Technical and Economic Assessment of Wind Farm Power Generation at Adrar in Southern Algeria

Energy Procedia 42 ( 2013 ) 53 – 62

1876-6102 © 2013 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of KES Internationaldoi: 10.1016/j.egypro.2013.11.005

ScienceDirect

The Mediterranean Green Energy Forum 2013, MGEF-2013

Technical and economic assessment of wind farm power generation at Adrar in Southern Algeria

Said Diafa,*, Gilles Nottonb, Djamila Diafa aCentre de Développement des Energies Renouvelables, BP 62, 16340 Bouzareah, Algiers, Algeria

bUniversity of Corsica Pasquale Paoli, UMR CNRS 6134, Centre Georges Peri, Route des Sanguinaires, 20000 Ajaccio, France

Abstract

A potential and economic analysis of a 10 MW wind farm at Adrar in the southern region of Algeria is realized for various WT types. Adrar has a good wind potential: and the most adapted wind turbine is the Vestas V90/2MW with a annual energy output equal to 40902 MWh. According to the studied machine, the capacity factor varies from 36% for Nordex N80/2500 and 47% for Vestas V90/2MW with respectively a cost per kWh of electricity generated of 0.0525 $/kWh and 0.0408$/kWh. According to our results, the wind resource appears to be suitable for power production in the site of Adrar, which makes it a viable substitute to diesel oil for electricity generation. © 2010 Published by Elsevier Ltd. Keywords: Wind energy; Weibull distributions; wind power; capacity factor; unit cost of electricity.

1. Introduction

Energy is one of the essential inputs for economic development. The renewable energy resources such as the wind and solar are the most efficient option for making electricity available to the billions of people that presently do not have access to it. They offer many environmental and economical benefits in contrast to conventional energy sources. The Algerian Government has been promoting the use of renewable energy by means of a series of laws and official programs. In Algeria, the electricity production is essentially based on fossil fuels in particular, natural gas which is abundant in the country. Since 2010, Algeria is implementing an ambitious strategy for encouraging and developing renewable energy in its territory. This strategy would gradually replace the use of fossil fuels (natural gas and oil) which currently are the main resource for the country’s electricity generation, by other energy sources

* * Corresponding author. Tel.: +213 21 90 15 03; fax: +213 21 90 16 54. E-mail address: [email protected].

Available online at www.sciencedirect.com

© 2013 The Authors. Published by Elsevier Ltd. Selection and peer-review under responsibility of KES International

54 Said Diaf et al. / Energy Procedia 42 ( 2013 ) 53 – 62

like solar and wind energy. The geographical location of Algeria has several advantages for extensive use of most of the renewable resources. In this regard, Algeria has to make use of its renewable resources, such as wind solar and geothermal, not only to meet the increasing energy demand but also for environmental reasons.

In Algeria, the renewable energy sources have been promoted since the application of the plan for the Promotion of Renewable Energies, approved by the government on 03 February 2011, that set an initial target of generating 40 % of total energy consumption from renewable sources by 2030 [1].

In this context, many renewable energy projects will be developed and realized to achieve this objective. One among these projects is the use of wind turbines to generate electricity.

In this study, the wind energy potential in the site of Adrar is investigated. In addition, the performances of wind farm 10 MW installed capacity, using different wind turbine models, located in this site were examined.

2. Wind speed data and site description

The wilaya of Adrar is situated in the south of Algeria at 27° 49’ N and 00°17’W with an elevation of 263 m above sea level and it is a mainly agricultural zone (Fig. 1). This area is characterized by its traditional system of irrigation the "Foggara", by a hot desert climate and frequent sand storms. The hottest month is July (mean minimum temperature 26.8°C, mean maximum temperature 44.9°C) and the coldest is January (mean minimum temperature 4.5°C, mean maximum temperature 20.3°C) [2].

This study is based on a data source measured at a height of 10 m above ground level. The wind data for this meteorological station were obtained from the Algerian Meteorological National Office and were collected during the period 1977-1988 [3].

Fig. 1. Position of the Meteorological site and the solar measuring station

3. Wind characteristics

3.1. Wind speed frequency distribution

In order to describe the wind speed frequency distribution, there are several probability density functions [4-6]. The Weibull distribution technique is widely accepted and used to estimate a site’s probability distribution of wind speeds. It was found to be the preferred method for describing wind speed frequency distribution at a given site; because it usually provides the best fit of measured wind data [7-8].

The Weibull probability density function is expressed by [4, 9-11]:

Said Diaf et al. / Energy Procedia 42 ( 2013 ) 53 – 62 55

k1kw c/vexpc/vc/k)v(f

)1(

Where v is the wind speed, k is a dimensionless Weibull shape parameter, which is a measurement of the width of the distribution, and c is a Weibull scale parameter which is closely related to the mean wind speed. The two parameters of Weibull can be determined by the mean wind speed-standard deviation method using the following equations [12-13]:

086.1vk 10k1 (2)

k/11vc

)3(

Where v is the mean wind speed and is the standard deviation calculated by [13]:

n

1iiv

n1v )4(

5.02N

1ii )vv(

1N1

(5)

Where N is the data number

3.2. Wind speed variation with height

Wind speed increases with height. Since wind speed data used in this study were obtained at a height of 10 m and most modern wind turbines have hub heights considerably higher, the measured wind speed must be extrapolated to the wind turbine hub height. In this study, the power law is applied for this objective, as shown in the following equation [8, 14]:

oo h/hv/v

(6)

where v is the wind speed at the required height h , ov is the wind speed measured at the reference height oh and is the surface roughness coefficient and is assumed to be 0.143 (or 1/7) in most cases. The surface roughness coefficient can also be determined from the following expression [15-16]:

)10/hln(088.01/)vln(088.037.0 oo

(7)

Alternatively, the Weibull probability density function can be used to obtain the extrapolated values of wind speed at different heights. This approach is used in this study.

The Weibull parameters can be evaluated at any desired height, h, based on that at measurement height by the following equations [15-16]:

noo h/hc)h(c

(8)

)10/hln(088.01/)10/hln(088.01k)h(k oo (9)

Where co and ko are the scale factor and shape parameter respectively at the measurement height, ho, and h is the hub height. The exponent n is defined as:

)10/hln(088.01/)cln(088.037.0n oo

(10)


3.3. Wind power density

The power of the wind that flows at speed v through a blade sweep area A, can be expressed by the following equation:

3v5.0)v(P (11)

Where is the air density (usually taken as equal to 1.225 kg/m3 for a temperature of 15°C and a standard pressure of 1013 mb).

Besides, the estimation of the wind power available at a given site can be done by two different ways. Firstly, Based on the wind speed data and their distribution, the mean wind power density can be expressed as follows [17]:

j

1ii

3i fxvρ5.0P (12)

Where vi is the median wind speed of the i-th class, fi is the frequency of occurrence of winds in the i-th class and j is the number of wind speed classes. In terms of the Weibull parameters k and c , the mean wind power density may be also calculated by the following equation [17]:

k/31c5.0P 3 (13)

4. Calculation of power and energy output for wind turbine

Each wind generator has a different power output curve. Therefore, to simulate the electrical power of a wind generator, various models are used. Some authors [8, 18-19] assume that the WT power curve has a linear, quadratic or cubic form. Other authors [17, 20] approximate the power curve with a piecewise linear function with a few nodes. In other case studies, a model which has a similar form is applied taking into account the Weibull parameters [21]. Previous models are often used for the simulation of wind power output of a wind generator. Some manufacturers provide a power curve in a tabular form for their wind turbines machines. However, in order to determine the power generated by the wind turbine when the wind speed is between two points j and j+1, an approximation method is necessary.

Here, the WT output power is estimated through interpolation of the data values provided by the manufacturers. As the power curves are quite smooth, we use a cubic spline interpolation [22].

The fitting equation of the output characteristic of wind generator can be expressed as

corr

r1njj2

j3

j

21222

23

2

1ci112

13

1

coci

wg

vvvforPvvvfordvcvbva

.........vvvfordvcvbvavvvfordvcvbva

vvandvvfor0

)v(P (14)

Where Pwg(v) is theWT power output at wind speed v, vci is the cut-in speed, vco is the cut-out speed vr is the rated speed and Pr is the rated power. The parameters a, b, c and d are the polynomial coefficients of the cubic spline interpolation functions, depending on the wind turbine model, and j is the number of cubic spline interpolation functions corresponding to j+1 value couples (speed, power) of data provided by the manufacturers.

The wind energy can be determined by the product of the power delivered by the wind generator at the wind speed, v, and the time, t for v prevails at the investigated site.


The total energy generated by the wind generator over a period is calculated by adding up the energy output corresponding to all possible wind speeds. In this study, the energy output from a wind turbine was obtained with the following equation using the hourly mean wind speeds.

t)v(PEn

1iwgwg

)15(

Where n is the number of hours in the period of the considered time, t is one hour time duration (in this study, n=8760 and t=1). The capacity factor, fC , is one of the important indicators for assessing the field performance of wind turbine. It is defined as the ratio of the energy actually produced by the system to the energy that could have been produced by it, if the machine would have operated at its rated power throughout the time period. Annual value of the capacity factor can be calculated as:

ratedwgf E/EC

)16(

5. Cost Analysis

Since the economic viability of the wind energy projects depends on its ability to generate electricity at a low operating cost per unit energy, accurate estimation of all the costs, involved in producing electricity over the life span of the system, is essential. Different methods are generally used to estimate the operating cost of a unit energy produced by the wind energy conversion system [23-25].

The levelised cost of electricity (LCOE) [23] is one of the most commonly used methods. The LCOE of the wind energy conversion system can be described as the ratio of the total annualized cost of the wind energy conversion system to the annual electricity produced by this system [23].

The determination of the unit cost of energy involves two mains steps: In the first step, by taking into considerations the initial investment cost of the system and the present value of operations and maintenance (O&M) cost throughout the lifetime system, the present value of costs (PVC) is calculated; the second step consists to determine the unit cost of energy (per kWh).

5.1. The present value of costs

The present value of costs (PVC) can be calculated as follows:

)p(omCICPVC (17)

Where IC is the initial investment cost, )p(omC is the present value of operation and maintenance costs for the system life

The IC initial investment cost consists of the wind turbine cost and all other initial costs including the costs of civil work, installation, connection cables to the grid and power conditioning. The cost of the wind turbine can be determined as follows:

rspewt PCC (18)

Where speC and rP are, respectively, the specific cost and rated power of the wind turbine. The present value of O&M costs (20 years of maintenance) is expressed as [6, 25]:

id)d1/()i1(1)id/()i1(CC Toma)p(om

(19)

Where i , d and T are the inflation rate for operation, interest rate and useful lifetime of turbine in years (20 years), respectively.

omaC is the cost of operation and maintenance for the first year. This cost is expressed as a fraction of the component cost. In this study, it is assumed to be 15% of the annual cost of the turbine (machine price/life time) [7, 24].


5.2. The Levelised cost of energy

The unit cost of energy (LCOE): is determined by:

wgtE/TACLCOE kWh/$ (20) Where TAC and wgtE represent, respectively, the total annualized cost and the annual total energy.

The TAC is calculated using the present value of costs ( PVC) and the capital recovery factor )CRF( :

CRF.PVCTAC (21)

For a given discount rate d and useful system lifetime T, the capital recovery factor is defined as [6] :

1)d1()d1(dCRF TT (22)

6. Results and discussion

The present study is based on a data source measured at a height of 10 m above ground level collected during the period 1976-1988 and obtained from the Algerian Meteorological National Office [3].

6.1. Wind characteristics

The monthly variations of mean wind speed at a height of 10 m for Adrar are shown in Fig. 2. The monthly mean wind speed has its highest values in March and July and lowest ones during winter. Adrar is windy during all the year with an average annual wind speed around 6 m/s at 10 m. The average annual evolution of 3-hourly wind speeds decreases during the night and increases during daytime; it is windier in the afternoon than in the morning. The wind blows at speeds higher than 5 m/s during all the day and higher than 6 m/s during almost 10 hours of the day (more than 40% of the time). Thus, this site have a good wind energy potential as the wind blows relatively at high speeds for long periods of time.

5.45.5

6.4

5.96.0

5.7

6.5

6.26.1

5.5

5.7

5.4

5

5.2

5.4

5.6

5.8

6

6.2

6.4

6.6

Ave

rage

Win

d Sp

eed

(m/s

)

Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Sep. Oct. Nov. Dec. 4.8

5.3

5.8

6.3

6.8

7.3

0 3 6 9 12 15 18 21Hours

Mea

n W

ind

Spee

d (m

/s)

Fig. 2. Mean monthly wind speed and Diurnal variation for the site of Adrar (at 10 m above ground)

The wind speeds at Adrar were analyzed using the Weibull probability density function. Fig. 3 shows the wind speed frequency histograms fitted by the Weibull frequency function. The Weibull distribution fits well the experimental distribution. On theses results and by considering a typical WT with a cut-in speed of 3 m/s and a rated speed of 13 m/s, we can conclude that : Only 12% of the wind speed was less than 3 m/s. Therefore, the WT site can produce energy for about

88% of the time with about 6% falling in the full power range (up to 13m/s). As the hub height exceeds 10 m (measured height), the wind turbines can operate for large wind speed intervals.


If a wind turbine system with design cut-in wind speed of 4 m/s is used in this site for electricity generation, this machine can generate energy for about 76% of the time respectively.

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28

Wind speed (m/s)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Freq

uenc

y (%)

Adrar

k = 2.15A = 7.2 Weibull

Data

Fig. 3. Wind speed Frequency with fitted Weibull distribution at 10 m height

Using Eq. (12), the mean wind power density is calculated. The mean wind power density is estimated from the Weibull parameters using Eq.(13) and the values of c and k respectively equal to 7.2 m/s et 2.15.

Table 1 shows the mean wind power densities calculated from the frequency of occurrence of speed classes and those obtained from the Weibull parameters for the site of Adrar.

Table 1. Annual mean wind power density at 10 m and 70 heights for the site of Adrar

Height (m) Wind power density (W/m2) From data From Weibull parameters

10 70

280

283 775

The results show that the estimation of the mean wind power density based on the Weibull parameters gives values very close to those calculated from the frequency of occurrence of speed classes. Thus, the Weibull parameters can be used for the evaluation of wind energy potential for this site.

The wind power density can be estimated, at different heights by using Eqs. (8, 9, 10, 13). Table 1 presents the wind power density at a height of 10 and 70 meters from the ground.

The results show that Adrar has an annual mean wind power density of 280 W/m2 and 775 W/m2 at 10 and 70 m. This site has a good potential for developing wind energy installation. We note that: There is an important potential for wind energy exploitation in the site of Adrar, When the hub height increases from 10 to 70 m, the available wind power density increases by 3.

6.2. Performance of selected wind turbines

The following analysis is to help designers and users to choose the most suitable wind turbine for the 10 MW wind farm which will be installed at Adrar. In this context, five commercial wind machines (Nordex N54/1000, Vergnet GEV HP1MW, Vestas V90/2MW, Nordex N80/2500 and Fuhrlander FL 2500) with different rated power were selected. The technical data of the selected wind turbine models is summarized in Table 2 [26-30] and the power curves shown in Fig.4.


Table 2. Technical data of different commercial wind turbines used in the analysis [26-30]

Turbine Type Cut-in speed

(m/s)

Cut-off speed

(m/s)

Rated speed

(m/s)

Rated power

(kW)

Hub height

(m)

Rotor diameter

(m)

N54/1000 3-4 25 14 1000 50. 60.70 54 GEV HP1MW 3 25 15 1000 70 62 V90/2MW 4 23 14 2000 95-105 90 FL 2500/90 3.5-4 25 13 2500 85-160 90 N80/2500 3 25 15 2500 60.70.80 80

0

500

1000

1500

2000

2500

0 5 10 15 20 25Wind Speed (m/s)

Pow

er (k

W)

Nordex N54/1000Vergnet GEVHP1MWVestas V90 2MWFuhrlander FL2500Nordex N80/2500

Fig. 4. Power curves for the selected wind turbines

The annual energy was obtained using the power curve provided by the manufacturer and the wind data of the site. Fig. 5 shows the annual energy output and capacity factor from the selected WT. The annual energy for the 10 MW farm ranges from 32 GWh for N80/2500 to 41 GWh for V90/2MW. The CF varies slightly according the WT between 36% (Nordex N80/2500) and 47% (Vestas V90/2 MW).

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

Nordex N54/1000 Vergnet GEVHP1MW

Vestas V90/2MW Fuhrlander FL2500/90

Nordex N80/2500

Year

ly E

nerg

y (M

Wh)

0.3

0.32

0.34

0.36

0.38

0.4

0.42

0.44

0.46

0.48

Cap

acity

Fac

tor

Yearly energy (MWh/year)Capacity factor

Fig. 5. Yearly energy and capacity factors for the 10 MW Wind Farm at Adrar

The CF at Adrar are greater than the recommended minimum value of 25% for all WT. This follows previous observation that Adrar location is excellent site for wind energy development. Based on the capacity factor of wind turbine models, the Vestas V90/2 MW model will be the best choice.


6.3. Energy cost analysis

The economic analysis was carried out to estimate the cost of the kWh produced by the 10 MW wind farm. The WT specific cost depends on the rated power and varies according to manufacturers. Thus, we consider a band interval (maximum and minimum values). Table 3 shows the specific cost of wind turbines for different size ranges [23, 24, 31]. The cost per kW decreases with the increase of the WT size. For WT above 200 kW, the WT cost can be taken as 1150 $/kW (average between the minimum of 700 $/kW and maximum of 1600 $/kW.

Table 3. Range of specific cost of wind turbines based on the rated power [23, 24, 31]

Wind turbine size (kW) Specific cost ($/kW) Average specific cost ($/kW)

<20 2200-3000 2600

20-200 1250-2300 1775

200> 700-1600 1150

The cost estimation has been done under the following assumptions [8]: • The O&M and repair cost is considered to be 15% of the WT annual cost (machine price/lifetime). • The interest rate (d) and inflation rate (i) were taken to be 8% and 6%, respectively. • The wind turbine lifetime (T) was assumed to be 20 years. • The other initial costs including civil work, installation, connection cables to the grid and power

conditioning are assumed to be 30% of the wind turbine cost. The results of cost analysis are presented in Table 4.

Table 5 : Cost analysis for wind farm 10 MW installed using different wind turbine models($/kWh).

Turbine model Yearly energy (MWh) Capacity factor Cost per unit ($/kWh)

Nordex N54/1000 32714.4 0.3735 0.0510

Vergnet GEV HP1MW 40022.9 0.4569 0.0417

Vestas V90/2MW 40902.3 0.4669 0.0408

Fuhrlander FL 2500/90 37719.3 0.4306 0.0442

Nordex N80/2500 31771.6 0.3627 0.0525

According to the cost analysis it is seen that:

• The cost per kWh depends on the WT and varies between 0.0408 $/kWh and 0.0525 $/kWh. • The minimum and maximum kWh cost is obtained for the Vestas V90 and the Nordex N80. •The electricity cost does not exceed 0.060 $/kWh which is a very competitive price compared to the

electricity price paid by the consumer of domestic sector in Algeria (0.054 $/kWh) and the cost of wind electricity will decrease further with the development of wind energy technology.

The results of this current study encourages the construction of wind farms at Adrar. In addition, using “ Vergnet GEV HP1MW and Vestas V90/2MW” is highly recommended.

7. Conclusion

The wind energy potential and economic analysis in the site of Adrar were investigated and the performances of 10 MW wind farm, using various WT located in this site were examined. Adrar has an important potential for wind energy exploitation, in term of energy produced and CF;


• The mean wind power density on this site is 280 and 775 W/m2 at 10 and 70 m respectively (x 3). • The best machine in term of production is the Vestas V90/2MW wind turbine model.

According to the cost analysis, it is seen that: • The minimum cost per kWh is 0.0408 $/kWh using the Vestas V90/2MW model and this price is

very competitive price compared to the price of electricity paid by the Algerian consumer; • The wind resource could provide a viable substitute to diesel oil.

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Technical and Economic Assessment of Wind Farm Power Generation at Adrar in Southern Algeria