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International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2016 All rights reserved

60

OPTIMIZATION OF DESIGN PARAMETER OF HYBRID SINGLE CHANNEL PHOTOVOLTAIC THERMAL

MODULE USING G.S.A

1

PREETI,

2

MD RIZWAN SAIFEE

1M.Tech Scholar(Power Electronics & Drives), SWAMI VIVEKANAND SUBHARTI UNIVERSITY

2Asst.Prof ,Department Of Electrical And Electronics Engineering, SWAMI VIVEKANAND SUBHARTI UNIVERSITY

Email: 1 preetisinghal83@gmail.com ,2mdrizwan008@gmail.com

ABSTRACT:

For reducing the gap between demand and supply of electricity generation many non conventional research community and industry has major concentration on the new advancement and improvement on power generation and their efficiencies. Photovoltaic voltaic thermal module system is one of the application of non conventional energy resources. Solar energy is consumed by PV/T system to generate the electricity and useful thermal energy. The solar panel (PV module) approx gives electrical efficiency in range of 7% to 12% the rest of energy being dissipated in form heat losses. An attempt has been made to model and optimize the parameter of hybrid single channel PV/T module like length of the channel, depth of the channel, velocity of the fluid etc . There are many other such type of parameters were used which affect the efficiency of PV/T system. Aim of this paper is to improve the exergy efficiency by optimize some design parameter with the help of Gravitational search Algorithm and compare with genetic algorithm. All the parameters that are used in GSA are varied and some parameter like ambient temperature, solar radiations etc. Excluded from the algorithm. By comparing the other methods we found that GSA very effective technique to estimate the design parameter of hybrid single channel PV/T module.

Index terms : GSA, Exergy, Energy ,PV/T module.

Abbreviations: GSA(gravitational search algorithm) ,PV/T(photovoltaic thermal)

1.INTRODUCTION:

Todays present world of digital and electronics the demand of electricity generation is increases day by day. The fulfillment of desired electricity generation is basic requirement of any developing country. To reduced the gap between demand and supply of electricity generation many non Conventional energy resources are used these days. As conventional sources supply decrease day by day and fossil fuel like coal, petroleum and natural gas will be depleted in few hundred years. Hybrid Photovoltaic thermal module is one of the system in which solar energy is used. New advancement and improvement on power efficiency of PV system has a major focus topic by many research community and industries. . Zhang et al. (2012) and Elmonds and Smith (2011) concluded that the use of renewable energy came to existence due to lack of conventional energy resources conventional power as compare to demand, most of the use renewable energy now days. The PV/T system come into existence with an idea to utilize the thermal energy along with electrical energy. Singh et al (2012) have done comparative study of different types of hybrid photovoltaic thermal air collector and concluded that the overall annul thermal energy , exergy gain and exergy efficiency of unglazed hybrid PV/T improved by 32%,55.9%,53% respectively over convention air collector. Different types of PV modules and their applications discussed by Tiwari et al.(2011) and they explain and discuss the modeling in details. Singh et al (2013) have been done experimental work on PV/T module they have given design and simulation of intelligent control

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International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2016 All rights reserved

61

MPPT technique for PV module using

MATLAB.Lots of theoretical and experimental work

have been done on hybrid PV/T system and shown in literature.

2. SYSTEM DESCRIPTION:

In this paper a modal is proposed with a channel between PV solar cell and tedlar a schematic view of PV thermal module is shown below in fig. 1. This type of system is called single channel photo voltaic thermal (SCPVT). Agrawal and tiwari (2011) have done many series and parallel combination of

photovoltaic thermal tile which have been considered referred as Single channel Photovoltaic thermal

(SCPVT) module shown in fig.1. in order to maximum exergy efficiency different parameter of SCPVT module has been optimized using Gravitational Search Algorithm. Solar thermal energy is employed for collecting the sun‟s energy and converting it to heat energy for application of like water heating and air heating etc. The photovoltaic thermal system is based on utilization of solar energy along the electrical energy and thermal energy.

Basically a solar thermal collector the absorber a heating or heat transferring medium when solar radiation falls on the Photovoltaic module the solar energy converted in to electrical energy directly with the help of silicon cells whereas PV/T refers to integration of PV module and conventional solar thermal system in a single piece equipment. The rationale behind the hybrid concept is that a solar cell converts solar radiations. In PV module due to thermal energy the solar cells gets heated and reduces electrical efficiency because solar cells of module are made with semiconductor material. So for maintain the electrical efficiency of PV module heat removal is essential. In order to converting heat into thermal energy modal is proposed in which a channel is used below the solar panel and air is flow as flowing fluid is being used as shown in figure 1.

1. Figure1. Proposed Single channel Photovoltaic thermal (SCPVT) module

(Singh et al,2014 optimization of design parameter of hybrid microchannel photovoltaic thermal module using gentic algorithms).

Nomenclature

ASC = Area of the solar cell (m2) b = Width of the channel (m)

bm = Channel width of the module (m) d = depth of the channel (m)

Cair = specific heat of air (J/Kg K) dx = small length (m)

dt = small time (s)

h = heat transfer coefficient(W/m2K)

hSCA = heat transfer coefficients from solar cell to ambient through glass cover

(W/ K )

hSCF = heat transfer coefficients from solar cell to flowing air(fluid)( W/m2 K)

hFA = heat transfer coefficients from flowing air(fluid) to ambient( W/m2 K)

In = Incident solar Intensity( W/m2) = Thermal conductivity ( W/m2 K) L = Length of the channel (m)

= Channel length of module ( m) Nc = Number of channel SCPVTmodule TA = ambient temperature( K ) TAvg = average temperature( K ) Vair = velocity of air( m/s) Vf= velocity of fluid (air) in channel( m/s)

mf= mass flow rate of fluid (air) in channel (kg/s) Qu = useful heat( W )

η TC = efficiency at standard test condition when In

=1000( W/m) and TA = 25 (oC ) nR = number of rows in solar PVT module

β0 =Temperature coefficient of efficiency( 1/K)

= Power conversion factor , C power

Am = Area of module α = absorptivity β = packing factor η= efficiency = density( Kg/m 3 ) TFI = temperature of fluid at inlet( K ) TFO = temperature of fluid at outlet( K)

Subscripts

A = ambient; C = channel; SC = solar cell

SCA = solar cell to ambient; SCF = solar cell to fluid;

FA = fluid to ambient; T = thermal; Avg = average; TC

= test condition ; R = rows ; F = fluid ; FI = fluid at inlet; FO = fluid at outlet; eff = effective ; U = useful 3.THERMAL MODELLING

In order to write the energy balance equation of SCPVT, the following assumptions have been made:

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International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2016 All rights reserved

62

1. There is no temperature gradient along the thickness

of solar cell.

2. Heat capacity of solar cell is neglected 3. Specific heat of air remains constant.

4. The system is in quasi-steady state.

5. Packing factor is unity.

The SCPVT module is shown in fig1. The small area of SCPVT is bdx. Following Tiwari and Sodha (2006), the energy balance equation for solar cell can be written as

[Rate of solar energy available on solar a cell] = [Rate of heat loss fromtop surface of solar cell to ambient ] + [Rate of heat transfer from solar cell to flowing fluid i.e. air ] + [Rate of electrical energy produced]

cIn*bdx]=[hSCA(TSC-TA)bdx]+[hSCF(TSC-

TF)bdx]+[ηTCIn*bdx] ---(1) After solving equation 1 , we get

---(2)

Where αeff =

(

)

Energy balance for air flowing in the channel of PVT for elemental area bdx is given by –

[

]

[ ( )] [

]

hSCF(TSC - TF )bdx = mF Cair

+ hFA (TF - TA)bdx--- ---(3) Where

Putting the value of TSC in equestion (3) we get,

+

(

)-

--- ---(4)

where

and =

or

(

)

=

Solving equation (4) with initial condition at X=0 i.e TF = TF1

,

[

][ (

)] (

) ( ) At, X = L, TF = TFO ,

The outlet air temperature of single channel photovoltaic thermal (SCPVT),

[

][ (

)] (

)—( ) The average air temperature over the length of air

below channel photovoltaic thermal is obtained with the help of equation 5

T Favg =

*

+ [

(

)

]

+

[

(

)

]

---(7)

The outlet air temperature of N number of SCPVT connected in series is derivedas

T

FO

*

+ * (

)+ + T

F1*

exp

(

)---(8)

The Rate of useful thermal energy obtained for n

R

row of SCPVT module

=

(

) ( )

=

*

+

** (

)+---(10)

3.1 INSTANTANEOUS ELECTRICAL

EFFICIENCY

Electrical efficiency of solar cell depends on solar cell temperature is given by Schot(1985) and Evans (1981) and it is represented by

η = η0

[ (

)] ( )

An expression for electrical efficiency of hybrid SCPVT which is a temperature dependent is as follows

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International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2016 All rights reserved

63

η=ηTC

[

{

( )

( ) { (

)

}

{ (

)

} ( )

}]

-

--- (12)

3.2 ENERGY ANALYSIS

The energy analysis is based on the first law of thermodynamics, and total thermal gain can be represented as

=

+

---(13) Where,

=

---(14) Here is a electric power generation efficiency

conversion factor of a conventional power plant for India which is in the range of 0.20-0.40, Huang et al.(2001)

EXERGY ANALYSIS

The general exergy balance by Agarwal &

Tiwari(2010) for a Single channel PVT module is expressed as :

=

---(15) Where

= *

+ and ∑ = * + The expression for Input Exergy is given by Petela(2003) as

[ () (

) () (

) ] – ( ) The Exergy efficiency of SCPVT module given by Hepbasli (2008) as

(

) ---(17)

Design parameters of single channel PVT module is given in table 1.

4. INTRODUCTION OF GRAVITATIONAL SEARCH ALGORITHM

The gravitational search algorithm is the latest nature inspired population based stochastic search algorithm which is widely used to solve the optimization problems. E. Rashedi (2009) initially proposed gravitational search algorithm to solve the optimization problem particularly for non linear problems. The

gravitational search algorithm is based on Newton„s theory, “action at separation”.

Newton„s law of gravity states that every particle attracts another particle by means of some gravitational force .

The gravitational force between two particles is directly proportional to the product of their masses and inversely proportional to the square of the distance between them. In proposed algorithm particles considered as objects and their performance has evaluated with their masses. In GSA, each particle has associated with four specifications: particle position, its inertial mass, active gravitational mass and passive gravitational mass. The position of particles provides the solution of problem while fitness function is used to calculate the gravitational and inertial masses. Every population based algorithm has two capabilities: exploration and exploitation. This algorithm uses exploration at the beginning to avoid local optimum problem and after that exploitation. A time function named as Kbest particle/agent is used to attract other particles.

The performance of GSA is improved by controlling exploration & exploitation. The value of Kbest function decreases with time linearly and at last only one agent will be there with heavy mass that represents final solution.

Step by step procedure of Gravitational Search Algorithm is given below:

•Identification of search space.

•Generate Initial population.

• Evaluate fitness function for each particle in population.

• Update the gravitational constant value. G (t) Calculate the total force in different direction (M)  Acceleration (a) Update the particle velocity

and position.

 Velocity and position of particle is calculated by following equations: Velocity Position Stopping criteria (repeat until stopping criteria met).

Gravitational search algorithm flow chart has been shown in figure 3.

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International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2016 All rights reserved

64

Figure 3: Gravitational Search Algorithm flow chart.

5. RESULTS AND DISCUSSION

In this paper we have optimized the four parameters like depth of channel, length of channel, velocity of fluid and inlet temperature. A gravitational search algorithm is applied for present problem to determine the optimize values of the said parameter by maximizing the overall exergy efficiency. The following procedure has been follow.

 Identify the parameters on which the overall exergy efficiency depends.

 After that we have define the four parameter and their limits which are feasible in designing the module.

 Then we have found the objective functions or fitness function.

 Then we have developed a program in MATLAB software for optimizing the parameter based on the flow chart shown in figure 3.

5.1 The analysis has been done at 11:00 AM at single intensity and ambient temperature and the following results have been obtained:

Figure 4: plot of overall exergy and time of the day Input Data:

Intensity of solar light (In) - 680.73 KWH Ambient Temperature( ) - 6.60C Day Time -

11:00AM

Output Data:

The maximum overall exergy efficiency of single channel PVT module is 20.884% which is obtained at following parameters. The optimized parameters are given in table. A comparison graph shown that GSA give more exergy efficiency then Gentic algorithm.

s.no Parameter Optimized value 1 Length of the

channel(m)L

0.30000 2 Depth of the

channel(m)d

0.00100

3 Velocity of

fluid(m/s)v

1.45559 4 Temperature at

inlet(0C)

4.86086 Table1. Optimized design parameters

5.2 The analysis has been done at different time from : 08:00AM to 05:00PM at different intensity and

ttemperature at different time and the following results obtained:

Input Data : Intensity of solar Light(In)

In=[132.99, 355.56, 554.69, 680.73, 726.74, 733.85, 656.08,...

500.00, 311.46, 106.42];

Ambient temperature

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International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2016 All rights reserved

65

=[7.90, 7.90, 7.90, 6.60, 6.40, 7.70, 10.60,

13.00, 15.00,16.50];

Time=[08.00 09.00 10.00 11.00 12.00 013.00 014.00 015.00 016.00 017.00]

OUTPUT DATA: The convergence curve of The proposed gravitational search algorithm At the different intensity of time (from 08:00 AM to 05:00PM)and ambient temperature are

compared with gentic algorithm shown in figure

Figure5: comparison bar graph b/w GSA and GA.

Form the figure shown in above bar graph shows that GSA having higher overall exegy efficiency as compare to optimization done by the genetic algorithm. The optimized design parameters are given the table 2 and table 3. It clear from the table shown below that the design parameter are

approximately equals. So it is better to take the average value of the parameter for best results.

Table for average value of optimize parameter done by gravitational search and genetic algorithm respectively.

Depth ,d Length,L Velocity Of fluid,v

Inlet temp T

0.00100 0.30000 1.45559 4.86086

Table 2.Average Value of optimized parameter by using GSA

Table3.Average value of optimized parameter by using GA

Table 4 optimized parameters of the day

From the figure 6 which shows that the at peak hour 12.00 in afternoon exergy efficiency maximum. The graph is plot between the efficiency of solar cell is and number of iterations.

Figure 6: exergy efficiency v/s optimization of solar cell at 12 .00 at afternoon.

6 References:

1. Singh et al,2014 optimization of design parameter of hybrid microchannel photovoltaic thermal module using gentic algorithms.

2.Agrawal, S. and Tiwari, G.N., 2011.

Performance evaluation of hybrid modified micro-channel solar cell thermal tile: an experimental validation. International

Depth Velocity of fluid Length Temp,T

0.00100 1.45559 0.30000 4.86086

Depth,d Length,L Fluid,V Temp, T

% Time

0.00100 0.30000 0.25943 1.49816 4.92799 8:00 0.00100 0.30000 0.29444 1.50000 10.88154 9:00 0.00100 0.30000 0.29748 1.48754 16.23553 10:00 0.00100 0.30000 0.24347 1.34811 19.35030 11:00 0.00100 0.30000 0.28381 1.40254 19.55750 12:00 0.00100 0.30000 0.30000 1.50000 18.40786 13:00 0.00100 0.30000 0.23306 1.43365 18.98991 14:00 0.00100 0.30000 0.26274 1.11486 13.85664 15:00 0.00100 0.30000 0.28373 1.37271 9.97839 16:00 0.00100 0.30000 0.26377 1.16563 6.94043 17:00

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International Journal of Advanced Engineering Science and Technological Research (IJAESTR) ISSN: 2321-1202, www.aestjournal.org @2016 All rights reserved

66

Journal of Engineering Science and

Technology 3(2011) 244-254.

3.Agrawal, S. Tiwari, G.N. and Pandey, H.D., 2012. Indoor experimental Analysis of glazed hybrid photovoltaic thermal tiles air collector connected in series. Energy and Building, 53(2012) 145-151.

4.Rajoria, C.S., Agrawal S. and Tiwari, G.N.2012. Overall thermal energy and exergy analysis of hybrid photovoltaic thermal array. Solar Energ 86,1531-1538. . .

5. Singh Swati, Mathew Lini and Shimi, S.L., 2013. Design and simulation of intelligent control MPPT technique for PV module using MATLAB/SIMSCAPE.

International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering.

6. B.J. Hunang, T.H. Lin, W.C. Hung, F.S. Sun, "Performance evaluation of solar photovoltaic/thermal systems,"

Solar energy 70(5), pp.443-448.

7. Tiwari Arvind, Sodha M.S., Chandra Avinash and Joshi J.C., 2006. Performance evaluation of photovoltaic thermal solar air collector for composite climate of India.

Solar Energy Materials and Solar Cells 90 (2),175-89.

8. Tiwari Arvind and Sodha M.S., 2007.

Parametric study of various configurations of hybrid PV Thermal air collector:

Experimental validation of theoretical model. Solar Energy Materials & Solar Cells 91, 17-28

9. Esmat Rashedi, Hossein Nezamabadi- pour *,Saeid Saryazdi GSA: A Gravitational Search Algorithm Information Sciences 179 (2009) 2232–2248.

10. Zhang, X., Smith, S., Zhao, X., Xu, J., Yu, X.,2012. Review of R&D,progress and practical application of the solar,(PV/T) technologies. Renew. Sustain. Energy Rev.

16, 599–617.

11. Edmonds, I., Smith, G.,2011. Surface reluctance and conversion efficiency

dependence of technologies for migrating global warming. Renew

Energy 36, 1343–1351.

12. Singh, G.K., Agrawal, S. and Tiwari, G.N 2012. Analysis of Different Types of Hybrid photovoltaic Thermal Air Collectors: A Comparative Study. Journal of Fundamentals of Renewable Energy and Applications, doi:10.4303/jfrea/R120305

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References

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