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Physics Procedia 55 (2014) 192 - 198

Eigth International Conference on Material Sciences (CSM8)

Assessment parameters and matching between the sites

and wind turbines

A.Chermitti8^ , M. Bencherif, Z. Nakoula, N. Bibitrikia, B. Benyoucefa

aUnité de Recherche Matériaux et Energies Renouvelables (URMER)

Université Abou-Bekr Belkaid, B.P. 119, Tlemcen, Algérie bLaboratoire d'automatique Departement GEE/Faculté-Technologie Université Abou-Bekr Belkaid, B.P. 230, Chetouane Tlemcen, Algérie

Abstract

The objective of this paper is to introduce the assessment parameters of the wind energy production of sites and pairing between the sites and wind turbines. The exploration is made with the wind data gathered at 10 m high is based on the atlas of the wind of Algeria established by the National office of the Meteorology runs 37 stations of measures. The data is used for a feasibility analysis of optimum future utilization of Wind generator potentiality in five promising sites covering a part of landscape types and regions in Algeria.

Detailed technical assessment for the ten most promising potential wind sites was made using the capacity factor and the site effectiveness approach.

The investigation was performed assuming several models of small, medium and big size wind machines representing different ranges of characteristic speeds and rated power suitable for water pumping and electric supply. The results show that small wind turbines could be installed in some coast region and medium wind turbines could be installed in the high plateau and some desert regions and utilized for water supply and electrical power generation, the sites having an important wind deposit, in high plateau we find Tiaret site's but in the desert there is some sites for example Adrar, Timimoun and In Amenas, in these sites could be installed a medium and big size wind turbines.

©2014ElsevierB.V.This isanopenaccess articleundertheCC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/3.0/).

Peer-review under responsibility of the Organizing Committee of CSM8-ISM5

Keywords: Wind energy; Wind characteristics; wind speed distribution; capacity factor; site effectiveness.

1. Introduction

To seize the range of renewable energies in Algeria and the stakes considerable, it is suitable first of all, to remind the considerable and inexhaustible existing resources of renewable energies not yet exploited, namely the exceptional solar layer which covers a surface of 2.381.745 km2, with more than 3000 hours of solar radiation per year and and an appreciable potential of wind energy and geothermic that can be easily mobilized.. In addition, these energies are clean, renewable and are used where they are and their decentralized character is appropriate well at the scattered state of the zones with low density of population.

Consequently, they can contribute to the environmental protection and be regarded as future and

* Chermitti ali. Tel.: 0213 526 0591. E-mail address:a.chermitti@yahoo.fr

1875-3892 © 2014 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.Org/licenses/by-nc-nd/3.0/).

Peer-review under responsibility of the Organizing Committee of CSM8-ISM5 doi: 10.1016/j.phpro.2014.07.028

alternative resources of energy in relation to the conventional resources. These energies are foreseen of the future of the rural world and against its insulation and for health of their inhabitants and to supply water, electrical power generation and telecommunications and to struggle against deforestation. What induces the stabilization of the populations on their origin places with promising prospects as for their living conditions. The ratification of the protocol of Kyoto and the law on the promotion of renewable energies within the framework of the durable development came to confirm the Algerian political good will and the engagement of our country for the exploitation of renewable natural resources and without polluting , thanks to an increased mobilization of the efforts of research and development for the control of the technologies implemented in the installations of conversion of the power renewable energies.

Nomenclature

E output energy, MWh/year

f(V) winds peed frequency distribution function

Pr rated output power of the turbine

Vc cut-in wind speed

Voff cut-off wind speed

Vm mean wind speed

V3m, cubic mean wind speed

Vo operational wind speed

Vr rated winds peed

CF capacity factor

s Site effectiveness

r| WECS overall efficiency

2. Wind distribution and available power

In many cases, the Weibull probability density function allows to model the availability of wind energy for a specific region by means of the probability of occurrence of the wind speed. The Weibull and Rayleigh probability density functions are commonly used and widely adopted in wind power studies. It is important to keep in mind that Rayleigh is only a subset of Weibull probability density function. The Weibull probability density function is defined as, [1, 2, 3, 4, 5]:

\k-1 i / Tr\k\

f (V ) =

The cumulative distribution function of the velocity V gives us the fraction of time (or probability) that the wind velocity is equal or lower than V. Thus the cumulative distribution F (V) is the integral of the probability density function [3, 6]:

i / \ k \

F (V ) = 1 - exp

k : is the shape factor of the Weibull law, describing the distribution of the winds velocity. C : in m/s is the scale factor of the Weibull law, it is connected to the mean velocity by the shape factor k.

3. Energy conversion model

The model of energy conversion used for the evaluation of the wind turbine siting and potentiality is the linear model, which is given by a single equation relating electrical power output to wind power input by the following relation [1, 5]:

P = (lm.%, )Pa = 0.5p{CpVmeVel )V3 (3)

Where Pa is the fraction of power extracted from the power in the wind and ^ is the global output of the system, which takes account various efficiency; electric (^el), mechanics (qme) aerodynamics (CP).

As for the power curve model of a real wind turbine, it can be modelled by four parameters: the cut-in speed Vc, the rated speed Vr, the cut-off speed Voff, and the nominal power Pr.

x 105 Wind turbine: Nortank150KW/24.5

—Typical WECS power curve -Power law approximation curve

10 15 20

Wind speed m/s

Fig. 1: Typical WECS power curve showing the theoretical power law approximation.

For the analysis of the available wind data in the chosen Algerian sites for wind turbines siting and potentiality. We use the power law of Stevens and Smulders and Pallabazzer, approximating the turbine power curve of real machines [7, 8] is as follows (see Fig. 1):

P(V) =

Pr(V2 - Vc2)

(Vr2 - Vc2 Pr 0

Vc < V < Vr

Vr < V < Voff Otherwise

It is appropriate to simulate the power curve of a pitch-controlled wind turbine and to a lesser extent a stall- or a yaw-controlled wind turbine, which do not have a constant power range and thus neglects the power output exceeding Pr. The overall efficiency ^ in this model takes the form:

rjrVr3(v2 - Vc2)

r/rVr3

Vc < V < Vr Vr < V < Voff

The maximum of ^ (design efficiency) falls into the power law range, Vc-Vr, and is calculated by finding the first derivative of ^ from Eq (4) with respect to V and assigning a zero to the derivative, we deduce the wind speed, V, is the operational wind speed , noted Vo, which gives the maximum efficiency, ^max. Vo = VcS ~ 1.73Vc

^rVr 3 (6)

t] max

2.6Vc (Vr 2 - Vc 2 )

Weibull probability density function is more accurately representing the wind speed variation; it will be used to calculate the average electrical output energy. The functioning of a wind turbine is limited by the cut-in speed Vc and the cut-off speed Voff. Therefore, E can be computed with [3, 4, 6]:

VfP(V) f (V )dV

The average electrical energy output can be calculated by integrating Eq. (7) over the intervals given. Thus the energy output can be given as:

[£p(V ) f (V )dV + Vf Pd (V ) f (V )dV

The evaluation of Eq. (9) has been analytically derived by using the incomplete gamma function and its relation is given as:

T Pr f VM2 " Vc2)/(V)dV + VO f (V)dV 1 (9)

(Vr2 - Vc2)

Voff C

The capacity factor is obtained as [4]: E

CF = -

The capacity factor reflects how effectively the turbine could harness the energy available in the wind spectra. Capacity factor for a reasonably efficient turbine at a potential site may range from 0.25 to 0.4. If its value reach 0.4 or higher indicates that the system is interacting with the regime very efficiently. The capacity factor of the system can be a useful indication for the effective matching of wind turbine and wind regime, it can be also interpreted like the effectiveness of outlet power with respect to the nominal power of the turbine.

Introducing the site coefficient, s, as the ratio between the output energy E and the maximum available energy converted by WECS running at constant design efficiency -qmax Em, where Em is the average wind energy available in a given site during a period T [6, 12]:

ilJKrrifHvc,KMf,f)] (12)

rj max Em

T] max (Vr 2 - Vc 2 )/m

4. Sites chosen

The sites whose main wind data have been used in this work are presented in Table 1. Tablel: Main wind data of the sites at an elevation 10 m

Topographical Situation Sites Symbol Roughness m K C m/s Vm m/s

Tiaret C01 0.02 1.58 6.90 6.19

Adrar D01 0.01 2.15 7.20 6.37

Sahara In Salah D02 0.02 1.78 6.01 5.42

In Aménas D03 0.02 2.01 6.16 5.46

Timimoun D06 0.01 1.89 6.5 5.76

Table 1 contains the yearly mean parameters of Weibull probability distribution function and the yearly mean wind speed. The data were collected by Algerian Meteorological Department at a standard height of 10 m. All quantities in Table 1 were evaluated as yearly mean value over an entire period of 10 last years. The sites are aggregated in the two geographic, (C) high plateau region and (D) Sahara region.

The Vertical extrapolation of Weibull parameters at an elevation above 10 m is obtained by using the following formulas [11, 13, 14]:

' logf"

V (Z 2 ) = V Z )

Where V (Z1) is the wind speed at standardised height (10 m), V (Z2) is the wind speed at the required or extrapolated height Z2 and Z0 is the surface roughness and is a terrain-dependent parameter.

C = Cj

0.0881 ln(C1) 1 - 0.00881 ln(0.1Z1 )

The obtained results are contained in the Table 2.

Table 2: Main wind data of the sites at various elevations

Sites H m K C m/s Vm m/s V3m m/s P W

C01 24 1.71 7.87 7.2 9.22 481.18

D01 24 2.33 8.11 7.18 8.51 378.59

D02 24 2.17 7.02 6.22 7.51 260.10

D03 24 2.02 6.97 6.17 7.62 271.91

D04 24 1.77 5.54 4.93 6.38 159.60

5. Models of Wind energy system conversion (WECS)

Twelve models of wind energy generators are considered (see Table 3). The models chosen represent different ranges of characteristic speeds and rated power. In addition, they have different fields of application. The models (I), (II) and (III) are small size wind turbines, suitable for low energy needs (water pumping and/or electric supply) in remote areas, although their design, performance and environmental needs are quite different. The medium size Models (IV), (V) (VI), however, is suitable for small electric networks or for grid connection and big size models (VII), (VII) (VIII).

Table 3: Main data of the twelve Wind Energy Conversion system models

Models Wind turbines Number of Blade D Pr Vc Vr Vo

(m) (kw) m/s m/s m/

Model I EW50 3 15 50 4.00 11.3 25

Model II BWCXL.50 3 14 50 2.50 11.0 25

Model III PGE50 3 20 50 3.00 11.0 25

Model IV Notanck150 3 24.5 150 4.00 12.0 25

Model V Norwin150 3 24.6 150 4.00 12.3 25

Model VI Ades wind turbine 200 1 30 200 4.00 11.7 25

Model VII Bonus1300 3 62 1300 3.00 16.0 25

Model VIII Nordex70 3 70 1500 4.00 13.0 25

Model IX BHD FL-1000 IEC IIA GL IIA 3 55 1000 3.50 13.5 25

6. Discussion of results

Fig. 2 and Fig. 3 show the capacity factor and the site effectiveness, i.e., which is contained in all other quantities, depends only on kinematics parameters, concentrated mainly on the effect of Vc and Vr.

Fig. 2 shows the capacity factor and the site effectiveness versus cut-in speed at constant Vr and the Fig. 3 displays their variation according to the rated speed Vr at constant cut-in speed Vc.

Capacity Factor and Site effectiveness versus cut-in speed at constant rated speed

¡3 04

1 ¿> 0.2

Capacity Factor ■ Site Effectiveness

2 3 4 5 6 7 8 cut-in speed m/s

Fig.2: Capacity factor and the site effectiveness

versus cut-in speed at Vr= constant

Capacity Factor and Site effectiveness versus rated speed at constant cut-in speed

Capacity Factor Site Effectiveness

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1

6 8 10 12 14 16 18 20 Rated speed m/s

Fig.3: Capacity factor and the site effectiveness versus rated speed at Vc= constant

Table 2 collects the main wind data for representative and promising sites of the four regions of Algeria. In Its two figures indicate that the capacity factor obey to a decreasing law in both supposed cases. Capacity factor versus cut-in speed at constant Vr, its maximum is gotten for the low value of Vc and for the versus rated speed at constant Vc its

optimum is obtained for the low value of Vr. But the site effectiveness follows a parabolic law, i.e., its curve passes by a maximum corresponding to a single value of Vc at a constant speed Vr while its variation according to Vr at a constant Vc has an increasing form which admits a horizontal asymptote. this table, the yearly mean velocity at a various heights and the density of average power available of the wind of each site computed directly by the wind data are presented in Table1. It can be seen that the yearly mean wind speeds at 24m elevation in some locations could reach as high as 6.10 and 5.30, 7.2 m/s in regions C, and 7.18, 6.22, 6.86, 6.17 m/s in regions (D) respectively. However, the sites with high energy level, with available energy flux proximate or higher than 2000 KW/m2/year, are limited to the locations; C03, D01, D02, D03, in the majority are located in the Sahara.

Selecting the appropriate machine to a given site leads to identify its speeds characteristic. The identification of Vc, Vr and Voff is made by plotting the capacity factor and sites effectiveness curves (example Figs.4).

The capacity factor and the site effectiveness of the representative promising sites against cut-in speed aggregated by model of wind turbine conversion system (WECS) is presented in Fig. 4. The operative data of each model are indicated in the captions. It is obvious that choosing the bad model for a site can be very penalizing. Therefore, the selection of WECS model is based on the capacity factor or the output energy.

For example, at site D01 model (VIII) of ^r = 0.2792 and ^max=0.3692, CF= 0.3277 and model (IX) of ^r = 0.2884 and ^max=0.3674 achieves s = 0.7262 and CF= 0.3568, Fig.4.

Capacity Factor of the Sites

Sites Effectiveness

LL 0.3-

Model VII ■ ä Vc=4m/s «0.25- Vr=12m/s

Model IX ■■■■ Vc=4m/s Vr=11 7m/s

0.8 g °.7

Model VIII -—

Vc=4m/s

Vr=12.3m/s

Hub height 24m

^ ~ ■ _

..... D06

. JSf Jr'C03

Model VII—

Vc=4m/s

Vr=12m/s

. jf Model VIII— Model IX....... .

r Vc=4m/s Vc=4m/s

Vr=12.3m/s Vr=11.7m/s ,

3 4 5 6 Cut-in wind speed Vc m/s

3 4 5 6 Cut-in wind speed Vc m/s

Fig.4: Capacity factor and site effectiveness of promising sites versus cut-in speed for the nine models of WECS.

Tables 4: The results

Model (I) Model (II) Model (III)

0.3400 0.5955 0.2808 0.7677 0.3262 0.6703

The best results

Sites C01 D01 D04 D02 (50 m)

Model (IV) CF 0.3310 0.3422 0.2805 0.3093

£ 0.5612 0.7368 0.7346 0.7695

Model (V) CF 0.3205 0.3278 0.2686 0.3119

£ 0.5730 0.7463 0.7404 0.6974

Model (VI) CF 0.3434 0.3568 0.2930 0.3380

£ 0.5487 0.7262 0.7246 0.6753

Sites C03 D01 D04

Model (VII) CF £ # # 0.6400 0.2797 # #

Model (VIII) CF £ 0.3725 0.5681 0.3860 0.7268 0.3145 0.7376

Model (IX) CF £ 0.3634 0.5389 0.3721 0.6875 0.3061 0.6981

of the nine wind

turbines models paired with the promising sites are listed in the Table 4, which are shown on the figures Fig.7a, These figures present the capacity factor and site effectiveness of promising sites versus cut-in speed for the nine models of WECS.

The tables and the figures (Fig.4) display that the models (IV) and (V) perform suitably with the sites. But the medium wind turbines models (VII) and (VIII) are matched with all the sites C01, D01 and D04.

7. Conclusion

The capacity factor and the site effectiveness range between the minimum and the maximum values according to the cut in speed and rated speed of the machine, which could be selected.

This study is based on the yearly data of wind at 10m elevation, from these data; the wind potential for the four regions in Algeria has been broadly assessed. The estimate of this potential to the different locations is determined by the evaluation of the available average power and the available average energy flow on the sites. On the other hand the index used for pairing of a wind turbine and site is the yearly capacity factor and site effectiveness and energy efficiency achieved in all the sites, this study has led to the following conclusions:

The site D02 and D03 possess a medium wind deposit described by the moderate parameters of Weibull probability density function and yearly mean velocity (see Table1 and 2). These can shelter wind turbines of typical rated power values of up to 30 to 50kw or more at 24m elevation, if its speeds specific, cut-in speed and rated speed takes one value of the values given in the Table 5, supply the best pairing factor CF.

For the locations of Tiaret (C03) and Adrar (D01), these sites possess a strong wind potential, which is shown by the best parameters of Weibull probability density function and their yearly mean speed are between 6.96 and 7.2m/s at 24 m elevation, whereas at 70 m elevation the yearly mean speed reach between 7.61 and 8.18 m/s (see Tablel and 2). The wind turbines could be installed on these sites can have typical rated power values of up to 150 to 200kw (Table 4 and Fig. 4) at an elevation of 24m, which deliver a best index matching site and wind turbine if the speeds characteristic belong to the interval given at Table 4.

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