International Journal of Scientific & Engineering Research, Volume 5, Issue 7, July-2014 1011

ISSN 2229-5518

A Novel Method of Optimization and Matching

Generation of Photovoltaic Modules and Wind

Turbines Models using Matlab

Abdelrahman Atallah Z. Saleh, Loai S. Nasrat, Barakat M. Hasaneen, Ahmed F. M. A. Elbendary

ABSTRACTThis paper presents the site matching with given data about the percentage of availability of renewable energy resources in the selected site to choose the optimum and the best suitable wind turbines and photovoltaic modules for this site in Egypt using different techniques such as capacity factor (CF) and turbine selection index (TSI), programs used (Matlab). The selected site is "Qena Al -Gadida" City, New Urban Communities Authority, Egypt.

Index Terms— Matlab, Simulations, Models, Generation, Matching, Wind, Turbines, Photovoltaic, Modules, TSI, Capacity factor, Method, Site, City.

—————————— ——————————

1. INTRODUCTION

HE New Cities of Egypt represent a major effort to redistribute investment and population away from Cairo and the Delta in a brave attempt to use desert
land, the biggest challenge is the provision of electricity
and water facilities for new cities. With the growing
energy demand and environmental awareness, wind power is being regarded as one of most important
alternative energy resources [5,17], The beneficial characteristics of wind power include clean and inexhaustible fuel, local economic development, modular and scalable technology, energy price stability, and reduced reliance on imported fuels [3]. Photovoltaic (PV) generation involves the direct conversion of sunlight into electrical energy. In recent years it has proved to be a
cost-effective method for generating electricity with
minimum environmental impact. Due to the
environmental and economic benefits PV generation is
now being deployed worldwide as an embedded renewable energy source and extensive research is being performed about this [11]. The selected site is "Qena Al- Gadida" City, New Urban Communities Authority, Egypt.

————————————————

Abdelrahman Atallah Z. Saleh, Department of Electrical Power and Machine, Faculty of Engineering, Alazhar University- Qena Al-Gadida City, New Urban Communities Authority- Qena, Egypt- abdelrahmanatallah2012@gmail.com

Loai S. Nasrat, Department of Electrical Power and Machine, Faculty of

Engineering, Aswan University- Aswan, Egypt.

Barakat M. Hasaneen, Department of Electrical Power and Machine, Faculty of

Engineering, Alazhar University- Cairo, Egypt.

Ahmed F. M. A. Elbendary, Department of Electrical Power and Machine,

Faculty of Engineering, Helwan University- Cairo, Egypt.

2. OPTIMIZATION OF WIND TURBINES Matching site data with wind turbine generator is an important problem. If the generator rated speed is chosen to be low, the site loses too much of the energy in the

higher wind speed intervals. If the generator rated speed is too high, the turbine will seldom operate at low capacity and the capital cost will be high. So this paper introduces a novel method of matching the wind turbine
generators to a specific site data using normalized powers and capacity factor curves termed the Turbine Selection Index. The new matching technique identifies optimum turbine speed parameters, such as rated speed, cut-in speed, cut-out speed, and rated power to maximize the energy production [3,5,6,9]. The new technique was applied on the selected city in Egypt "Qena Al-Gadida" to evaluate the validity of the method using MATLAB program.
Equations of Simulations of Wind turbines [4, 8]
p = ∫ p f(v)dv (1)
Where f(v) is a probability density function of wind
speeds.



f(v) = . ( ) . exp (− ( ) ) (2) Using the model of p ,
p = ∫ (a + bv ) f(v)dv + ∫ p f(v)dv (3)

p = p { − e } (4)

p = 0.5 η ρ Α v { − e } (5)
Normalizing the p equation we will get:-

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p = = ( ) ∗ CF (6)

.


( ) ( )

3. RESULTS AND DISCUSSION OF WIND TURBINES

Table (1) represents the monthly averaged wind speed

CF = {

( ) ( )


− e ( ) } (7)
of Qena Al-Gadida city [1] and table (2) represents the
weibull parameters [2]. Fig. (1) Shows Normalized power
(Pn) and Capacity factor (CF) curves of Qena Al-Gadida
v = normalized speed ∗ c (8)
v = δv (9)
v = γv (10)
p = p ∗ CF (11)
Energy = p (time) (12)
where Pn(max) = 1.415 at normalized speed= 2.443 and
CF(max)= 0.5893 at normalized speed= 0.733. Fig.(2)
shows the Turbine Selection Index (TSI) curve where
TSImax= 0.4928 at normalized speed= 1.222.Table (3)
represents the simulation results of wind turbine parameters for Qena Al-Gadida .Fig. (3) Shows the Energy curve.

TSI =

(13)

TABLE(1): MONTHLY AVERAGED WIND SPEED (M/S) OF QENA AL-GADIDA

Month

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Annual

Average

Wind Speed (m/s)

4.47

4.50

5.05

5.17

5.19

5.58

5.19

5.08

5.08

4.97

4.36

4.36

4.91

TABLE(2): WEIBULL PARAMETERS OF QENA AL-GADIDA

City

Weibull parameter (c)

Weibull parameter (k)

Qena Al- Gadida

4.0925

2.23

Fig. (1) Normalized power (Pn) and Capacity factor (CF) curves of Qena Al-Gadida

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Fig. (2) Turbine Selection Index (TSI) curve of Qena Al-Gadida

Fig. (3) Energy curve of Qena Al-Gadida

TABLE(3): THE SIMULATION RESULTS OF W IND TURBINE PARAMETERS FOR QENA AL-GADIDA

Normalized

speed

Cut in

speed m/s

Rated

speed m/s

Cut

out speed m/s

Rated

power

KW

Average

power

KW

Capacity

factor

Normalized

power

Output

energy

MW.hr

1.222

1.35

5.00

8.60

4.80

2.26

0.47

0.8657

19.80

A number of wind turbines were tested using previous technique as shown in table (4) and the turbine with the highest TSI was BWC Excel-R wind turbine [7,17].

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TABLE (4): TURBINE SELECTION INDEX (TSI) FOR VARIOUS W IND TURBINES OF QENA AL-GADIDA

Turbine

Rated speed(m/s)

Cut-in speed (m/s)

Cut-out speed (m/s)

Rated

Power

Capacity

Factor

Normaized Average Power

TSI

BWC Excel-R

13

2

25

7.5

0.072

1.667

0.1526

BWC Excel-S

16

3

25

10

0.035

1.52

0.0632

BWC XL.1

13

4

22

1

0.0419

0.97

0.0385

Encron E33

13

3

26

330

0.0584

1.35

0.0881

Entegrity EW-15

16

5

25

50

0.0153

0.65

0.0084

Fuhrlinder 100

14

3

25

100

0.0487

1.4

0.0782

Fuhrlinder 250

21

3

25

250

0.0182

1.77

0.0416

Fuhrlinder 30

16

3

23

30

0.0351

1.51

0.0632

GE 1.5sl

12

4

25

1500

0.0514

0.93

0.0446

Northern power NW 100/19

13

4

25

100

0.0419

0.97

0.0385

PGE 11/35

15

4

25

35

0.0292

1.0394

0.0300

PGE 20/25

12

4

25

25

0.0514

0.93

0.0446

SW AIR X

11

4

18

0.4

0.0644

0.903

0.0528

SW Sky stream 3.7

11.5

3.5

25

1.8

0.0684

1.097

0.0749

SW Whisper 100

14.5

3.5

24

0.9

0.0383

1.231

0.0503

SW Whisper 200

13

3.5

24

1

0.0502

1.164

0.0604

SW Whisper 500

12.5

3.5

24

3

0.0554

1.142

0.0646

Vestas V82

13

4

25

1650

0.041

0.97

0.0385

WES 18

16

4

20

80

0.025

1.07

0.0269

WES 30

15

4

20

250

0.029

1.04

0.0300

WES 5 Tulipo

11

3

20

2.5

0.0883

1.239

0.1166

The BWC Excel-R turbine is rated at 7.5 KW at 13 m/s. The area is 38.465 m2. To compute the rated overall efficiency at the rating and standard conditions [8]:

At standard conditions, = 0.647 (14)
Excel program was used to calculate these equations for each turbine and to calculate the average values of "rotor diameter" and "efficiency" of all wind turbines as shown
in table (5) to be used in Eqn (5). Also δ and γ for each
turbine and the average values of them were calculated as
shown in table (6) to be used in Eqn (9), (10).

[ ]

o=


= . = . =

[ ] ∗ ∗

. ∗ . ∗ ∗

. = 0.137170648 (15)

.

TABLE (5): CALCULATION OF THE AVERAGE OF "ROTOR DIAMETER" AND "EFFICIENCY" OF WIND TURBINES OF QENA AL-GADIDA

Turbine

Rotor

diameter(M)

Sweept

area (M2)

Rated

speed(m/s)

Rated

Power(KW)

Wind Rated

Power(KW)

Efficiency

ηo

BWC Excel-R

7

38.465

13

7.5

54.67642044

0.137170648

BWC Excel-S

23

415.265

16

10

1100.49876

0.009086789

BWC XL.1

2.5

4.90625

13

1

6.974033219

0.14338905

Encron E33

33.4

875.7146

13

330

1244.7924

0.265104446

Entegrity EW-15

15

176.625

16

50

468.076032

0.106820253

Fuhrlinder 100

21

346.185

14

100

614.6057711

0.162705924

Fuhrlinder 250

29.5

683.14625

21

250

4093.321472

0.061075096

Fuhrlinder 30

13

132.665

16

30

351.5771085

0.085329788

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GE 1.5sl

77

4654.265

12

1500

5203.542738

0.288265145

Northern power

NW 100/19

19

283.385

13

100

402.8201587

0.248249741

PGE 11/35

11

94.985

15

35

207.4116206

0.168746572

PGE 20/25

20

314

12

25

351.057024

0.071213502

SW AIR X

1.15

1.0381625

11

0.4

0.894020904

0.447416831

SW Sky stream

3.7

3.7

10.74665

11.5

1.8

10.57476942

0.170216477

SW Whisper 100

2.1

3.46185

14.5

0.9

6.828361949

0.131803206

SW Whisper 200

2.7

5.72265

13

1

8.134512346

0.122932999

SW Whisper 500

4.5

15.89625

12.5

3

20.08764404

0.149345538

Vestas V82

82

5278.34

13

1650

7502.943898

0.21991368

WES 18

18

254.34

16

80

674.0294861

0.11868917

WES 30

30

706.5

15

250

1542.731063

0.16205028

WES 5 Tulipo

5

19.625

11

2.5

16.90020613

0.14792719

Average

20.02619048

0.162735825

TABLE (6): CALCULATION OF Δ AND Γ OF WIND TURBINES OF QENA AL-GADIDA

Turbine

Rated speed(m/s)

Cut-in speed

(m/s)

Cut-out speed

(m/s)

δ

γ

BWC Excel-R

13

2

25

0.153846154

1.923076923

BWC Excel-S

16

3

25

0.1875

1.5625

BWC XL.1

13

4

22

0.307692308

1.692307692

Encron E33

13

3

26

0.230769231

2

Entegrity EW-15

16

5

25

0.3125

1.5625

Fuhrlinder 100

14

3

25

0.214285714

1.785714286

Fuhrlinder 250

21

3

25

0.142857143

1.19047619

Fuhrlinder 30

16

3

23

0.1875

1.4375

GE 1.5sl

12

4

25

0.333333333

2.083333333

Northern power NW 100/19

13

4

25

0.307692308

1.923076923

PGE 11/35

15

4

25

0.266666667

1.666666667

PGE 20/25

12

4

25

0.333333333

2.083333333

SW AIR X

11

4

18

0.363636364

1.636363636

SW Sky stream 3.7

11.5

3.5

25

0.304347826

2.173913043

SW Whisper 100

14.5

3.5

24

0.24137931

1.655172414

SW Whisper 200

13

3.5

24

0.269230769

1.846153846

SW Whisper 500

12.5

3.5

24

0.28

1.92

Vestas V82

13

4

25

0.307692308

1.923076923

WES 18

16

4

20

0.25

1.25

WES 30

15

4

20

0.266666667

1.333333333

WES 5 Tulipo

11

3

20

0.272727273

1.818181818

Average

0.263507462

1.736508589

4. OPTIMIZATION OF PHOTOVOLTAIC MODULES

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A methodology for the selection of the optimum photovoltaic module for a specific site is developed. The selection is based on the capacity factors (CF) of the available PV modules. The PV module with the highest capacity factor is the optimal and recommended PV module for the selected site, this is through three steps [10, 12, 14].

4.1. Fitting the irradiance data

To fit the irradiance data, three probability density functions were chosen: Beta, Weibull and Log-Normal. The three PDFs will be tested using Kolmogorov-Smimov tests goodness of fit (KS test) for the best fit.

4.2. Calculation the Average Output Power

The output power of the module is a product of the output voltage and the output current.
p(s) = v(s). i(s) (16)
S – Value of insolation.
The average power output from a PV module is the
power produced at each insolation level multiplied by the probability of the insolation experienced and integrated over all possible insolation spectrum. In the integral form, the equation is:
pa = ∫ p(s). f(s). ds (17)
The function f(s) is the probability density function
chosen.

4.3. Calculation the capacity Factors

Capacity factor can be defined as the ratio between average power output given by (17) and rated power of
the considered module Pr:

cf = ∫ p(s). f(S). ds (18)

5. RESULTS AND DISCUSSION OF

PHOTOVOLTAIC MODULES

KS test identified that Lognormal and Weibull make a good fit for the insolation data, Weibull distribution was selected to represent the PDF of the insolation data. Table.(7) represents Monthly Averaged Insolation data (kWh/m2/day) of Qena Al-Gadida [1]. Fig. (4) shows Probability density Function (PDF) of the insolation data of Qena Al-Gadida.

TABLE (7): MONTHLY AVERAGED INSOLATION DATA

(KWH/M2/DAY) OF QENA AL-GADIDA

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Fig. (4) PDF of the insolation data of Qena Al-Gadida

The capacity factors of various modules were calculated using Matlab Program for Qena Al-Gadida as shown in table (8). KD140SX- UFBS module was chosen that has the highest value of capacity factor [15, 16].
I-V and P-V characteristics of the pv module " KD140SX- UFBS" simulated using Matlab and represented in fig.5 and fig.6. The increasing of irradiation leads to the increasing of the open circuit voltage logarithmically and
the increasing of the short circuit current linearly [11, 13].

TABLE (8): CAPACITY FACTORS OF VARIOUS PV MODULES FOR QENA AL-GADIDA

Number

Module

Rated

Power

Voc

Isc

Vmp

Imp

Capital

Cost

($)

CF

1

Helioss 6T

250

37.40

8.72

30.30

8.22

333

0.7072

2

CHSM

6610P-250

250

38.19

8.65

30.30

8.27

265

0.7159

3

EP125M/72-

190

190

44.83

5.749

36.11

5.325

233

0.7006

4

ETP660245B

245

37.27V

8.73A

30.14

8.13

258

0.7061

5

Sanyo

HITN225A01

225

53

5.66

43.4

5.21

690

0.6910

6

KD140SX- UFBS

140

22.1

8.68

17.7

7.91

300

0.7170

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7

Mono X

LG250S1CG2

250

37.1

8.76

29.9

8.37

410

0.7155

8

Sharp ND-

240QCJ

240

37.5

8.75

29.3

8.19

330

7807.0

9

SW 130

poly R6A

130

21.5

6.36

17.4

5.74

277

0.5395

10

SW-S85P

85

22

5.4

17.4

4.9

340

7807.0

11

Trina 230,

TSM-PAO5

230

37

8.25

29.8

7.72

360

780700

Fig.(5) I-V characteristics of Qena Al-Gadida module "KD140SX- UFBS" with various irradiances

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Fig.(6) P-V characteristics of Qena Al-Gadida module "KD140SX- UFBS" with various irradiances

6. CONCLUSION

The selection of proper and optimal system components is mainly based on the weather data and maximum capacity of components. This paper has presented a novel method of matching to generate electricity using renewable energy resources on the site, especially the wind energy and the solar energy. The new matching technique has used to select the optimum wind turbine
for Qena Al-Gadida, the turbine with the highest TSI was
BWC Excel-R wind turbine. Similarly, the optimum PV module is selected based on the capacity factor technique to match the site, the module that has the highest capacity factor was KD140SX- UFBS photovoltaic module.

7. ACKNOWLEDGMENT

Our thanks to Dr. Arch. Hend Farouh, Head of the Central Unit of Environmental Affairs, New Urban Communities Authority, NUCA, for her encouragement.
Our thanks to head and vice head of "Qena Al-Gadida" city, for their encouragement.

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