A review on modeling, design methodology and size optimization of photovoltaic based water pumping, standalone and grid connected system

Renewable and Sustainable Energy Reviews 57 (2016) 1506–1519

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

A review on modeling, design methodology and size optimization of photovoltaic based water pumping, standalone and grid connected system Rahul Rawat n, S.C. Kaushik, Ravita Lamba Centre for Energy Studies, Indian Institute of Technology Delhi, New Delhi 110016, India

art ic l e i nf o

a b s t r a c t

Article history: Received 4 December 2014 Received in revised form 23 October 2015 Accepted 24 December 2015

Solar Photovoltaic system comprises of photovoltaic (PV) array, converter, inverter and battery storage unit of appropriate capacity to serve the load demand in reliable, efficient and economically feasible manner. The proper selection of technology and size of these components is essential for stable and efficient operation of PV system. Therefore, a number of modeling equations and methodologies for designing a PV system based on application have been developed in order to ensure the optimum performance of the PV system. In this paper, a comprehensive designing process of solar photovoltaic water pumping system, standalone PV system and grid connected PV system is presented. The modeling of PV modules, cell temperature, water pumping system and battery state of charge is tabularized so as to facilitate their utilization for proposing a PV system based on the techno-economic variables and environmental parameters. The financial and reliability parameters for techno-economic size optimization of PV system are also identified and different optimization techniques have been discussed. A state of art literature survey has been presented which is useful in designing and installation of solar PV systems for standalone as well as grid connected power supply. & 2016 Elsevier Ltd. All rights reserved.

Keywords: Photovoltaic system Standalone Grid-Connected PV Pumping System Modeling Optimization

Contents 1. 2. 3. 4. 5. 6.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modeling of solar radiation and photovoltaic modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Designing solar photovoltaic water pump . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Standalone photovoltaic system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Grid connected photovoltaic system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Optimum sizing of photovoltaic systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1. Intuitive methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2. Analytical methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3. Numerical methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4. Intelligent methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction The electricity is the most prominent form of final energy being used in the modern world. Therefore, the electricity demand has been increasing worldwide continuously at the rate of 3% per year n

Corresponding author. E-mail address: [email protected] (R. Rawat).

http://dx.doi.org/10.1016/j.rser.2015.12.228 1364-0321/& 2016 Elsevier Ltd. All rights reserved.

1506 1508 1511 1511 1511 1512 1514 1514 1515 1515 1515 1516

primarily due to the expanding industrialization, multiplying population and day by day improvement in human comfort level [1]. The electricity shortage has become a major issue worldwide especially in developing and under developed countries. In order to fulfill the electricity demand, the worldwide electricity generation has been increased from 6144 TW h to 23391 TW h respectively from year 1973 to 2013 [2]. The electricity generated from fossil fuel based power plants were dominant in year 2013

R. Rawat et al. / Renewable and Sustainable Energy Reviews 57 (2016) 1506–1519

Nomenclature Ai bf C c1 ; c2 ::: cf CS df DPP E Fb Fe FR G G gf H hp I kB kf ki kr LLP LPSP LUCE m n NPC NPV P pf Q q r R S SOC SPP t T U v V W X x,x1,x2

Anisotropic index Horizon brightening factor Cost Constants Correction factor Capacity shortage Derating factor Discounted payback period Energy Back panel surface configuration factor Back panel surface emissivity factor Collector heat removal factor Irradiance Average irradiance Geometric factor Head Penalty factor due to tedlar through glass, solar cell and encapsulation Current Boltzmann Constant Flux coefficient Clearness index Ross coefficient Loss of load probability Loss of power supply probability Levelized unit cost of electricity Meteo parameter Pump speed Net present cost Net present value Power Packing factor Volume flow rate Electric charge Discount rate Resistance Fraction of shaded series modules State of charge Simple payback period Time Temperature Heat transfer coefficient Wind velocity Voltage Battery capacity Fraction of shaded parallel strings constants

Greek Letters

α β ε η θ θZ κ

Absorptivity Tilt angle Emissivity Efficiency Incidence angle Zenith angle Self-discharge rate

and holds about 67.2% share of the gross world electricity production which gives rise to the environmental pollution. Therefore, in order to generate electricity with the environmental constraints, the renewable energy sources have been

ξ μ ϕ τ ρ σ ω Δ

1507

Back emf Temperature coefficient Permanent magnetic flux Transmittance Reflectivity Stefan Boltzmann constant Solar radiation coefficient Difference

Subscript 0 a b B back bat ch cond cvb cvf d D def dis ex F front g g-td H I inv L load life m max min mp NOCT norm oc OM P PV RD ref s sc sky st T td th tot TS V

Initial condition Ambient Beam Annual benefit Back surface Battery Charging Conductive heat transfer Convective heat transfer from back surface Convective heat transfer from front surface Diffuse Diode Deficit Discharging Excess Annual financing cost Front surface Ground Glass to tedlar Horizontal surface Current Inverter Loss Load Lifetime Motor Maximum Minimum Maximum power point Nominal operating cell temperature Normalized Open circuit Annual operation and maintenance Power Photovoltaic module/cell Reference device Reference condition Series Short circuit Sky Storage tank Tilted surface Tedlar sheet Thermal Total Test specimen Voltage

increased attention of researchers, governments as well as industrialists [3]. It causes an annual worldwide growth of about 2.2% in renewable energy market since 1990 to 2013 and out of which the solar photovoltaic (PV) power generation shares an annual growth

1508

R. Rawat et al. / Renewable and Sustainable Energy Reviews 57 (2016) 1506–1519

Fig. 1. Power flow diagram of photovoltaic system.

of about 46.6% [4]. The solar PV electricity generation is one of the promising options as solar radiation is in abundance, cost of electricity generation is reducing rapidly, requires negligible maintenance and provides highly reliable energy [5,6]. The total global installed capacity of the solar PV was about 177 GW in year 2014 which contributes about 1% of the global electricity generation [7]. The PV cell is a semiconductor device which directly convert the solar radiation into electrical energy. There are different types of solar PV cells available in market i.e. mono-crystalline silicon, multicrystalline silicon, amorphous silicon, cadmium telluride, micromorph, CIGS, hetero-junction modules etc. Initially the PV systems were employed in satellites but now it is being used for diversified applications ranging from very low energy consuming appliances such as watches, calculators to megawatt scale grid connected solar PV plant [8]. A single PV cell is used in calculators while a number of PV cells are connected in series to make a PV module in order to be utilized in solar lighting system and other small load applications [9]. Further, a number of PV modules are connected in series and/or parallel to make PV array for large standalone and grid connected power generation plant. Apart from PV modules; the PV systems consists of battery bank and power conditioning unit (PCU) which includes converter, inverter, maximum power point tracking (MPPT), dump load and data logger as shown in Fig. 1 [10]. The PV module is exhibits non-linear current–voltage characteristic which is influenced by load, solar radiation intensity and cell temperature. Therefore, in order to extract maximum power from PV module, an MPPT is required. The converter with charge controller is required for battery charging and inverter for AC load. The optimum sizing of these devices is important for reliable operation. Therefore, these systems need to be designed properly according to the site, land area available, load requirement, load pattern, environmental conditions and economics in order to utilize available resources efficiently and economically [11,12]. Oshiro et al. studied different design parameters and based on the practical data, calculated the parameters for Japan [13]. In this paper, a comprehensive review of design methodology of water pumping, standalone and grid connected photovoltaic system and different methods of techno-economic size optimization are presented along with the modeling equations of electrical parameters of PV cell so as to provide a basic insight as well as latest research on the topic.

2. Modeling of solar radiation and photovoltaic modules The commonly available or estimated solar radiation data is global horizontal irradiance [14]. The solar radiation intensity received by a PV module at a site depends on its orientation as well as weather conditions. Therefore, the PV modules are either installed at an inclination towards a specific direction according to the geographical site or have a mechanical sun tracking mechanism. As mechanical tracking increases the cost, maintenance and losses, the PV modules are generally fixed at yearly optimum angle or have manual adjustment for seasonal or monthly optimum angle [15]. Mousazadeh et al. has presented a review and analysis of different sun tracking methods [16]. The different models available in literature for estimating solar radiation on an inclined surface is divided into isotropic and anisotropic models [17–22]. Bakirci presented a comprehensive review of models available in literature for solar radiation analysis and estimation [23]. Yadav and Chandel has reviewed the different methods of tilt angle optimization to maximize the solar radiation incident on the PV module [24]. Tomar et al. and Kaushika et al. developed an artificial neural network model for estimating direct, diffuse and global solar radiation on horizontal and tilted surface [25,26]. In order to estimate electricity generation by a solar PV module, the optimum yearly or seasonal or monthly tilt angle of the specific site and global solar radiation on the tilted surface of PV module at the given site must be known. The hourly averaged global solar radiation at tilted solar photovoltaic module can be calculated from hourly averaged global horizontal radiation by using the relations as given in Duffie and Beckman (1980) [27]. For determining the beam and diffuse component of global radiation, the correlation based on clearness index (ki ) is being used widely so as to evaluate the hourly averaged diffuse radiation at the horizontal surface (Gd ) (Eq. (1)). Where clearness index is the ratio of solar radiation incident on the horizontal surface to that of extraterrestrial environment. The beam radiation (Gb ) can be calculated by subtracting the diffuse radiation (Gd ) from hourly average of measured global radiation at horizontal surface (GH ). Gd GH

¼

8 > < > :

for

ki r 0:22

2 3 4 0:9511  0:1604ki þ 4:388ki  16:638ki þ 12:336ki

for

0:22o ki r 0:80

0:165

for

ki 40:80

1:0 0:09ki

9 > = > ;

ð1Þ

R. Rawat et al. / Renewable and Sustainable Energy Reviews 57 (2016) 1506–1519

1509

horizon as given by Eq. (4).

Hay–Davies–Klucher–Riendel (HDKR) model is used to estimate the solar radiation on the tilted surface [28–30]. According to the HDKR model, the diffuse radiation is composed of isotropic, circumsolar and horizon brightening component. Geometric factor (gf ) is the relation of beam radiation on the tilted surface with the beam radiation on horizontal surface and can be determined by the ratio of cosine of incidence angle to that of zenith angle as given by Eq. (2). The environmental transmittance measure of beam radiation is taken as anisotropic index (Ai) as given by Eq. (3) and horizon brightening measure is taken into account by the factor (bf) which shows the concentration of diffuse radiation near

gf ¼

Ai ¼

bf ¼

cos θ cos θz

ð2Þ

Gb

ð3Þ

Gref sffiffiffiffiffiffiffi Gb

ð4Þ

GH

Table 1 Expressions for estimating power generation from solar PV module. Sr. No. 1.

Model

Ref.

    c1 I mp ¼ ½I mp;ref þ μI T PV  T PV;ref  GGrefT   c2 V mp;ref   T TPV ;ref V mp ¼ 1 þ μV ln

2. 3. 4. 5.

Gref

[36]

PV

GT

P mp ¼ GT ðc1 þ c2  GT þ c3  T a þ c4  vÞ  2 V mp ðGT ; T PV Þ ¼ V mp;ref þ c1 lnðGnorm Þ þ c2 lnðGnorm Þ þ μV ðT PV  T ref Þ   I mp ðGT ; T PV Þ ¼ c3 þ Gnorm I mp;ref þ μI ðT PV  T ref Þ V mp ðT ref Þ ¼ V mp  μV ðT PV  T ref Þ I mp ðT ref Þ ¼ I mp  μI ðT PV  T ref Þ h i   R I c1 V mp ¼ V oc 1  V norm ln c2  Vs OCmp ð1  c2  c1 Þ c2 c1 ¼ c2 þ 1

Rs I mp c2 ¼ V norm þ 1 2 V th

[37] [37] [38] [39]

I mp ¼ I sc ð1  c2  c1 Þ 6. 7. 8.

9.

10 11. 12. 13. 14. 15.

16.

17.

18. 19. 20.

 x  x P mp ¼ c1 GT þ c2 T PV þ c3 lnðGT Þ þ c4 T PV lnðGT Þ   GT P mp ¼ Gref P mp;ref þ μP ðT PV  T ref Þ  x  x P mp ¼ c1 GT þ c2 T a þ c3 lnðGT Þ þ c4 T a lnðGT Þ  x þ c5 GT expðx1 þ x2 vÞþ c6 GT lnðGT Þ expðx1 þ x2 vÞ

    I mp ¼ c1 Gnorm þ c2 G2norm I mp;ref þ μI T PV  T ref  2 V mp ¼ V mp;ref þ c3 N s V th lnðGnorm Þ þc4 N s V th lnðGnorm Þ þ μV ðGnorm Þ T PV  T ref   V mp ðT PV Þ ¼ V mp;ref þ μV T PV  T ref    GT I mp ðGT ; T PV Þ ¼ Gref I mp;ref þ μI T PV  T ref     P mp ¼ GGrefT P mp;ref þ μP T PV  T ref   0:008G2  P mp ¼ Gref T P mp;ref þ μP T PV  T ref h  i     G  GT P mp ¼ GGrefT P mp;ref þ μP T PV  T ref  P mp;ref  cf  Grefref  200

 4     GT P mp ¼ GGrefT P mp;ref þ μP T PV  T ref  P mp;ref  cf  1  1  200   hGref i I PV ¼ I ref þ μI T ref  T PV GT  i   h G V PV ¼ V ref þ μV T ref  T PV 1 þ kB Tq PV ln Gref PV     GT I PV ¼ Gref I ref þ μI T PV  T ref       V PV ¼ V ref  Rs IPV  I ref þ kB Tq PV ln GGrefT þ μV T PV  T ref h i   Isc;ref ;RD I PV ¼ I ref þ I sc;TS Isc;RD  1 þ μI T PV  T ref        V PV ¼ V ref  Rs IPV  I ref  cf  I PV T PV  T ref þ μV T PV  T ref    P mp ¼ c1  Gref 1þ c2 T PV  T ref     c3 þ Gref P mp ¼ c1 1 þ c2 T PV  T ref   P str1 ¼ P str;ref

XS 1þV

VD mp;ref

 P str2 ¼ P str;ref 1  c1 S2  c2 S

21.

X

ði Þ 

[40] [41] [42]

[43]

[44] [45] [45] [45] [45] [46]

[47]

[48]

[49] [49] [50]

ðiiÞ

P str3 ¼ P str;ref ½c3 ðS  1Þ þ Gnorm  ðiiiÞ P str ¼ MaximumðP str1 ; P str2 ; P str3 Þ      P PV ¼ GGrefT  df  P ref þ μP T PV  T ref

[33]

1510

R. Rawat et al. / Renewable and Sustainable Energy Reviews 57 (2016) 1506–1519

The hourly average global solar radiation incident on tilted surface (GT ) can be calculated by using HDKR model as given by Eq. (5) for optimum tilt angle which can be determined by varying the tilt angle from 0° to 90° using different optimization techniques [31,32].

h i 1 þ cos β GT ¼ ðGb þ Gd U Ai Þ  gf þ Gd  ð1  Ai Þ  2



 n oi 1 þ cos β 3 þ GH  ρ g  ð5Þ  1 þ bf sin β=2 2 Where β denotes the tilt angle and ρg denotes ground reflectance. The global solar radiation on the tilted PV module is used to estimate the power generation by a PV module or PV array with

the help of different modeling equations available in literature [5,8,9,33–35]. The modeling equations of photovoltaic modules being utilized for theoretical analysis are given in Table 1. The cell temperature (TPV) is one of the key parameters which affects the PV module efficiency. Skoplaki and Palyvos carried out a survey on the correlations available in literature for estimating PV cell temperature [51]. The nominal operating cell temperature (NOCT) can be estimated by the procedure as described by Garcia and Balenzategui [52]. The correlations are based on environmental parameters, NOCT, optical properties, geometrical properties and thermal properties of PV module as presented in Table 2. It is clear that power generation through PV module is influenced by several parameters which are intermittent and it also

Table 2 Correlations for estimating cell temperature of solar PV module. Sr. No. Model

Remarks

  Based on environmental parameters ¼ 1:14 T a  T ref þ 0:0175  ðGT  300Þþ 30   ¼ 1:14 T a  T ref þ 0:0175  ðGT  300Þþ 30  cf  v Based on environmental parameters Skoplaki model; For free stream wind velocity (v40) m/s. ¼ T a þ 8:910:32 þ 2vGT

1.

T PV

2.

T PV

3.

T PV

4.

0:25 T PV ¼ T a þ 5:7 þ 3:8vGT T a þ ½ðαPV  ηPV ÞGT þ ðc1 þ c2 T a Þ T PV ¼ ð17:8 þ 2:1vÞ

5. 6. 7. 8. 9. 10. 11. 12.



T PV ¼ T a þ c1 GT ð1 þ c2 T a Þð1  c3 vÞ 1 1:053  ηPV T PV ¼ 0:943T a þ 0:028GT  1:528v þ 4:3 T PV ¼ T a þ kr  GT T PV ¼

T PV ¼

 20Þ T a þ ð219  832  ki ÞðT NOCT 800 U tot T a þ GT ½ðατÞ  ηref ð1 þ μη T ref Þ U tot  μη ηref GT

Modified NOCT model

[68,69]

Mattei et al. model; Based on energy balance.

[70]

Based on environmental parameters.

[71]

Based on energy balance

[72]

Includes wind speed parameter in Ref [50]

[27]

King et al. model; Dimensionally inconsistent.

[43]

Based on Ross model Includes energy balance in Ross model.

[73] [74]

Based on environmental and optical parameters; Includes sky temperature.

[75]

½ðατÞ  ηPV ;ref GT þ T a ½hcvf þ 2σετð1 þ

cos βÞT sky T 2a þ hcvb þ 4σT 3a F e F b 

hcvf þ 2σετT 3a ð1 þ cos βÞ þ hcvb þ 4σT 3a F e F b

  ðτα  ηPV Þ U tot

GT

  0:0712v2  2:411v þ 32:96

T PV ¼ T PV ¼

τ½pf αPV þ αtd ð1  pf ÞGT þ ðU tot;f ront þ U cond;td ÞT a U tot;f ront þ U cond;td

17. 18. 19. 20.

T PV

[55] [56]

14.

T PV

[42,54]

Schott model; Based on environmental and optical parameters.

[57,58] [59–65]

T a þ GGrefT

16.

[54]

Skoplaki model; v is the wind velocity component parallel to the surface of PV array.

Servant model; c1, c2 and c3 are empirical constants; includes cell efficiency.

13.

15.

[53] [53]

Based on environmental parameters. Ross model; Estimate instantaneous value; kr depends on the module specifications and environmental conditions; kr ¼ 0.02–0.054. Ingersoll model; Predicts the average steady-state cell temperature; Includes environmental, geometrical, optical, and thermal properties. Based on energy balance

T PV ¼ T a þ T PV ¼



Ref.





   GT PV 1  ητα ¼ T a þ 5:7 þ9:5 3:8v GNOCT T NOCT  T a;NOCT h i ðT  T Þ ¼ T a þ GT eðc1 þ c2 vÞ þ PV Grefback ref

T PV ¼ T a þ 0:031GT  0:058 T PV ¼ T st þ kηth GT where; k ¼ F1RUFtotR

 εT PV 4  T 4sky GT T PV ¼ T a þ c1 þ c2 v ð1 ρÞð1  ηPV Þ  σ GT    PV  T PV ¼ T a þ GT Uταtot 1  ητα

[66] [67]

Included transmittance in Ref. [160]

[27,76]

T PV ¼ 3:12 þ 0:025GT þ 0:899T a  1:3v     U tot;NOCT  GT PV T PV ¼ T a þ GNOCT T NOCT  T a;NOCT 1 ητα U tot i h ih ðm=f pÞðT a þ T PV Þ T PV ¼ ðU tot =pf Þ 2ðm=f ð1þ YÞ1=2  1    pÞ 4 m=pf ðU tot =pf ÞT a þ ðα  ηPV;ref' ÞGT Y¼  2 ðU tot =pf Þ ðm=pf ÞðT a þ T PV Þ

 GT η0PV;ref ¼ ηPV ;ref 1þ x1 T PV;ref þ x2 ln Gref

Based on environmental parameters. Suitable for free standing PV array; Not suitable for BIPV.

[77] [27]

Based on environmental parameters and heat loss by radiation.

[78]

24. 25. 26.

T PV ¼ 30 þ 0:0175ðGT  150Þ þ 1:14ðT a  25Þ T PV ¼ T a þ c1 GT ð1 þ c2 T a Þð1  c3 vÞ   G T a þ ½α  ηPV ð1 þ μη T ref Þ U T   tot T PV ¼

Based on incident radiation and ambient temperature Servant model; Based on environmental and optical parameters. Based on environmental, geometrical and optical parameters.

[79] [56] [80]

27. 28.

T PV ¼ T a þ 0:028GT  1 h i T PV ¼ T a þ GGrefT T max eðcf vÞ þ T min þ ðT PV  T back Þref

Based on Ross model King model

[55] [81]

29. 30.

T PV ¼ 30:006 þ 0:0175ðGT  300Þþ 1:14ðT a  25Þ   GT PV T PV ¼ T a þ GNOCT ðT NOCT  T a;NOCT Þ 1  ητα    T PV ¼ T a þ GT Uαtot 1  ηPV α

Based on environmental parameters. Davies et al. model; Assumes constant heat transfer coefficient.

[82] [27,83]

21. 22. 23.

1  ηPV μη

31. 32. 33. 34. 35. 36. 37.

GT U tot

GT T a þ 800 ðT NOCT

T PV ¼  20Þ T PV ¼ T a þ 0:0155GT þ 0:7 τ½ðαpf Þ þ αtd ð1  pf ÞGT  ðηPV GT pf Þ þ U tot;f ront T a þ U cond;td T td T PV ¼ U tot;f ront þ U cond;td T PV ¼ T a þ 0:031GT T PV ¼ 3:81 þ 0:0282GT þ 1:31T a  1:65v GT T PV ¼ T a þ 800 ðT NOCT  20Þþ cðv  1Þ

Based on steady state energy balance.

[84]

NOCT model

[85–93]

Based on Ross model Based on energy balance equation of solar cell and tedlar

[94] [95]

Based on Ross model Based on environmental parameters. Based on NOCT model; Includes the impact of wind speed.

[96,97] [77] [98]

R. Rawat et al. / Renewable and Sustainable Energy Reviews 57 (2016) 1506–1519

exhibits non-linear current–voltage characteristics. Therefore, maximum power point tracking (MPPT) techniques, which is an electronic tracking based on algorithm in order to track the operating point continuously until it delivers maximum possible generated power, are used to extract maximum power [99]. Several researchers carried out comparative analysis of different and MPPT techniques and suggested incremental conductance and perturb & observe (P&O) techniques for practical implementation [100–103]. Rawat and Chandel (2013) investigated 21 different MPPT techniques and found that voltage and current controlled techniques are simple to implement, intelligent techniques are fast converging and P&O techniques are most commonly used [104]. The efficiency of a PV module can be estimated theoretically by utilizing the modeling equations based on the efficiency at standard test conditions (STC: Irradiance ¼1000 W/m2; Cell temperature ¼25 °C; Air mass ¼ 1.5) as shown in Table 3.

3. Designing solar photovoltaic water pump Solar energy is being used for water pumping either through thermal to mechanical energy conversion processes using solar collector with Rankine cycle, Brayton cycle and Stirling cycle or through solar to electric conversion process using electric motorpump set with photovoltaic, thermoelectric and thermionic option [112]. The solar photovoltaic water pumping system (SPWPS) is emerging out as best option for water pumping as it is simple to install and operate, requires low maintenance and cost effective [113,114]. It consists of PV array, motor-pump set, battery storage unit and power conditioning unit. The most commonly used motor-pump set are AC motor with centrifugal pump and DC motor with positive displacement pump [115]. These motor-pump set requires DC/AC inverter and DC/DC converter respectively in power conditioning unit along with maximum power point tracking and battery storage unit. The designing of SPWPS depends on the hydraulic load, insolation, and electrical configurations of motor-pumps set [116]. The hydraulic load relies upon the volume flow rate (Q), pump speed (n) and head (H) which are modeled by following relations [69,117,118]:

n Q ¼ Q ref  ð6Þ nref H ¼ H ref 

n nref

ð7Þ

Swami et al. developed empirical models for SPWPS components and assessed the performance of permanent magnet brushless DC motor connected to PV array [119]. Hamidat and Benyoucef proposed a mathematical model to relate electric power of motor (Pm) with volume flow rate and head as given by Eq. (8) [120]. P m ðQ ; HÞ ¼ aðHÞQ 3 þbðHÞQ 2 þ cðHÞQ þdðHÞ

ð8Þ

where aðHÞ ¼ a0 þ a1 H þ a2 H 2 þ a3 H 3 Table 3 Correlations for estimating efficiency of solar PV module. Sr. No. 1. 2. 3.

Model     ηPV ¼ ηref 1 μη T PV  T ref þ ωLog 10 ðGT Þ   1=4 1=4 ηPV ¼ ηref  22:4 T PV  273 h   μ τ αG i ηPV ¼ ηref 1  μη T a  T ref  η U tot T

1511

bðHÞ ¼ b0 þb1 H þ b2 H 2 þ b3 H 3 cðHÞ ¼ c0 þ c1 H þ c2 H 2 þ c3 H 3 dðHÞ ¼ d0 þd1 H þd2 H 2 þ d3 H 3 a0, 1, 2, 3; b0, 1, 2, 3; c0, 1, 2, 3; d0, 1, 2, 3 are constants. The motor voltage (Vm), current (Im) and torque (tr) can be determined from the following relations: V m ¼ ξ þ I m Rm tr ¼ kf ϕI m

ð9Þ ð10Þ

Where ξ is back emf, ϕ is permanent magnet flux; kf is flux coefficient and Rm is motor resistance. The size and configuration (i.e. total no. of PV modules in series and parallel combination) of PV array for water pumping system can be determined by using the empirical models of components, hydraulic load and environmental data [121,122]. Caton has studied two different methodology for designing SPWPS for a village in West Africa [123]. The author also explore the effects of fixed orientation, single axis tracking and dual axis tracking on the performance of the SPWPS and suggested that single axis tracking with monthly adjustment is preferable. Meah et al. designed a SWPS for remote location and compared it with utility and diesel generator in terms of technical, economic and environmental benefits [124]. The procedure of designing the SPWPS is depicted by flow chart as shown in Fig. 2.

4. Standalone photovoltaic system The standalone photovoltaic system (SPV) consists of a PV array coupled either directly with the AC or DC load through PCU or backed up with battery storage unit. This type of system is one of the best option for meeting electricity demands of remote region which assist in their socio-economic development [125]. In this system, the size of PV array depends on the load demand and size of battery storage unit depends mainly on the time interval for which backup is required during off sunshine hours [126,127]. The load demand is catered by PV power during sunshine hours and excess electricity (if produced after fulfilling the load demand) is stored in battery storage unit which is utilized either during the interval when load demand is higher than the electricity production through PV or during off sunshine hours [128–130]. There are several models available in literature for simulating battery charge and discharge characteristics as presented in Table 4. A charge controller is included in PCU along with MPPT for monitoring and controlling the SOC of battery storage unit [136]. The charge controller allows the system to discharge up to a certain depth of discharge and starts trickle charging after fully charged preventing it from overcharging and over-discharging. This not only increases the battery life but also improves its performance and protect it from the damage caused due to deep discharging [137]. The charge controller commands battery storage unit to operate between a maximum and minimum state of charge point. The flow chart for designing a SPV system is shown in Fig. 3

5. Grid connected photovoltaic system Ref. [105–109] [110] [111]

The grid connected PV systems feed the generated power to utility grid in place of domestic load and does not have any storage unit [138]. The elimination of storage unit reduces the system sizing complexity and economics of the grid connected PV system, but on the other hand, complications related to the power system

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Fig. 2. Design methodology of solar photovoltaic based water pumping system. Table 4 Correlations for simulating battery state of charge. Sr. No.

Model

1.

ðt Þ Δt SOC ðt þ 1Þ ¼ SOC ðt Þþ ηbat ðt ÞWIbat ðt Þ Rt 1 SOC ¼ SOC 0 þ W norm t0 ðI bat  IL Þ dt    Rt  κ SOC ¼ SOC 0 1 24 ðt  t 0 Þ þ t0 IbatWηbat dt h  i SOC t ¼ SOC t  1 ð1  κÞ þ ηch ηEex inv h  i Edef SOC t ¼ SOC t  1 ð1  κÞ  η η

2. 3. 4. 5.

Reference

inv

[131,132] [133] [134] [135] [135]

dis

devices, i.e. inverter and transformers, increase so as to reduce the harmonic distortion and match the voltage and frequency with the utility grid [139]. This system consists of PV array, DC/DC convertor with MPPT, inverter with islanding prevention and step-up transformer as shown in Fig. 4. The sizing of PV array and inverter for grid connected system depends on rated capacity of PV array at STC, geographical location, environmental conditions and losses in inverter, converter, transformer and power cables [140,141]. The inverter should suppress harmonic distortion and have islanding detection and prevention mechanism [142,143]. The PV system is considered to be islanded when it continues to supply power to a utility grid which is isolated from main power grid source. Gua et al. carried out a comprehensive review of patented anti-islanding techniques [144]. The classification of anti-islanding techniques are shown in Fig. 5. There are two types of inverters available in market i.e. thyristor based line-commutated inverter and IGBT or MOSFET based self-commutated inverter. The self-commutated inverter is prominent for grid connected PV systems as it regulate power factor, reduce harmonics distortion and maintain quality of AC waveform [145]. Testa et al. presented a methodology to select optimum step up transformer for connecting a PV system with grid utility [146]. Wong et al. has studied the voltage issues at point of common coupling (PCC) such as voltage rise, voltage unbalanced and flicker emission in 7.2 kWp grid connected systems in Malaysia and suggested approaches for voltage stabilization [147]. Gong and Kulkarni designed a 43.2 kWp rooftop grid connected PV system

for Federal office building and also presented the design methodology [148]. They also discussed the islanding and harmonic distortion problem coupled with it. Fernandez-Infantes et al. provides comprehensive design methodology with technical, economical as well as ecological parameters [149]. Notton et al. presented an energy based sizing approach for grid connected system and applied it on different locations of France [150]. The approach includes efficiency of different PV technologies, tilt angle of PV module, efficiency curve of inverter, and environmental conditions. They found that the efficiency curve is most sensitive parameter for sizing of the grid connected PV system.

6. Optimum sizing of photovoltaic systems The modeling equations, technical, economical and environmental parameters are used for size optimization of different PV configurations. The electrical output and performance of PV system depends on a number of stochastic environmental parameters, hence it is critically important to optimally size the PV system for reliable, efficient and cost effective operation. The optimization techniques for sizing PV system utilizes economic parameters and control strategies to define objective functions and constraints [151,152]. Some of the economic tools used by researchers as objective function are net present cost (NPC), net present value (NPV), levelized unit cost of electricity (LUCE), simple payback period (SPP) and discounted payback period (DPP) [153]. Soni and Gakkhar (2014) has carried out survey on the values of parameters used for economic evaluation of solar energy systems in India and suggest the methodology for selecting the correct value of these parameters [154]. NPC is the present value of all initial investment, operation and maintenance cost and financial cost [155]. NPV is the subtraction of the NPC of a PV plant from the present value of all benefits and salvage cost [156]. The positive NPV shows that the PV plant is in benefit. The LUCE is the annualized cost per kWh generation of electricity [157,158]. Branker et al. has discussed the assumptions and methodology for calculating LUCE of PV systems [159]. The SPP period is a simple financial tool which does not consider time value of money and simply calculate the time period taken to

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Fig. 3. Design methodology of standalone photovoltaic system with battery storage.

Fig. 4. Design methodology of grid connected photovoltaic system.

recoup the invested money [160]. The SPP is modified to DPP by including time value of money [161]. The formulas for calculating the above financial parameters and their utilization for optimum sizing of PV system components are given in Table 1. Some of the constraints used by researchers to define boundary conditions and reliability of the system size are battery SOC, loss of load probability (LLP), loss of power supply probability (LPSP) and

capacity shortage as shown in Table 5 [162,163]. SOC is the stored charge status of battery (Wt) with respect to the total capacity of the battery (W). LLP is the probability of number of hours of load deficit (tdef) with respect to the number of hours of the load satisfaction (tload) [164,165]. Excess energy (Eex) is the amount of energy which is lost after serving load and charging the battery to its maximum capacity. Capacity shortage (CS) is the fraction of

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Fig. 5. Classification of anti-islanding techniques.

6.1. Intuitive methods

Table 5 Optimization parameters for sizing PV system components. Definition Objective functions To minimize the NPC.

Mathematical formulae

t P

NPC ¼

t¼0

To maximize the NPV To minimize the LUCE.

C 0 þ C OM þ C F ð1 þ rÞt

NPV ¼  C 0 þ ðC B  C OM  C F Þ LUCE ¼

t P

i

ðC 0 þ C OM þ C F Þ=ð1 þ rÞt Et =ð1 þ rÞt

LLP ¼

Capacity shortage

CS ¼

Excess electricity Battery state of charge

Eex ¼ EPV  Eload

DPP ¼

ð1 þ rÞt  1 rð1 þ rÞt

t¼0 C0 C B  C OM  C F

To minimize the simple payback period. To minimize the discounted payback period. Constraints Loss of load probability

SPP ¼

h

lnðC B  C OM  C F Þ  lnfðC B  C OM  C F Þ  rC 0 g lnð1 þ r Þ

t load;def t load Edef Eload

t SOC t ¼ W W ; (Table 4)

load demand (kW h) that is not supplied to the total energy demand (kW h) during a time interval. The optimum sizing of PV systems are classified into four approaches i.e. intuitive method, analytical method, numerical method and intelligent method [166]. Khatib et al. has reviewed the size optimization techniques for PV systems and concluded that simulation based numerical methods are most widely used technique [167].

The intuitive methods are simplest among all other but very less accurate and provide a rough estimation of the component sizes. This method is based on the designer's experience to calculate the size of PV array and battery storage unit in order to cater the load during worst sunshine conditions of the year including some safety margin [168]. In this method, simple mathematical equations are used and the estimation is usually oversized [169]. 6.2. Analytical methods The analytical methods are simple and more accurate, with the support of empirical relationships to calculate the sizing of the components of PV array [170]. These methods employs developed mathematical modeling for sizing a reliable and/or cost effective PV system [171]. Egido and Lorenzo reviewed different sizing methods and proposed a new analytical method based on LLP with four location specific coefficients for sizing of the standalone PV systems with more accuracy [172]. Markvart et al. divides the time series solar radiation data into two sets based on the average of the data and then carried out sizing of PV system using empirical relationship derived from a fitting function of sizing curve [173]. Posadillo and Luque, proposed an analytical method based on distribution function of insolation for sizing PV system with variable load demand [174]. The proposed method includes standard deviation of LLP and annual number of system failures for characterizing the reliability of the PV system. Fragaki and Markvart uses sizing curves based on long term daily solar radiation data

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Table 6 Design issues related to PV system components. Concerns

Depends on

Parameter

PV module Specifications at STC

Technology of the module

Rated power (PSTC), open circuit voltage (Voc) and short circuit current (Isc) β (Tilt angle); Azimuth angle

Whether the PV module to be mounted at yearly optimum angle or exhibits Application and load pattern monthly/seasonal adjustment or having mechanical solar tracking system? Monthly, seasonal or yearly optimum tilt angle of the module Geographical location and environmental conditions Solar radiation on the tilted surface Orientation of the PV module PV array No. of modules in series connection No. of arrays in parallel connection Load Demand Total load, Peak load, average load and minimum load Load pattern with respect to the time DC/DC converter Size of the converter Type of the converter MPPT Type of MPPT technique Inverter Type of the inverter Size of the Inverter Battery storage unit Type of the battery Size of battery storage unit No. of batteries in series connection No. of strings in parallel connection Depth of discharge

GT

Voltage requirement Current requirement

Array Voltage (Varray) Total Current (IPV)

Application Application

Load in kW h and kW

Peak load Application

Capacity (kW)

Converging time and accuracy requirement of the application

P&O; Incremental conductance

Application

line-commutated inverter; self-commutated inverter Capacity (kWh)

Peak load Application and maintenance Load demand and number hours of backup Voltage of the system Current requirement Life cycle of the battery

and requisite minimum storage for no load shedding condition [175]. They applied the sizing approach for five locations and also investigated the effect of length of data on the accuracy of the result. Bortolini et al. studied designing and control strategies of SPV system and calculated the size of PV array and battery storage unit with an aim of minimum LUCE [176].

Lead-Acid; Nickel Cadmium Capacity (Ah, kW h) Battery voltage Battery current Usually 30–40%

Simulink platform [180]. Koukouvaos et al. simulate PV system on MATLAB-Simulink and LABVIEW for design and performance analysis. Furthermore they carried out comparative evaluation of the used design software platform for explaining their reliability in PV system [181]. 6.4. Intelligent methods

6.3. Numerical methods The numerical methods use simulation based programs for calculating size according to the time interval, usually hourly, based calculations of different parameters. The power generation, load requirement, battery state of charge and battery I/O power is calculated for each time interval i.e. minute, hourly or daily [177]. These methods require heavy computations and long term weather data which are either used in number of simulation software i.e. PVSYST, Hybrid Optimization Model for Electric Renewables (HOMER), Retscreen, System Advisory Model (SAM) etc. or in simulation environments such as MATLAB, TRANSYS, LABVIEW etc. Sinha and Chandel reviewed the features of 19 simulation software tools for designing renewable energy systems [178]. The numerical methods are further classified into stochastic numerical method and deterministic numerical method. The stochastic methods include uncertainties associated with the solar radiation, ambient temperature and load requirement by using small time interval in the calculation usually hourly whereas the deterministic methods consider daily averaged parameters. The deterministic approach is easier but the stochastic approach is more accurate and reliable. Kaushika et al. (2005) provides a linear programming simulation model for determining optimal sizing of PV array and battery storage unit based on loss of power supply probability as reliability parameter [179]. Molina and Espejo (2014) developed a PV simulation software tool, named PVSET 1.0, for designing grid connected PV systems based on MATLAB-

The intelligent methods utilize artificial intelligence techniques such as artificial neural network, genetic algorithm, fuzzy logic etc. to solve complicated optimization problem [182]. These methods exhibit high accuracy, reliability and can handle incomplete data but it requires high computations. Mellit et al. proposed a sizing approach based on adaptive artificial neural network for SPV systems [183]. Mellit and Kalogirou investigated the application of artificial intelligence techniques for prediction and forecasting of solar radiation, optimization of tilt angle, optimization of component sizing and simulation of photovoltaic systems [184]. Mellit et al. reviewed the intelligent methods for sizing PV system and found that these methods are best to implement where the required data for designing the system is either incomplete or noisy and high accuracy is needed [185]. The concerns, parameters and its dependency of components in order to design solar PV system is summarized in Table 6.

7. Conclusions A comprehensive review of design procedure of different PV system configurations has been presented. An up to date survey of modeling equations for simulating PV power, cell temperature, PV efficiency and battery state of charge has also been presented. The utilization of these modeling equations for simulations, technoeconomic size optimization and prefeasibility studies of solar PV

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systems has been highlighted. The technical and economical parameters for optimum sizing of the components of PV system have been discussed along with the intuitive, analytical, numerical and intelligent optimization techniques available for technoeconomic sizing. The major conclusions are as follows:

 The motor torque, load management and efficiency curve of      

inverters are major factors for optimum sizing of SWPS, standalone and grid connected PV systems respectively. The self-commutated inverters are able to regulate the quality of output power and hence most suitable for grid connected PV systems. If the availability of resource data is inferior, the intelligent methods are most preferable for size optimization of PV systems. Artificial neural network, genetic algorithm and fuzzy logic are highly accurate and reliable for size optimization but very complex to implement. If long term data is available, the numerical methods are superior for size optimization of PV systems. Numerical methods include software programs and tools which are user friendly, but it requires more precise input data for reliable and accurate results. The governing parameters for designing a photovoltaic system are based on their application. Therefore, flow chart of design methodology of SPWPS, standalone PV and grid connected PV system has been developed.

After review, it can be seen that a lot of research has been carried out on optimization techniques, sizing methodology, modeling and control strategies of components, but still there is a lack of literature on appropriate selection of power cables, MPPT techniques and anti-islanding techniques based on its application. There is a lack of practical implementation and performance analysis of MPPT, anti-islanding techniques and different inverter technologies for different environmental and load conditions.

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