbines having different characteristics for cut-in speed, rated speed, furl speed and rated output power were examined in the present study. The aim was to conduct reliable matching between the wind sites and wind turbines by estimating the capacity factor and energy captured of the tested turbines.

The rated electrical output power of wind turbine can be estimated as [13]

PR=1/2 ρ A CP ηmech ηel VR3

(24)

Here

(ρ)is the air density;

(A)is the rotor swept area;

(CP)is the power coefficient of wind turbine rotor;

(ηmech)is the efficiency of the mechanical system;

(ηel)is the efficiency of electrical system; and

(VR)is the rated wind speed.

The power which is produced by wind energy conversion system at non-rated region (i.e. from cut-in wind speed to rated wind speed) and rated region (i.e. from rated wind speed to furl wind speed) can be used to estimate the average output power as [13]:

Pav=∫0∞PV fV dV

(25)

Here

(PV)is the output power of wind turbine as a function of wind velocity; and

fVis the probability density function.

Then, the average output power

(Pav)of wind turbine for non-rated region and rated region can be formulated as

Pav=0,                                                       Vf<V<Vc1/2 ρ A CP ηmech ηel V3,          Vc≤V<Vr 1/2 ρ A CP ηmech ηel VR3,         VR≤V≤Vf

(26)

Here

(Vc)is the cut in wind speed,

(VR)is the rated wind speed, and

(Vf)is the furl wind speed.

The main parameter to assess the efficient wind turbines in the candidate wind site is the capacity factor. The capacity factor

CFof wind turbine represents the ratio of the average output power to the rated output power.

CF=(Pav/PR)

(27)

Integrating Eq. (26) over time gives the capacity factor of wind turbine. This can be formulated as:

CF=1VR3∫VcVRV3 fV dVnon rated region+∫VRVffV dVrated region

(28)

The analytical derivation of the Eq. (28) can be written as [13],

CF=VcVR3 e-Vc/CK-e-Vf/CK+3Γ3/KKVR/C3γVRCK, 3K-γVcCK, 3K

(29)

Here

(γ)is incomplete gamma function and it estimated as

γu,a=1Γa∫0uxa-1 e-x dx

(30)

Where:

u=VR/CK, and

a=3/K

The average energy production of wind turbine is usually accounted for as [18]

AEP=CF PR T

(31)

Here (T) is the time interval under consideration (in hours). For example,

(T=8760 h)for the annual production.

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