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
—————————— ——————————
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.
————————————————
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.
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:-
IJSER © 2014
International Journal of Scientific & Engineering Research, Volume 5, Issue 7, July-2014 1012
ISSN 2229-5518
p = = ( ) ∗ CF (6)
.
( ) ( )
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
IJSER © 2014
International Journal of Scientific & Engineering Research, Volume 5, Issue 7, July-2014 1013
ISSN 2229-5518
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].
IJSER © 2014
International Journal of Scientific & Engineering Research, Volume 5, Issue 7, July-2014 1014
ISSN 2229-5518
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 |
IJSER © 2014
International Journal of Scientific & Engineering Research, Volume 5, Issue 7, July-2014 1015
ISSN 2229-5518
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 |
IJSER © 2014
International Journal of Scientific & Engineering Research, Volume 5, Issue 7, July-2014 1016
ISSN 2229-5518
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].
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.
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.
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
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
IJSER © 2014
International Journal of Scientific & Engineering Research, Volume 5, Issue 7, July-2014 1017
ISSN 2229-5518
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 |
IJSER © 2014
International Journal of Scientific & Engineering Research, Volume 5, Issue 7, July-2014 1018
ISSN 2229-5518
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
IJSER © 2014
International Journal of Scientific & Engineering Research, Volume 5, Issue 7, July-2014 1019
ISSN 2229-5518
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.
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.
[1] NASA Surface Meteorology and Solar Energy [Online].
Available at http://www.nasa.gov.
[2] National Renewable Energy Laboratory (NREL ) Available at www.nrel.gov.
[3] Radwan .H Abdel-Hamid, Maged A. Abu Adma, Ashraf A.
Fahmy, and Sherief F8 Abdel Samed ‘Optimization of Wind
Farm Power Generation Using New Unit Matching Technique’
2009 7th IEEE International Conference on Industrial
Informatics.
[4] Burton, Tony, David Sharpe, Nick Jenkins, and Ervin Bossanyi
‘Wind Energy Handbook8’ John Wiley & Sons Ltd, West
Sussex,England, 2001.
[5] Shyh-Jier Huang, Senior Member, IEEE,and Hsing-HoWan
‘Determination of Suitability Between Wind Turbine
Generators and Sites Including Power Density and Capacity Factor Considerations’ IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 3, NO. 3, JULY 2012.
[6] Suresh H. Jangamshetti, Student Member, IEEE and V.
Guruprasada Rau, Senior Member, IEEE ‘Normalized Power
Curves as a Tool for Identification of Optimum Wind Turbine
Generator Parameters8’ IEEE TRANSACTIONS ON ENERGY
CONVERSION, VOL. 16, NO. 3, SEPTEMBER 2001.
[7] Wind turbine manufacturer [Online]. Available at http://msmelectric.com/montage/catalog/.
[8] Dr8 Gary L8 Johnson ‘Wind Energy Systems’, 27708
[9] M. H. Albadi, Student Member, IEEE, and E. F. El-Saadany, Senior Member, IEEE ‘Wind Turbines Capacity Factor Modeling’ IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 24, NO. 3, AUGUST 2009.Ding, W. and Marchionini, G.
1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
[10] Ziyad M8 Salameh ,Bogdan S8 Borowy ‘Photovoltaic Module-
Site Matching Based on the Capacity Factors8’ June 1995, IEEE.
[11] Dr. Ibrahim, A. M, Dr. Abdel-Aziz, M. M, Faisal Ahmed Ali Al- Kandari, 'Simulations of Photovoltaic Systems using Matlab / Simulink'. March 2009, faculty of engineering, cairo university.
[12] S. Sheik Mohammed, "Modeling and Simulation of Photovoltaic module using MATLAB/Simulink", International Journal of Chemical and Environmental Engineering, October
2011, Volume 2, No.5.
IJSER © 2014
International Journal of Scientific & Engineering Research, Volume 5, Issue 7, July-2014
ISSN 2229-5518
1020
[13] Abdulaziz Ahmed Abdulla, Dr Abdel-Aziz M M, Dr Ibrahim A M, 'Simulations of Photovoltaic Systems by using PSPICE'. Msc Thesis 2009, faculty of engineering, cairo university.
[14] Brian Severson and Aaron St. Leger, Senior Member, IEEE,
'Feasibility Study of Photovoltaic Panels in Mihtary Temporary
Housing Structures'. 2013 IEEE Green Technologies
Conference.
[15] Solar Electricity Handbook- 2013 Edition: A Simple Practical Guide to Solar Energy - Designing and Instalhng Photovoltaic Solar Electric Systems.
[16] Photovoltaic panels manufacturer [Onhne]. Available at http:// www.wholesalesolar.com/products.
[17] El Badawe, M.; Iqbal, T.; Mann, G.K., "Optimization and a comparison between renewable and non-renewable energy systems for a telecommunication site," Electrical & Computer Engineering (CCECE), 2012 25th IEEE Canadian Conference on
, vol., no., pp.1,5, April29 2012-May 2 2012.
IJSER © 20 14 http /lwww .qser.org