Wind resource assessment in Algeria

Sustainable Cities and Society 22 (2016) 171–183

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Wind resource assessment in Algeria Sidi Mohammed Boudia a,∗ , Abdelhalim Benmansour b , Mohammed Abdellatif Tabet Hellal c a

Centre de Développement des Energies Renouvelables, CDER, 16340 Algiers, Algeria Division des Nanomatériaux, Microsystèmes, Nanosystèmes et Composants, Unité de Recherche Matériaux et Energies Renouvelables, DNMNC-URMER, University of Tlemcen, 13000 Tlemcen, Algeria c Laboratoire de la promotion des ressources hydriques, minières et pédologiques, législation de l’environnement et choix technologiques, University of Tlemcen, 13000 Tlemcen, Algeria b

a r t i c l e

i n f o

Article history: Received 8 June 2015 Received in revised form 11 February 2016 Accepted 14 February 2016 Available online 18 February 2016 Keywords: Wind energy Wind power density Weibull parameters Economic analysis Algeria

a b s t r a c t In this work the Algerian wind resource assessment is made using statistical analysis based on the measured wind speed data in the last decade from 63 meteorological stations distributed over the Algerian territory and 24 in neighboring countries close boundaries. Weibull distribution is used to study the monthly, seasonal and annual wind power potential over the country. Technical and economic evaluations of electricity generation from different commercial wind turbines, ranging between 200 kW and 2 MW are examined for four selected locations in Algeria for their greatest wind potential. The results led to the actualization of the wind map in Algeria which gave an enhancement of wind energy potential in the eastern region of the Sahara, the occidental Highlands region, three coastal regions open to the Mediterranean Sea and the extreme south of the country, while it has been downgraded in two regions situated in the north. The temporal study gives spring as the windiest period over the largest part of the country. The energy cost analyses show that the four windiest sites have good economic potential where the minimum cost per kWh is about 2.8 c$/kWh, assessed at the site of Hassi R’mel using the Gamesa G80/2000 wind turbine of 2 MW rated capacity, which generates the highest annual energy of 5.46 GWh with a capacity factor of 0.31. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction Since 2011, Algeria has engaged in a new phase of sustainable energy use, the government program consists to install 22 GW of power generated from renewable sources by 2030 (Stambouli, Khiat, Flazi, & Kitamura, 2012). Despite its relatively low potential assessed until now, wind energy is not excluded from the new program, as it constitutes the second axis of development with an electricity production expected to reach about 5 GW in 2030 with 1 GW which must be achieved by 2020 (CREG, 2015). Although Africa is beginning to exploit its enormous wind power potential, energy produced in 2012 was still small, with just 1 GW installed across the continent. Since Africa’s wind resource is best around the coasts and in the eastern highlands, at the end of 2012, the continent’s almost total wind installations can be found only

∗ Corresponding author at: Centre de Développement des Energies Renouvelables, CDER, B.P. 62 Route de l’Observatoire Bouzaréah, 16340 Algiers, Algeria. E-mail addresses: [email protected], [email protected] (S.M. Boudia). http://dx.doi.org/10.1016/j.scs.2016.02.010 2210-6707/© 2016 Elsevier Ltd. All rights reserved.

across three countries – Egypt (550 MW), Morocco (291 MW) and Tunisia (104 MW) (Sawyer & Rave, 2010). This wind energy evolution in Africa, sets Algeria at the bottom of the global installed wind power capacity table in the region. After taking several years, the installation of the first wind power plant of only 10 MW has been issued in June 2014, at Adrar in South of the country. The choice of this site is in adequacy with several wind resource assessment studies, which place the south of Algeria in the Sahara as the windiest (Boudia, 2013; Chellali, Khellaf, Belouchrani, & Recioui, 2011; Diaf & Notton, 2013a; Himri, Himri, & Stambouli, 2009; Himri, Stambouli, & Draoui, 2009; Himri, Stambouli, Draoui, & Himri, 2009; Merzouk, 2000; Merzouk, 2006), and would give the best resources. An important step in using wind energy is to have information on the carrying capacity of the local wind to drive wind turbines. Thus, several researchers have assessed the potential for wind energy and wind energy potential has been evaluated in many parts of the country. In this research axis, one may cite studies of Himri, Himri, et al. (2009), Himri, Rehman, Setiawan, and Himri (2012), Himri,

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Himri, and Stambouli (2010), Himri, Stambouli, Draoui, et al. (2009), Himri, Rehman, Draoui, and Himri (2008), Himri, Stambouli, and Draoui (2009) that were among the first to give a statistical analysis of wind speed at different regions in Algeria. The works in the assessment of wind resource at different sites in the Algerian Highlands (Boudia, Benmansour, Ghellai, Benmdjahed, & Tabet Hellal, 2012a, Boudia, Benmansour, Ghellai, Benmedjahed, & Tabet Hellal, 2012b) and in the Algerian Sahara (Boudia, Benmansour, Ghellai, Benmedjahed, & Tabet Hellal, 2013; Boudia, Benmansour, Ghellai, Benmedjahed, & Tabet Hellal, 2012c; Boudia, Benmansour, & Tabet Hellal, 2014). However, few research works in Algeria have been carried out for a technical and economic analysis of wind energy, as the studies of Diaf and Notton (2013a, 2013b). In the field of the establishment of Atlases and maps of wind in Algeria, a preliminary work was given by Ibrahim (1984), followed by the study of Bensaid (1985). Five years after, Hammouche (1990) published the first wind atlas of Algeria including the classification of wind parameters on the basis of the month and the wind direction for 37 meteorological stations using the WAsP software. This database was mainly used by Merzouk (2000) to map the wind atlas at 10 m, where wind data of 48 stations have been used, covering almost all the topographic zones of the country and to refine the tracing of the borders map, yearly mean wind speeds of 16 stations located in neighboring countries have been used. Aiche-Hamane and Khellaf (2003) give the mapping of the monthly mean wind speed from 75 meteorological stations in Algeria. Merzouk (2006) resumed the preceding work by refining results to plot a new Wind Atlas. Chellali et al. (2011) contributed to the actualization of the wind map of Algeria by performing a spectral analysis to study the cyclical wind, using daily mean wind speed collected over 37 meteorological stations between 2004 and 2009. Where the site of Hassi R’mel in north of the Sahara was introduced, which has a good wind potential, but has been underestimated due the fact that it was located between two less windy regions. In this work, we propose to assess the wind potential in Algeria, according to the months, seasons and whole years’ data, and contribute to the updating of the wind map at 10 m from the ground, using more recent meteorological data, collected from a larger number of measurement points. Thus, daily mean wind speeds collected in the last decade from 87 stations, 63 of which are located in Algeria and 24 in neighboring countries have been used to update the distribution cartography of wind speed in Algeria at a height of 10 m. In the first part, the wind data analysis was done by using the Weibull function at the anemometer height. After the statistical analysis which include several fundamental properties, such as the Weibull parameters, mean wind speed, skewness, kurtosis, standard deviation and power potential variations, the temporal evolution of mean wind speed, the annual mean value of shape factor and mean power density were mapped. In the second part, the performance of six chosen commercial wind turbine models, designed for electricity generation is examined, located at the four windiest sites of this study, with an economic evaluation of the wind energy. As for the project in Copenhagen, leading to produce energy for the city and which took part in 2000 in a large offshore wind farm project called Middelgrunden (Larsen, Soerensen, Christiansen, Naef, & Vølund, 2005) two kilometers off the city’s coastline, or for the total of 200 MW installed wind turbine capacity in the port of Rotterdam (Port of Rotterdam, 2016), the wind energy which could be eventually produced locally in the Algerian Sahara can be used for the national consumption. In this case, the present study may support the idea that cities can be visionary and produce energy themselves.

Fig. 1. Distribution of meteorological stations over Algeria.

2. Methodology In the perspective to prove that some cities can optionally produce their own energy, this study includes two major aspects which are: • The updating of the wind map in Algeria, using most recent meteorological data collected at ports and airports, near cities, • The technical-economic assessment of the wind energy production at the windiest cities. For this purpose, the present section involves to: • Introduce the wind data used in the study, with the specific probability distribution function used to describe and analyze the wind speed frequency, • Present the mathematical models used to assess the wind potential at the wind turbine hub height and the wind energy produced at the output of a given wind turbine, • Define the assumptions relating the method used to determine the present value of costs of electricity produced per year. In addition, the software packages Matlab and Surfer were exploited. 2.1. Wind data This work is based on daily mean wind speeds collected in the last decade at a standard height of 10 m from the ground for 63 meteorological stations distributed over Algeria (Fig. 1) belonging the network of National Meteorological Office (ONM). The geographical coordinates of these meteorological stations and measurement periods are given in Table 1. In addition, daily mean wind speeds of 24 stations located in seven neighboring countries close boundaries have been exploited to refine the tracing of the borders map. The data used were measured over a period of five (05) years from 01/01/2006 until 31/12/2010. Geographical coordinates are given in Table 2.

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Table 1 Geographical data of the measurement sites in Algeria. No.

Stations

1 2 3 4 5 6 7 8 9 10 11

Alger-Port Annaba Arzew Bejaia Beni-Saf El-Kala Ghazaouet Jijel-Achouat Jijel-Port Oran-Port Skikda

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Batna Borj Bou Arreridj Chlef Constantine Dar-El-Beida (Alger) Maghnia Mascara-Ghriss Médea Milliana Mostaganem Oran-Senia Oum-El-Bouagui Sétif Sidi-Belabes Souk-Ahras Tafraoui Tizi-Ouzou Tlemcen-Zénata

30 31 32 33 34 35 36 37 38 39 40

Boussaada Djelfa El-Bayadh El-Kheiter Kasr-Chellala Mechria Msila Naama Saïda Tebessa Tiaret

41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63

Adrar Ain-Safra Bechar Beni-Abbas Biskra Bordj Badji Mokhtar Djanet El-Golea El-Oued Ghardaia Hassi R’mel Hassi-Massaoud Illizi In-Amenas In-Salah In-Salah-North Laghouat Ourgla Tamanrasset Tamanrasset-Aguenna Timimoun Tindouf Touggourt

Longitude (◦ )

Latitude (◦ )

North-Coast

3.1 7.81 −0.26 5.06 −1.35 8.45 −1.86 5.78 5.78 −0.65 6.95

36.76 36.83 35.81 36.71 35.30 36.9 35.10 36.80 36.10 35.70 36.88

12 04 04 02 70 13 05 02 06 22 07

01/01/2008 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2003 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2004 to 31/12/2010 01/01/2008 to 31/12/2010 01/01/2002 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2008 to 31/12/2010 01/01/2009 to 31/12/2010 01/01/2001 to 31/12/2010

North-Tell

6.18 4.76 1.33 6.61 3.25 −1.78 0.15 2.75 2.23 0.11 −0.60 7.11 5.41 −0.61 7.95 −0.53 4.05 −1.46

35.75 36.06 36.21 36.28 36.68 34.81 35.21 36.28 36.30 35.88 35.63 35.86 36.18 35.20 36.28 35.53 36.70 35.01

1052 930 143 694 25 428 513 1036 721 138 90 891 1038 476 680 115 189 247

01/01/2001 to 31/12/2010 01/01/2002 to 31/12/2010 01/01/2003 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2003 to 31/12/2010 01/01/2003 to 31/12/2010 01/01/2002 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2002 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2007 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2002 to 31/12/2010 01/01/2003 to 31/12/2010 01/01/2005 to 31/12/2010 01/01/2003 to 31/12/2010 01/01/2001 to 31/12/2010

Highlands

4.20 3.25 1.00 0.06 2.31 −0.43 4.50 −0.30 0.15 8.13 1.43

35.33 34.33 33.66 34.15 35.16 33.58 35.66 33.26 34.86 35.41 35.35

461 1144 1347 1001 801 1149 442 1166 752 813 1127

01/01/2001 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2002 to 31/12/2010 01/01/2002 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2008 to 31/12/2010 01/01/2008 to 31/12/2010 01/01/2002 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2001 to 31/12/2010

Sahara

−0.28 −0.60 −2.23 −2.16 5.73 0.95 9.46 2.86 6.11 3.81 3.28 6.15 8.41 9.63 2.46 2.51 2.93 5.40 5.51 5.46 0.28 −8.13 6.13

27.88 32.76 31.50 30.13 34.80 21.33 24.26 30.56 33.50 32.40 32.93 31.66 26.50 28.05 27.23 27.25 33.76 31.93 22.80 22.80 29.25 27.70 33.11

263 1059 773 505 87 398 1054 397 63 450 764 142 558 562 269 269 765 141 1364 1377 312 431 85

01/01/2001 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2002 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2005 to 31/12/2010 01/01/2002 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2008 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2002 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2001 to 31/12/2010 01/01/2001 to 31/12/2010

Topographic situation

2.2. Wind analysis model As reported in a previous study (Maatallah, El Alimi, Dahmouni, & Nasrallah, 2013), wind speed frequency distribution has been represented by various probability density functions such as gamma, lognormal, three parameter beta, Rayleigh and Weibull distributions. However, the Weibull distribution has been used extensively to model variation of wind speed, where the most

Altitude (m)

Measurement period

appropriate estimation method of scale and shape Weibull parameters is still studied in order to improve the adjustment (Akda˘g & Güler, 2015). It is defined by the following equation (Akpinar & Akpinar, 2004; Ammari, Al-Rwashdeh, & Al-Najideen, 2015):

f (v) =

 k   v k−1 A

A

  k  v

exp −

A

(1)

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Table 2 Geographical coordinates of the measurement sites in neighboring countries. No.

Stations

1 2 3 4 5

Longitude (◦ )

Country

Oujda Errachidia Midelt Nador Ouarzazate

Morocco

Latitude (◦ )

Altitude (m)

−1.93 −4.40 −4.73 −2.91 −6.90

34.78 31.93 32.68 34.98 30.93

468 1034 1515 03 1139

9.16 8.70 8.81 8.80 8.45 8.75 8.68 8.10 9.80

31.68 36.13 34.41 36.48 35.15 36.95 35.55 33.91 37.25

258 518 313 143 707 20 1092 87 05

6 7 8 9 10 11 12 13 14

El-Borma El-Kef Gafsa Jendouba Kasserine Tabarka Thala Tazeur Bizerte

Tunisia

15

Gao

Mali

−0.05

16.26

265

16 17 18 19 20

Ghadames Ghat Nalut Obari Sebha

Libya

9.50 10.13 10.98 12.78 14.43

30.13 25.13 31.86 26.60 27.01

346 699 621 463 435

21 22

Agadez Tahoua

Niger

7.98 5.25

16.96 14.90

501 386

23

Bir-Moghrein

Mauritania

−11.61

25.23

364

24

Cagliari

Italy

9.05

39.25

04

where f(v), is the probability of observing wind speed v, k is the dimensionless Weibull shape parameter, and A is the Weibull scale parameter. The average wind speed can be calculated on the basis of the Weibull parameters, as given below (Bagiorgas, Giouli, Rehman, & Al-Hadhrami, 2011):



Vm = A · 

1+

1 k



(2)

where Vm is the average wind speed, and  is the Gamma function. There are various methods for estimating the parameters of the Weibull probability distribution function (de Andrade, Neto, Rocha, & da Silva, 2014; Maatallah et al., 2013). Taking into account the good performance of the maximum likelihood method (Seguro & Lambert, 2000), this method has been used in this work. The shape parameter can be estimated by the following equation iteratively (Petkovic´ et al., 2014): k=

n  n k  v ln(vi ) ln(vi ) i=1 i n k − i=1

(3)

n

v

i=1 i

where n is the total number of wind speed measurements and vi is the measured wind speed value for the ith measurement. After calculating the value of the shape parameter k, the scale parameter A can be determined using the following equation:

 A=

1 k vi n n

1/k (4)

i=1

In addition, the mean standard deviation , skewness Sk, and kurtosis Kt which are given by the following formulas can be calculated to evaluate the shape of the real probability distributions (Monahan, 2006):



=A  Sk =

1+

 2 k



−2 1+

 1 1/2

(5)

k

 (1 + 3/k) − 3 (1 + 1/k) (1 + 2/k) + 2 3 (1 + 1/k)



3/2

 (1 + 2/k) −  2 (1 + 1/k)

(6)

Kt =  (1+ 4/k) − 4 (1+ 3/k) (1+1/k) + 6 (1+ 2/k) 2 (1+1/k) − 3 4 (1+1/k)



2

−3

 (1+ 2/k) −  2 (1+1/k)

(7)

2.3. Wind power density It is well known that the power of the wind that flows at speed v through a blade sweep area Sw increases as the cubic of its velocity and is given by (Li & Li, 2005): P(v) =

1 Sw v3 2

(8)

where  is the air density, and considered to be equal to 1.225 kg/m3 . The wind power density of a site based on a Weibull probability density function can be expressed as follows: P 1 = A3  Sw 2



1+

3 k



(9)

2.4. Vertical extrapolation of mean wind speed The wind speed at the hub height is of interest for wind power application, the available wind speeds must be extrapolated to the wind turbine hub height. In this study, the power law is applied to this objective, as shown in the following equation (Justus & Mikhail, 1976; Tizpar, Satkin, Roshan, & Armoudli, 2014) V2 = V1

 Z ˛ 2

Z1

(10)

where V2 is the extrapolated wind speed at height Z2 and V1 is the measured speed at Z1 . The exponent ˛ depends on such fac˜ tors (Banuelos-Ruedas, Angeles-Camacho, & Rios-Marcuello, 2010; Spera, 1994) as the nature of the terrain (surface roughness) (Davenport, 1966), wind speeds (Ramachandra, Rajeev, Krishna, &

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175

Shruthi, 2005) and atmospheric stability (Smith, 1968). To estimate the coefficient ˛, we use the following expression which takes into consideration several types of soil roughness (Knidiri & Laaouina, 1986; Nfaoui, Buret, & Sayigh, 1998): ˛=

x − 0.088 · ln(V1 ) 1 − 0.088 · ln(Z1 /10)

(11)

where the coefficient x depends on the roughness class as follows: x = 0.25 for 0.005 > Z0 ≥ 0 m; x = 0.31 for 0.05 > Z0 ≥ 0.005 m; x = 0.37 for 0.5 > Z0 ≥ 0.05 m; x = 0.48 for 4 > Z0 ≥ 0.5 m. 2.5. Wind power and wind energy output The capacity factor Cf is defined as the ratio of the mean power output Pout to the rated electrical power Pr of the wind turbine, and can be calculated by the following formula (Adaramola, Paul, & Oyedepo, 2011; Bagiorgas et al., 2011; Boudia et al., 2013)

 Cf =

k

e−(Vc /A) − e−(Vr /A) k

(Vr /A) − (Vc /A)



k

k

−e

−(Vf /A)k

(12) Fig. 2. The distribution of annual mean wind speed in Algeria at 10 m height (m/s).

where Vc , Vr and Vf are the cut-in wind speed, rated wind speed and cut-off wind speed, respectively. Thus, the average power output Pout can be expressed as



Pout = Pr ·

k

e−(Vc /A) − e−(Vr /A) k

(Vr /A) − (Vc /A)



k

k

− e−(Vf /A)

k

(13)

Once the average power output Pout is known, average gross energy production Eavg of a turbine can be calculated for a specific period as: Eavg = Pout · T

(14)

where T is the time frame under consideration in hours, T = d·24 and d is days number. 2.6. Analysis of energy cost The feasibility of a system in a wind energy plant over its expected lifetime can be judged by a cost analysis. The cost of the WECs depends on the investment costs, operation and maintenance costs, operating condition and the location of the wind turbine. The computation of wind energy cost is carried out using the Present Value Cost (PVC) method and the Unit Energy Cost (CPU) method. In this analysis, the PVC method is used to estimate the cost of wind energy production. The estimation of the cost of the kWh of energy produced by different wind turbine models is expressed as the present value of costs (PVC) of the investment divided by the energy output during the lifetime of a wind turbine (Bataineh & Dalalah, 2013). The calculation of the cost has been done under the following assumption (Bataineh & Dalalah, 2013; Diaf & Notton, 2013a; Shata & Hanitsch, 2006): • The method of the present value of the money is used to determine the costs. • The machine is assumed to have a lifetime of 20 years. • The interest rate (r) and the inflation rate (i) are said to be 8 and 9%. • Operation, repair and maintenance costs (COMR ) are estimated by 25% of the annual cost of the machine (machine price/lifetime). • The salvage value S is taken to be 10% of the investment of machines and civil work.

• An investment of an amount of I includes the turbine price plus its 20% for the civil work, the connection cables to the grid and other setup costs. Under the above assumptions, the present value of the costs PVC is: PVC = I + COMR

1 + i r−i

1−

 1 + i n  1+r

−S

 1 + i n 1+r

(15)

where I is the investment cost, COMR is the operation, maintenance and repair cost, i is the inflation rate, r is the interest rate, n is the lifetime of the machine (in years) and S is the salvage value. As shown in Table 3, the specific cost of a wind turbine is dependent on the rated power, but varies from one manufacturer to another (Adaramola et al., 2011; Gökc¸ek, Erdem, & Bayülken, 2007; Sathyajith, 2006). Since the economic feasibility of the wind energy development depends on its ability to generate electricity at a low operating cost per unit energy, accurate estimate of all the costs involved in generating electricity over the life span of the system is essential. For machine size above 200 kW, the turbine cost can be taken as 1150 $/kW, an average value between a minimum of 700 $/kW and a maximum of 1600 $/kW (Diaf & Notton, 2013b). Thus, the wind turbine price can be determined as the specific cost multiplied by the rated power of the wind turbine. Finally, the unit cost of energy (CPU) represents the Present Value Cost (PVC) divided by the total energy generated by the wind turbine during its entire lifetime.

Table 3 Range of specific cost of wind turbines based on the rated power (Adaramola et al., 2011). Wind turbine size (kW)

Specific cost ($/kW)

Average specific cost ($/kW)

<20 20–200 >200

2200–3000 1250–2300 700–1600

2600 1775 1150

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Fig. 3. Previous annual maps of wind speed in Algeria at 10 m height. (a) Chellali et al. (2011). (b) Merzouk (2006).

3. Results and discussion 3.1. Updating of the wind map We should stress that in this paper, we focus on large-scale features over Algeria. This is due to the limited number of measurement points over a territory which reaches 2.3 m km2 . Thereby,

the maps in this study were made by Surfer software which uses a bilinear interpolation method to calculate the values at points that do not coincide with grid nodes. The annual wind map of Algeria, updated at a height of 10 m, is presented in Fig. 2. The wind map shows that the annual mean wind speed varies from 1.2 to 6.3 m/s. Adrar region maintains its supremacy in terms

Fig. 4. The distribution of seasonal mean wind speed in Algeria at 10 m height (m/s).

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177

Fig. 5. The distribution of monthly mean wind speed in Algeria at 10 m height (m/s).

of maximum wind speed with 6.3 m/s. Followed by the region of Hassi R’mel with 6.1 m/s. The site of Tindouf in the extreme west of the country takes the third place with and annual mean wind speed ≈6 m/s.

By incorporating new weather stations in this work, the present wind map is updating the previous studies (Fig. 3), where the wind potential in different regions has been revised upward, such as:

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Fig. 6. The distribution of annual shape factor in Algeria at 10 m height (dimensionless).

- Mechria, in occidental Highlands, where annual mean wind speed is ≈5 m/s. - Bordj Badji Mokhtar, in the extreme south of the Sahara, with an annual mean wind speed ≈5 m/s. - The eastern boundary of the country with Tunisia, where annual mean wind speed ≈5 m/s. - Coastal regions open to the Mediterranean Sea, where: • The annual mean wind speed exceeds 4 m/s in Oran and Algiers ports. • El-Kala site in Eastern coast, where the annual mean wind speed ≈4 m/s. • Extreme western coast, with Moroccan borders where the annual mean wind speed ≈4 m/s. While the wind potential in other regions has been revised downward, as: - Tizi-Ouzzou and Maghnia in the north, where the annual mean wind speed is equal to 1.2 and 1.4 m/s, respectively.

Fig. 4 gives the seasonal maps of the wind resource over Algeria. Winter data relate to the period from 22 December to 20 March; Spring, from 21 March to 22 June; Summer, from 23 June to 21 September and Autumn, from 22 September to 21 December. From the seasonal study, spring is clearly the windy season over the largest part of the country (Fig. 4c), followed by summer in the south of Algeria (Fig. 4d) and winter in north (Fig. 4b). Autumn period gives the less windy season (Fig. 4a), where mean wind speed does not exceed 4 m/s over a large part of the country excepted about ten sites especially located in the Highlands and Sahara. The highest seasonal mean wind speed is estimated in spring with 7.8 m/s at Hassi R’mel region, while the lowest value is given in autumn at the site of Tizi-Ouzzou, equal to 1.16 m/s. Also, it must be noted that some sites remain quite windy during the year, as Adrar in south-west, Hassi R’mel in the center of the country, Mechria in the western Highlands and the region near the eastern borders. Furthermore, wind potential in the west coastline in the region of Oran remains stable over the fourth seasons. According to this seasonal study, it is clear that south of Algeria including Highlands is windier than north for a large period. From the monthly study shown in Fig. 5, it is clear that the average wind speed varies differently in each studied region. As a general rule, and in adequacy with the seasonal assessment, the period from March to June is the windiest throughout the country, while the months in autumn are the least windy. The highest monthly mean wind speed is assessed in May with 8.21 m/s at Hassi R’mel region, while the lowest value is evaluated in November at the site of Tizi-Ouzzou, equal to 1.08 m/s. It should be noted also that the north part of Algeria is less windy in summer months than winter months, and vice versa for the south. 3.2. Shape factor evolution The use of the Weibull distribution has the advantage to define the type and the nature of wind by studying the evolution of shape factor. Thus, Fig. 6 illustrates the annual variability of the shape parameter k at 10 m high. It is noticed that the shape parameter is around 2 on a large part of the southern Sahara, in a part in the northeast and in a small part of the west side of the Highlands. The northern part of the country, including the highlands (above 32◦ latitude) with the exception of the northeast has a lower value of the shape factor, leading to a wind less constant than in the southern Sahara. Note also the large value of the shape factor at Adrar in southwest, Bejaia and Annaba, two coastal sites in the east of Algeria, where it is around 3. This leads to conclude that the wind in these three sites is the most stable and the most constant around its average value. The site of Arzew in northwest and the site of Setif in the northeast have a good value of shape factor respectively equal to 2.5 and 2.8. The same at Tamanrasset in the south and the border area between Tunisia and Libya which have a shape parameter ≈2.5 and confirm the stability of the wind speed in these regions around their respective average mean speeds. 3.3. Mean power density

Fig. 7. The distribution of annual wind power density in Algeria at 10 m height (W/m2 ).

One of the most important parameters to assess wind energy potential is the mean power density; it indicates the energy available on a site after its conversion into electricity. Using the mean power density estimated at 10 m height for all studied sites, the annual map of average power density is given in Fig. 7. This parameter varies significantly by region, where the maximum value is given in south at Hassi R’mel site with 295 W/m2 , while the site of Bordj Bou Arredidj in the north gives the lowest value with 13 W/m2 .

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Table 4 Wind speed statistics for meteorological stations in Algerian’s North-Coast. No.

Stations

1 2 3 4 5 6 7 8 9 10 11

Alger-Port Annaba Arzew Bejaia Beni-Saf El-Kala Ghazaouet Jijel-Achouat Jijel-Port Oran-Port Skikda

Topographic situation

North-Coast

 (m/s)

Sk

Kt

2.41 1.37 1.80 1.15 2.27 2.30 1.35 1.67 1.93 1.99 1.47

0.84 0.18 0.34 0.17 1.60 0.88 0.75 0.95 0.67 0.45 0.41

0.70 −0.27 −0.16 −0.27 3.64 0.82 0.50 1.01 0.31 −0.03 −0.09

Table 5 Wind speed statistics for meteorological stations in Algerian’s North-Tell. No.

Stations

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Batna Borj Bou Arreridj Chlef Constantine Dar-El-Beida (Alger) Maghnia Mascara-Ghriss Médea Milliana Mostaganem Oran-Senia Oum-El-Bouagui Sétif Sidi-Belabes Souk-Ahras Tafraoui Tizi-Ouzou Tlemcen-Zénata

Topographic situation

North-Tell

Note also the good wind potential in the southwestern part of Algeria at Adrar, Bordj-Badji-Mokhtar, Timimoun, Tindouf and InSalah. While in the Highlands, a significant mean power density equal to 130 W/m2 is noted at Djelfa, Mecheria, Kasr-Chellala and Msila. 3.4. Results analysis of the wind speed data Applying the above statistics introduced in Section 2.2 to the wind speed data, the obtained results are listed in Tables 4–7, according to the topographical situation of the meteorological stations.

 (m/s)

Sk

Kt

1.82 1.23 1.81 1.60 1.47 2.01 1.54 1.80 2.03 1.43 1.72 1.97 1.48 1.89 2.69 2.15 1.52 1.41

0.53 0.77 0.76 1.02 0.64 2.20 0.96 0.84 1.13 1.07 0.60 0.87 0.24 1.18 1.31 0.88 1.81 1.12

0.07 0.54 0.52 1.21 0.27 7.37 1.04 0.70 1.60 1.39 0.19 0.77 −0.24 1.79 2.30 0.82 4.79 1.56

As it is apparent from Tables 4–7, the skewness is always positive. Positive values for the skewness indicate that the data are skewed right, which means that the right tail is long relative to the left tail. The skewness is a measure of symmetry, or more precisely, a lack of symmetry. A distribution or data set is symmetric if it looks the same to the left and right of the center point. The skewness for a normal distribution is zero, and any symmetric data set should have a skewness of close to zero. Kurtosis is a statistic employed to describe the steep degree of the data, where Kurtosis = 0 representing the steep degree of the data is the same as the standard normal distribution, Kurtosis > 0 means the distribution of the data is steeper than the standard

Table 6 Wind speed statistics for meteorological stations in Algerian’s Highlands. No.

Stations

1 2 3 4 5 6 7 8 9 10 11

Boussaada Djelfa El-Bayadh El-Kheiter Kasr-Chellala Mechria Msila Naama Saïda Tebessa Tiaret

Topographic situation

Highlands

 (m/s)

Sk

Kt

4.35 2.65 2.30 2.57 3.51 2.46 3.17 2.50 1.97 1.96 2.75

2.93 0.77 1.33 1.27 2.13 0.56 1.38 0.99 0.96 0.86 1.11

13.92 0.54 2.36 2.11 6.87 0.11 2.57 1.14 1.04 0.75 1.51

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Table 7 Wind speed statistics for meteorological stations in Algerian’s Sahara. No.

Stations

Topographic situation

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Adrar Ain-Safra Bechar Beni-Abbas Biskra Bordj Badji Mokhtar Djanet El-Golea El-Oued Ghardaia Hassi R’mel Hassi-Massaoud Illizi In-Amenas In-Salah In-Salah-North Laghouat Ourgla Tamanrasset Tamanrasset-Aguenna Timimoun Tindouf Touggourt

Sahara

 (m/s)

Sk

Kt

2.09 1.85 2.34 1.74 2.77 3.41 1.88 2.28 2.28 2.14 3.48 2.40 1.84 2.21 2.87 2.79 2.58 2.09 1.65 1.66 2.57 2.78 2.00

0.06 1.18 0.89 0.94 0.95 1.11 0.69 0.86 1.31 0.85 0.76 0.99 0.44 0.51 0.75 0.69 1.27 0.72 0.43 0.41 0.64 0.54 0.96

−0.29 1.79 0.85 0.98 1.01 1.51 0.35 0.75 2.30 0.73 0.52 1.14 −0.05 0.04 0.50 0.37 2.11 0.43 −0.06 −0.08 0.26 0.08 1.04

Table 8 Main characteristics of the selected wind turbines. Model

Rated capacity (kW) Rotor diameter (m) Hub height (m) Cut in speed (m/s) Rate speed (m/s) Cut out speed (m/s) Swept area (m2 )

Vergnet GEV MPR

Izar-Bonus MK IV

Nordex N50

Neg Micon NM60/1000

GE Wind Energy GE1.5

Gamesa G80/2000

200 30 32.3 4 12 25 706.5

600 44 40 4 15 25 1519.76

800 50 50 4 14 25 1962.5

1000 60 45 3 14 20 2826

1500 77 65 3 12 20 4654.26

2000 80 67 3 15 25 5024

normal distribution while Kurtosis < 0 reflects the distribution of the data is less steep than the standard normal distribution. 3.5. Electricity generation and cost analysis The actualization of the wind map of Algeria in this study, gives the windiest sites situated in the south. Thus, the energy yield estimation of six wind turbines models chosen in this study, was made

Fig. 8. Power curves for the selected wind turbines.

at the four windiest sites, which are Adrar, Hassi R’mel, Tindouf and In-Salah-North. The technical data of the selected wind turbine models with a rated capacity between 200 kW and 2 MW are summarized in Table 8, and the power curves are shown in Fig. 8. At the four selected sites, the assessment of mean annual wind speed, standard deviation, mean power density and the two Weibull parameters at the hub height for each wind turbine are given in Table 9. While the results of Capacity Factor and Annual Electricity Production of wind turbines with Cost Per Unit are given in Table 10. The annual energy production ranges from about 273.59 MWh in Adrar with the Vergnet GEV MPR model to 5467.07 MWh in Hassi R’mel using Gamesa G80/2000 wind turbine. The annual energy output using the six selected wind turbines ranges from 273.59 MWh to 3133.63 MWh in Adrar, from 543.32 MWh to 5467.07 MWh in Hassi R’mel, from 400.44 MWh to 4182.54 MWh in Tindouf and from 364.85 MWh to 3769.15 MWh in In-Salah-North. Although Adrar represents the windiest region, we can explain the low energy output compared to the three other regions by the high value of the shape parameter in this region, which reach the value of 3.4, while it does not exceed the value of 2.15 at the other studied regions, for each hub height. Regarding locations, the Gamesa G80/2000 wind turbine model generates the highest quantity of annual energy output in all selected regions. The smaller quantities of annual energy production are given in Adrar for all wind turbines. The annual capacity factors calculated for the six wind turbines considered in this paper, shows that it depends both on the site and on the machine model. This factor varies slightly from one turbine

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Table 9 Annual statistical analysis at the four selected sites for each wind turbine hub height. Adrar

Hassi R’mel

Tindouf

In-Salah-North

Vergnet GEV MPR (200 kW) 32.3 m a.g.l.

Vm (m/s)  (m/s) P (W/m2 ) A (m/s) k

7.52 2.24 330.06 8.33 3.74

7.18 3.70 426.48 8.10 2.03

6.74 3.00 305.91 7.61 2.39

6.14 3.04 255.44 6.93 2.13

Izar-Bonus MK IV (600 kW) 40 m a.g.l.

Vm (m/s)  (m/s) P (W/m2 ) A (m/s) k

7.76 2.27 358.78 8.58 3.82

7.40 3.74 456.79 8.35 2.07

6.96 3.04 331.04 7.85 2.44

6.35 3.08 267.83 7.17 2.17

Nordex N50 (800 kW) 50 m a.g.l.

Vm (m/s)  (m/s) P (W/m2 ) A (m/s) k

8.01 2.29 391.51 8.85 3.91

7.63 3.78 490.98 8.62 2.12

7.19 3.08 359.63 8.11 2.50

6.57 3.13 301.22 7.42 2.22

Neg Micon NM60/1000 (1 MW) 45 m a.g.l.

Vm (m/s)  (m/s) P (W/m2 ) A (m/s) k

7.89 2.28 375.69 8.72 3.87

7.52 3.76 474.50 8.49 2.10

7.08 3.06 345.82 7.98 2.49

6.46 3.11 289.43 7.30 2.20

GE wind energy GE1.5 (1.5 MW) 65 m a.g.l.

Vm (m/s)  (m/s) P (W/m2 ) A (m/s) k

8.32 2.32 433.96 9.17 4.02

7.92 3.83 534.83 8.94 2.18

7.48 3.12 396.63 8.43 2.57

6.85 3.18 332.86 7.73 2.28

Gamesa G80/2000 (2 MW) 67 m a.g.l.

Vm (m/s)  (m/s) P (W/m2 ) A (m/s) k

8.35 2.33 439.16 9.21 4.03

7.95 3.83 540.17 8.98 2.19

7.51 3.13 401.16 8.46 2.58

6.88 3.19 336.74 7.77 2.29

model to another for a given site where the difference does not go beyond 7%. While for a given wind machine, the highest difference of the factor capacity value does not reach 14% from one turbine model to another. According to the cost analysis, it is seen that at the four selected sites, the minimum cost of unit energy per kWh of energy is obtained with the Neg Micon NM60/1000. By other hand, at the

site of Hassi R’mel, the Gamesa G80/2000 with a rated capacity of 2 MW gives the similar minimum cost as given by the Neg Micon NM60/100 wind turbine, equal to 2.8 c$/kWh. The maximum cost is obtained with the GE Wind Energy GE1.5 at Hassi R’mel, Tindouf and In-Salah-North, while at Adrar region, the maximum cost is obtained with the Izar-Bonus MK IV. By other hand, at Adrar, the cost of electricity per kWh varies between a minimum of

Table 10 Capacity factor (Cf ), Annual Energy Production (AEP) and cost analysis (CPU) for the selected wind turbines at the four regions. Region

Turbine model

Cf

AEP (MWh)

CPU (c$/kWh)

Adrar

Vergnet GEV MPR Izar-Bonus MK IV Nordex N50 Neg Micon NM60/1000 GE Wind Energy GE1.5 Gamesa G80/2000

0.1388 0.1163 0.1262 0.1820 0.1243 0.1789

273.59 611.19 995.14 1594.16 1632.88 3133.63

6.3 7.5 6.9 4.8 7.0 4.9

Hassi R’mel

Vergnet GEV MPR Izar-Bonus MK IV Nordex N50 Neg Micon NM60/1000 GE Wind Energy GE1.5 Gamesa G80/2000

0.2757 0.2623 0.2624 0.3120 0.2471 0.3120

543.32 1378.49 2068.60 2733.33 3246.58 5467.07

3.2 3.3 3.3 2.8 3.5 2.8

Tindouf

Vergnet GEV MPR Izar-Bonus MK IV Nordex N50 Neg Micon NM60/1000 GE Wind Energy GE1.5 Gamesa G80/2000

0.2032 0.1887 0.1908 0.2416 0.1783 0.2387

400.44 991.58 1504.05 2116.67 2342.21 4182.54

4.3 4.6 4.6 3.6 4.9 3.7

In-Salah-North

Vergnet GEV MPR Izar-Bonus MK IV Nordex N50 Neg Micon NM60/1000 GE Wind Energy GE1.5 Gamesa G80/2000

0.1851 0.1770 0.1745 0.2177 0.1588 0.2151

364.85 930.38 1375.56 1907.07 2086.22 3769.15

4.7 4.9 5.0 4.0 5.5 4.1

Bold values indicate the wind turbine which gives the best results for each region.

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4.8 c$/kWh and a maximum of 7.5 c$/kWh. At Hassi R’mel, it varies between 2.8 and 3.5 c$/kWh. At Tindouf, it varies between 3.6 and 4.9c $/kWh, while at In-Salah-North, the cost varies between 4.0 and 5.5 c$/kWh. 4. Conclusions In this paper, a contribution to the improvement of the wind map in Algeria was made, using more recent meteorological data, collected at 63 measurement points distributed over the Algerian territory and 24 in neighboring countries close boundaries. It has been found that: • The annual mean wind speed varies from 1.2 to 6.3 m/s at 10 m and the sites situated in south of Algeria are windiest where Adrar gives the greatest annual mean wind speed with 6.3 m/s, followed by Hassi-R’mel with 6.1 m/s. • By incorporating new weather stations in this work, wind potential in different regions has been revised upward, as Mechria site, in occidental Highlands, Bordj Badji Mokhtar site, in the extreme south of the Sahara, the eastern boundary of the country and three sites in the coastline at Oran port, Algiers port and El-Kala. While wind potential in Tizi-Ouzzou and Maghnia has been revised downward. • The temporal assessment gives spring as the windiest period, followed by summer in the south and winter in the north; autumn represents the less windy season. • Wind in Algeria is generally quite stable, where the annual shape parameter exceeds the value of 1.5 over a large part of its surface. • The annual mean power density study gives a potential which varies significantly by region, where the maximum value was assessed in the south at Hassi R’mel site with 295 W/m2 and the minimum value was evaluated in Highland at Bourdj Bou Arreridj site with 13 W/m2 . • The four windiest regions with an annual mean wind speed which varies from 5.13 to 6.37 m/s have been chosen for the assessment of electricity generation using the performances of six selected commercial wind turbine models. • The annual energy production ranges from about 273.59 MWh in Adrar with the Vergnet GEV MPR model to 5467.07 MWh in Hassi R’mel using Gamesa G80/2000 wind turbine. While by site, the annual energy output ranges from 273.59 MWh to 3133.63 MWh at Adrar, from 543.32 MWh to 5467.07 MWh at Hassi R’mel, from 400.44 MWh to 4182.54 MWh at Tindouf and from 364.85 MWh to 3769.15 MWh at In-Salah-North. • According to the cost analysis, it is given that the minimum cost of unit energy per kWh of energy is obtained with the Neg Micon NM60/1000. Concerning the maximum cost, it is obtained with the GE Wind Energy GE1.5 at Hassi R’mel, Tindouf and In-SalahNorth, while at Adrar, the maximum cost is obtained by the IzarBonus MK IV wind turbine. • At Adrar, the cost of electricity per kWh varies between a minimum of 4.8 c$/kWh and a maximum of 7.5 c$/kWh. At Hassi R’mel, it varies between 2.8 and 3.5 c$/kWh. In Tindouf, it varies between 3.6 and 4.9 c$/kWh, while at In-Salah-North, the cost varies between 4.0 and 5.5 c$/kWh. • The Neg Micon NM60/1000 of 1 MW rated capacity wind turbine model can be recommended for wind farms constructing at Hassi R’mel region, for electricity generation. Finally, this work conducted to the updating of the wind map of Algeria with the study of other parameters over the surface. It is a base for the assessment of wind energy potential of Algeria, and should be improved, considering the number of measurement points over a surface of more than 2.3 m km2 . In addition, the

meteorological stations used in this work are for the most part located in airports, supposed to be less windy areas. To refine this work, the number of the measurement points should be multiplied and placed in different regions of the country, avoiding urban areas, where the measurements shall be made with greater frequency and at different heights.

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