3. Introduction to standalone CPV-hydrogen system

The simple schematic of concentrated photovoltaic (CPV) system with hydrogen as energy storage is shown in Figure 1 for steady power supply and standalone operation. The concentrated photovoltaic (CPV) system, acting as primary energy supply unit, consists of multijunction solar cells (MJC)-based CPV modules, mounted onto two-axis solar trackers. As the solar concentrators require beam radiation for their operation, therefore, accurate two axis solar tracking is of prime importance for CPV operation. The arrangement of concentrating assembly of CPV system can be either a reflective type, that is, Cassegrain arrangement of reflectors or refractive type, that is, Fresnel lens. The developed performance model will be able to handle any type of concentrating assembly as it is based upon the final concentration ratio required by the MJC. For efficient CPV performance, maximum power point tracking (MPPT) device is attached to its output. The produced electricity after passing through MPPT device and DC/DC converter is supplied to the main DC line. The power consuming devices, such as solar trackers, take the power from main DC line for their operation.

The objective of targeted standalone CPV-hydrogen system is to provide an uninterrupted power supply to external consumer load, at any time and at level load level, by taking into account its own operational energy requirements. The consumer load is connected to the main Concentrated Photovoltaic (CPV): Hydrogen Design Methodology and Optimization http://dx.doi.org/10.5772/intechopen.78055 103

Figure 1. CPV-hydrogen system schematic for steady power supply and standalone operation [17].

shown better computational power with 8–12 times faster response and better Pareto front than NSGA-II [19]. Therefore, in this study, micro-GA is used as the optimization algorithm to

Regarding system models and optimizations strategy, there are many studies in the literature which are related to standalone system size optimization and techno-economic evaluation. However, all of the studies related to photovoltaic system consider conventional flat plate single junction photovoltaic (PV) panels [20]. Even in hybrid renewable energy systems, utilizing wind turbine, battery, hydrogen, and so on, the main solar photovoltaic energy system is conventional PV [21]. All of the studies aim to increase the overall conversion efficiency of the primary energy system by utilizing its full potential, so that the overall size of the system can be reduced. On the other hand, due to many systems involved in hybridization, a complex control and energy management strategy is needed to operate the system. The concentrated photovoltaic (CPV) system provides the most efficient and simple photovoltaic technology for solar energy conversion, without any hybridization. However, there is no study in the literature that discusses the performance model and control strategy related to standalone operation of CPV. In addition, there are many commercial software related to simulation and optimization of renewable energy systems and their hybrids, for example, iHOGA, HYBRIDS2, INSEL, HOMER, TRNSYS + HYDROGEMS, SOMES, ARES, RAPSIM and SOLSIM [22]. But none of the software available hitherto has the capability to simulate and optimize the performance of concentrated photovoltaic (CPV) system. Therefore, the main objective of this chapter is to introduce and discusses the comprehensive model and optimization methodology for standalone and long-term operation of

optimize the overall size of standalone CPV-Hydrogen system.

102 Advances In Hydrogen Generation Technologies

concentrated photovoltaic (CPV) with hydrogen production as energy storage.

The simple schematic of concentrated photovoltaic (CPV) system with hydrogen as energy storage is shown in Figure 1 for steady power supply and standalone operation. The concentrated photovoltaic (CPV) system, acting as primary energy supply unit, consists of multijunction solar cells (MJC)-based CPV modules, mounted onto two-axis solar trackers. As the solar concentrators require beam radiation for their operation, therefore, accurate two axis solar tracking is of prime importance for CPV operation. The arrangement of concentrating assembly of CPV system can be either a reflective type, that is, Cassegrain arrangement of reflectors or refractive type, that is, Fresnel lens. The developed performance model will be able to handle any type of concentrating assembly as it is based upon the final concentration ratio required by the MJC. For efficient CPV performance, maximum power point tracking (MPPT) device is attached to its output. The produced electricity after passing through MPPT device and DC/DC converter is supplied to the main DC line. The power consuming devices, such as solar trackers, take the power from main

The objective of targeted standalone CPV-hydrogen system is to provide an uninterrupted power supply to external consumer load, at any time and at level load level, by taking into account its own operational energy requirements. The consumer load is connected to the main

3. Introduction to standalone CPV-hydrogen system

DC line for their operation.

DC line through DC/AC converter. The power supply to the consumer load is the first priority of the system, followed by the system operational needs. However, any excess power available is then supplied to the electrolyzer to produce hydrogen and oxygen by electrolytic splitting of water. The produced hydrogen is compressed and sent to tanks for storage and future use. However, the oxygen is stored at produced pressure as it can also be taken from environment in case of shortage. The power requirement of hydrogen compressor also comes from main DC line through DC/AC converter. In case of power deficit by the CPV system, the stored hydrogen is supplied to the fuel cell, with oxygen, to produce electricity and water. The produced electricity is then supplied to the main DC line through DC/DC converter. However, the produced water goes to storage tank, to be used as a loop in electrolyzer.

#### 4. Performance model development for CPV-hydrogen system

In this section, detailed performance model of CPV-hydrogen system is discussed which is based upon the performance model of individual components which are linked together as a complete system through energy management strategy as shown in Figure 2. The main power input of the system is the solar energy in form of beam radiations or direct normal irradiance (DNI) as CPV cannot accept diffuse radiations. The weather data in the form of direct normal irradiance (DNI) are provided to the CPV performance model as input solar energy. The received solar energy is then used to calculate the concentration at the MJC area, according to the optical efficiency of the solar concentrators. By knowing the temperature characteristics of

Figure 2. Energy management strategy for performance model of CPV-hydrogen system [17].

MJC and the total number of MJC units, the total power output from CPV can be calculated. Another input to the system performance model is the consumer electrical load requirements which is then combined with the system operational power requirements to calculated overall load of the system needed to be powered. The performance models for each individual components of the system are discussed later.

#### 4.1. Concentrated photovoltaic system

The performance model of concentrated photovoltaic (CPV) system is based upon the single diode model, representing solar cell characteristic curves but considering concentration factor, is given by Eq. (1) [23].

$$P\_{\mathbb{C}} = I\_{\mathbb{C}} V\_{\mathbb{C}} = V\_{\mathbb{C}} \left[ I\_{\mathbb{o}} \left\{ \exp \left( \frac{qV\_{\mathbb{C}}}{nkT\_{\mathbb{C}}} \right) - 1 \right\} - I\_{\mathbb{S}\mathbb{C}} \right] \tag{1}$$

It must be noted that the values of all of the parameters appearing in the model of any component of the system are given in Table 1. For open circuit voltage, the diode saturation current factor "Io" can be found by using V = VOC and I = 0 in Eq. (1).

$$I\_o = \frac{I\_{\text{SC}}}{\left[\exp\left(\frac{qV\_{\text{OC}}}{nkT\_{\text{C}}}\right) - 1\right]}\tag{2}$$

The cell under consideration is a triple junction InGaP/InGaAs/Ge cell, for which the characteristics curves are shown in Figure 3. The short circuit current is proportional to the concentration ratio. However, open circuit voltage is following logarithmic variation against concentration ratio. In order to find out cell voltage and current, there is a need for "VOC" and "ISC" of cell to be known at that operating condition. If the trends of "VOC" and "ISC" are known at 25C, then

Figure 3. Variation in (a) short circuit current and (b) open circuit voltage of InGaP/InGaAs/Ge triple-junction solar cell

Parameter Value Parameter Value q (Coulomb) [24] 1.6021765 <sup>10</sup><sup>19</sup> a1 [25] 0.995

ηmppt [26] 85% a4 [25] 0

) [25] 7.331 <sup>10</sup><sup>5</sup> a7 [25] <sup>0</sup>

S1 (V) [25] 1.586 <sup>10</sup><sup>1</sup> Uo (mV) [25] <sup>1065</sup>

r 1.4 ηcom (%) [31] 70

Table 1. Information regarding constant factors appearing in CPV-hydrogen performance model.

) [25] 1.107 <sup>10</sup><sup>7</sup> n [25] <sup>2</sup>

) [25] 1.606 <sup>10</sup><sup>5</sup> MH2 (g/mol) 2.0159

) [25] 1.599 <sup>10</sup><sup>2</sup> CPH (J/kg K) 14,304

) [25] 1.302 Tcom (K) 306

) [25] 4.213 <sup>10</sup><sup>2</sup> <sup>η</sup>DC/AC (%) <sup>90</sup>

) [25] 9.5788

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105

Concentrated Photovoltaic (CPV): Hydrogen Design Methodology and Optimization

) [25] 96,485

) [25] 80

) [25] 0.438

) [25] 1502.7083

) [25] 70.8005

) [25] 0.0555

) [24] 1.3806488 <sup>10</sup><sup>23</sup> a2 (m2 <sup>A</sup><sup>1</sup>

nC [24] 2 a3 (m2 A<sup>1</sup> C<sup>1</sup>

ηDC/AC [27, 28] 90% F (As mol<sup>1</sup>

ηCDC [29, 30] 95% a5 (m4 A<sup>1</sup>

Urev (V) [25] 1.229 a6 (m4 A<sup>1</sup> C<sup>1</sup>

) [25] 1.378 <sup>10</sup><sup>3</sup> b (mV dec<sup>1</sup>

) [25] 0.25 R (Ωcm<sup>2</sup>

k (m2

r1 (Ωm<sup>2</sup>

r2 (Ωm<sup>2</sup> C<sup>1</sup>

S2 (V C<sup>1</sup>

S3 (V C<sup>2</sup>

t1 (m2 A<sup>1</sup>

t2 (m2 A<sup>1</sup> C<sup>1</sup>

t3 (m2 A<sup>1</sup> C<sup>2</sup>

with temperature and concentration [17].

AE (m2

kgs<sup>2</sup> K<sup>1</sup>

As the multijunction solar cells (MJC) can operate at higher concentrations, therefore, their performance characteristics are depending upon both temperature and concentration at cell area.


Table 1. Information regarding constant factors appearing in CPV-hydrogen performance model.

MJC and the total number of MJC units, the total power output from CPV can be calculated. Another input to the system performance model is the consumer electrical load requirements which is then combined with the system operational power requirements to calculated overall load of the system needed to be powered. The performance models for each individual

Figure 2. Energy management strategy for performance model of CPV-hydrogen system [17].

The performance model of concentrated photovoltaic (CPV) system is based upon the single diode model, representing solar cell characteristic curves but considering concentration factor,

It must be noted that the values of all of the parameters appearing in the model of any component of the system are given in Table 1. For open circuit voltage, the diode saturation

As the multijunction solar cells (MJC) can operate at higher concentrations, therefore, their performance characteristics are depending upon both temperature and concentration at cell area.

Io <sup>¼</sup> ISC exp qVOC nkTC � �

nkTC � �

� �

� �

� 1

� 1

� ISC

h i (2)

(1)

PC <sup>¼</sup> ICVC <sup>¼</sup> VC Io exp qVC

current factor "Io" can be found by using V = VOC and I = 0 in Eq. (1).

components of the system are discussed later.

4.1. Concentrated photovoltaic system

104 Advances In Hydrogen Generation Technologies

is given by Eq. (1) [23].

The cell under consideration is a triple junction InGaP/InGaAs/Ge cell, for which the characteristics curves are shown in Figure 3. The short circuit current is proportional to the concentration ratio. However, open circuit voltage is following logarithmic variation against concentration ratio. In order to find out cell voltage and current, there is a need for "VOC" and "ISC" of cell to be known at that operating condition. If the trends of "VOC" and "ISC" are known at 25C, then

Figure 3. Variation in (a) short circuit current and (b) open circuit voltage of InGaP/InGaAs/Ge triple-junction solar cell with temperature and concentration [17].

their corresponding values at different temperature and concentration can be found using Eqs. (3) and (4).

$$V\_{\rm OC}(T\_{\rm C}, \rm C\_{\rm C}) = [V\_{\rm OC}(at \ 25^{\circ} \rm C)]\_{\rm C} + (T\_{\rm C} - 25) \left[ \frac{dV\_{\rm OC}}{dT\_{\rm C}} \right]\_{\rm C} \tag{3}$$

$$I\_{\mathcal{SC}}(T\_{\mathcal{C}}, \mathbb{C}\_{\mathcal{C}}) = [I\_{\mathcal{SC}}(at \ 25^{\circ}\mathbb{C})]\_{\mathbb{C}} + (T\_{\mathcal{C}} - 25) \left[ \frac{dI\_{\mathcal{SC}}}{dT\_{\mathcal{C}}} \right]\_{\mathbb{C}} \tag{4}$$

Another required parameter is the cell temperature, which is assumed to be 40�C higher than the then ambient temperature, with 10�C temperature difference between cell surface and back plate temperature [32]. However, remaining temperature drop is between ambient and the heat sink/back plate. The concentration at cell area is given by Eq. (5).

$$\mathcal{C}\_{\mathbb{C}} = I\_b \times \frac{A\_{\text{com}}}{A\_{\mathbb{C}}} \times \eta\_{\text{OP}} \tag{5}$$

In order to model maximum power point tracking (MPPT) device, the derivative of cell power output equation can be equated to zero, as:

$$\frac{dP\_{\mathbb{C}}}{dV\_{\mathbb{C}}} = 0 \tag{6}$$

4.2. Electrolyzer

temperatures [19].

UE ¼ Urev þ

For the electrolyzer unit, the considered theoretical model is based upon the characteristics of an alkaline electrolyzer available in the literature [25]. The operating temperature of unit is assumed to be fixed at 80�C. The performance model for the operating voltage of single

Figure 4. Comparison of experimentally measured and simulated efficiency of MJC at different concentrations and

IE þ S<sup>1</sup> þ S2TE þ S3TE

through the electrolyzer and the Faraday efficiency, as given by Eqs. (13) and (14).

E,H<sup>2</sup> ¼ ηEF

a<sup>2</sup> þ a3TE þ a4TE

IE AE

n •

0 B@

5 W, the required number of electrolyzer cells can be calculated as:

<sup>2</sup> � �: log

It must be noted that the values of all required constant parameters are given in Table 1. The amount of hydrogen and oxygen production is based upon the amount of current passing

> NECIE nF <sup>¼</sup> <sup>2</sup><sup>n</sup> •

In the design of the electrolyzer, the important parameter is the total number of cells needed. The number of electrolyzer cells depend upon the maximum amount of excess power that can be expected throughout the operational cycle of the system. By assuming the maximum rated current of 750 A and single cell voltage of 1.8 V [25], and the maximum MJC power output of

2

þ

a<sup>5</sup> þ a6TE þ a7TE

IE AE � �<sup>2</sup>

<sup>t</sup><sup>1</sup> <sup>þ</sup> <sup>t</sup><sup>2</sup> TE <sup>þ</sup> <sup>t</sup><sup>3</sup> TE 2

Concentrated Photovoltaic (CPV): Hydrogen Design Methodology and Optimization

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107

AE

!

E,O<sup>2</sup> (13)

CA (14)

2

1

IE þ 1

(12)

electrolyzer cell, under consideration, is given by Eq. (12).

r<sup>1</sup> þ r2TE AE

ηEF ¼ a1:exp

$$\frac{d}{dV\_{\mathcal{C}}} \left[ V\_{\mathcal{C}} I\_{\mathcal{C}} \left\{ \exp \left( \frac{qV\_{\mathcal{C}}}{nkT\_{\mathcal{C}}} \right) - 1 \right\} - V\_{\mathcal{C}} I\_{\mathcal{SC}} \right] = 0 \tag{7}$$

After simplification, we can have the expression for power output of MJC as:

$$V\_{mppt} = V\_{OC} - \frac{nkT\_{\mathcal{C}}}{q} \ln\left[1 + \frac{qV\_{mppt}}{nkT\_{\mathcal{C}}}\right] \tag{8}$$

$$I\_{\rm mppt} = I\_0 \left\{ \exp\left(\frac{qV\_{\rm mppt}}{nkT\_{\odot}}\right) - 1 \right\} - I\_{\rm SC} \tag{9}$$

$$P\_{mppt} = \eta\_{mppt} \times I\_{mppt} \times V\_{mppt} \tag{10}$$

The simulated and experimental efficiencies of MJC are shown in Figure 4. By incorporating the efficiencies of voltage converters and the tracker power requirement, the net power output of CPV units is given by Eq. (11).

$$P\_{\rm CPV} = \eta\_{\rm DC/AC} \times \eta\_{\rm CDC} \times \eta\_{\rm Tr} \times P\_{\rm imppt} \times N\_{\rm CM} \times \rm NP\_{\rm CPV} \tag{11}$$

For the current study, a CPV panel is assumed to be made of 25 MJCs, arranged into a 5 � 5 array. In addition, the power consumption of solar tracker is considered only during diurnal period, that is, from sunrise to sunset as calculated from solar geometry model [33].

Figure 4. Comparison of experimentally measured and simulated efficiency of MJC at different concentrations and temperatures [19].

#### 4.2. Electrolyzer

their corresponding values at different temperature and concentration can be found using

VOCð Þ¼ TC; CC VOC at <sup>25</sup><sup>o</sup> ½ � ð Þ <sup>C</sup> <sup>C</sup> <sup>þ</sup> ð Þ TC � <sup>25</sup> dVOC

ISCð Þ¼ TC;CC ISC at <sup>25</sup><sup>o</sup> ½ � ð Þ <sup>C</sup> <sup>C</sup> <sup>þ</sup> ð Þ TC � <sup>25</sup> dISC

Another required parameter is the cell temperature, which is assumed to be 40�C higher than the then ambient temperature, with 10�C temperature difference between cell surface and back plate temperature [32]. However, remaining temperature drop is between ambient and the

> Acon AC

In order to model maximum power point tracking (MPPT) device, the derivative of cell power

dPC dVC

nkTC 

q

The simulated and experimental efficiencies of MJC are shown in Figure 4. By incorporating the efficiencies of voltage converters and the tracker power requirement, the net power output

For the current study, a CPV panel is assumed to be made of 25 MJCs, arranged into a 5 � 5 array. In addition, the power consumption of solar tracker is considered only during diurnal period, that is, from sunrise to sunset as calculated from solar geometry

qVmppt nkTC 

� 1

ln 1 þ

� 1

PCPV ¼ ηDC=AC � ηCDC � ηTr � Pmppt � NCM � NPCPV (11)

� VCISC

qVmppt nkTC 

Pmppt ¼ ηmppt � Imppt � Vmppt (10)

heat sink/back plate. The concentration at cell area is given by Eq. (5).

output equation can be equated to zero, as:

of CPV units is given by Eq. (11).

model [33].

d dVC CC ¼ Ib �

VCIo exp qVC

After simplification, we can have the expression for power output of MJC as:

Vmppt <sup>¼</sup> VOC � nkTC

Imppt ¼ Io exp

dTC 

dTC  C

� ηOP (5)

¼ 0 (6)

¼ 0 (7)

� ISC (9)

C

(3)

(4)

(8)

Eqs. (3) and (4).

106 Advances In Hydrogen Generation Technologies

For the electrolyzer unit, the considered theoretical model is based upon the characteristics of an alkaline electrolyzer available in the literature [25]. The operating temperature of unit is assumed to be fixed at 80�C. The performance model for the operating voltage of single electrolyzer cell, under consideration, is given by Eq. (12).

$$\mathcal{U}\_{\rm E} = \mathcal{U}\_{\rm rev} + \frac{r\_1 + r\_2 T\_E}{A\_E} I\_E + \left(\mathcal{S}\_1 + \mathcal{S}\_2 T\_E + \mathcal{S}\_3 T\_E^2\right) \cdot \log\left(\frac{t\_1 + \frac{t\_2}{T\_E} + \frac{t\_3}{T\_E}}{A\_E} I\_E + 1\right) \tag{12}$$

It must be noted that the values of all required constant parameters are given in Table 1. The amount of hydrogen and oxygen production is based upon the amount of current passing through the electrolyzer and the Faraday efficiency, as given by Eqs. (13) and (14).

$$
\stackrel{\bullet}{\mathfrak{m}}\_{E,H2} = \eta\_{EF} \frac{N\_{EC}I\_E}{nF} = \mathbf{2} \stackrel{\bullet}{\mathfrak{m}}\_{E,O2} \tag{13}
$$

$$\eta\_{EF} = a\_1 \exp\left(\frac{a\_2 + a\_3 T\_E + a\_4 T\_E^2}{\frac{I\_E}{A\_E}} + \frac{a\_5 + a\_6 T\_E + a\_7 T\_E^2}{\left(\frac{I\_E}{A\_E}\right)^2}\right) \tag{14}$$

In the design of the electrolyzer, the important parameter is the total number of cells needed. The number of electrolyzer cells depend upon the maximum amount of excess power that can be expected throughout the operational cycle of the system. By assuming the maximum rated current of 750 A and single cell voltage of 1.8 V [25], and the maximum MJC power output of 5 W, the required number of electrolyzer cells can be calculated as:

$$N\_{\rm EC} = \frac{\left(P\_{\rm M/C,max} \times N\_{\rm CM} \times NP\_{\rm CPV}\right) - L\_{\rm min}}{V\_{\rm EC,max} \times I\_{\rm EC,max}} \tag{15}$$

If the electrolyzer cells are assumed to be connected in series, then the current flow through the electrolyzer is given by Eq. (16). It must be noted that the excess power available must be calculated after fulfilling the total load requirements, that is, consumer load plus the system operational power requirement.

$$I\_E = \frac{\eta\_{\rm CDC} \times P\_{\rm excess}}{N\_{\rm EC} \times U\_E} \tag{16}$$

requirements, is based upon the thermodynamic equation depending upon the pressure ratio,

It must be noted that the hydrogen produced by the electrolyzer goes to the compressor as they are connected in series. Therefore, the flow of hydrogen through compressor is the same as the rate of hydrogen production in electrolyzer. In addition, the compressor operating pressure is also assumed to be the same as the electrolyzer operating pressure as hydrogen directly goes to the compressor after production. The pressure at inlet of compressor is the same as the operating pressure of electrolyzer "PE" and the pressure at the outlet of compres-

In order to model the hydrogen storage tank, ideal gas equation is considered with compressibility factor "Z," as given by Eq. (22), to find out the tank pressure according to the stored

A cylindrical tank of 3.34 m3 capacity is considered and the Eq. (23) for compressibility factor for hydrogen storage tank is obtained from the actual gas pressure data obtained at 33�C from REFPROP (Reference Fluid Thermodynamic and Transport Properties), provided by NIST (National Institute of Standards and Technology) standard reference database Version 8.0 [34]. Eq. (22) for tank pressure, after putting the compressibility factor "Z," takes the final form

<sup>3</sup> <sup>þ</sup> <sup>0</sup>:0032872nH

It has been mentioned that the main motivation of this study is to optimize the individual component of the CPV-Hydrogen system for uninterrupted power supply to consumer load, while meeting the system operational power requirements. However, for such system design problem, there can be many system size configurations that can fulfill the condition of steady power supply to the consumer. The main purpose of this optimization is to define a set of objective functions to look for system configuration which will not only provide uninterrupted power supply but at minimum cost and optimum system performance. Therefore, to achieve

<sup>2</sup> <sup>þ</sup> <sup>4</sup>:<sup>3</sup> � <sup>10</sup>�<sup>5</sup>

Pta <sup>¼</sup> ntaRTta Vta

ZH <sup>¼</sup> <sup>3</sup>:<sup>5</sup> � <sup>10</sup>�<sup>11</sup>nH

nH

PH <sup>¼</sup> <sup>2</sup>:<sup>666</sup> � <sup>10</sup>�<sup>8</sup>

5. Multiobjective optimization criteria

Tcom ηDC=AC � ηcom

Pta PE � � <sup>r</sup>�<sup>1</sup> ð Þ<sup>r</sup>

Concentrated Photovoltaic (CPV): Hydrogen Design Methodology and Optimization

( )

� ZH (22)

nH þ 1 (23)

<sup>2</sup> <sup>þ</sup> <sup>761</sup>:7476nH (24)

� 1

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(21)

109

that is, pressure at inlet and outlet of compressor, which is given by Eq. (21) [31].

MH<sup>2</sup> <sup>1000</sup> � � � CPH �

sor is the same as the pressure inside storage tank "Pta."

Pcom ¼ n • E,H<sup>2</sup> �

4.5. Hydrogen storage tank

amount of hydrogen.

as Eq. (24).

#### 4.3. Fuel cell

For the current study, the proton exchange membrane (PEM) type of fuel cell is considered for which the performance characteristics are given in literature [25] and the performance model is given by Eq. (17).

$$\mathcal{U}I\_{\mathcal{F}} = \mathcal{U}\_o - b.\log\left(\frac{I\_{\mathcal{F}}}{A\_{\mathcal{F}}}\right) - R\left(\frac{I\_{\mathcal{F}}}{A\_{\mathcal{F}}}\right) \tag{17}$$

Like the electrolyzer, the gas consumption by fuel cell depends upon its total current flow, as per required power, which is given by Eq. (18). The amount of current flow through the fuel cell is given by Eq. (19), which is the same for each cell as they are connected in series. It must be noted that the fuel cell is assumed to be operated with a Faraday efficiency of 70% and the surface area for a single cell is taken as 300 cm<sup>2</sup> .

$$\mathfrak{N}\_{\rm F,H2} = \eta\_{\rm FF} \frac{N\_{\rm FC} I\_{\rm F}}{nF} = 2 \mathfrak{N}\_{\rm F,O2} \tag{18}$$

$$I\_F = \frac{P\_{req}}{\eta\_{\rm CDC} \times \eta\_{\rm DC/AC} \times N\_{\rm FC} \times U\_F} \tag{19}$$

Like CPV and electrolyzer design, the fuel cell design is also based upon the maximum possible number of cells needed to meet the total load demand. As per the considered fuel cell characteristics [25], the maximum power per cell is 114.6 W. Therefore, for maximum load requirement of the customer, the total number of cells required in the design of a fuel cell is given by Eq. (19).

$$N\_{\rm FC} = \frac{L\_{\rm max}}{\eta\_{\rm CDC} \times \eta\_{\rm DC/AC} \times P\_{\rm FC, max}} \tag{20}$$

#### 4.4. Hydrogen compressor

As mentioned earlier, the produced hydrogen is compressor by a mechanical compressor to be stored in the storage tank as the mechanical compression system provides a most compact and reliable storage solution. The performance model for mechanical compressor, for its power requirements, is based upon the thermodynamic equation depending upon the pressure ratio, that is, pressure at inlet and outlet of compressor, which is given by Eq. (21) [31].

$$P\_{com} = \left(\overset{\bullet}{n}\_{\mathrm{E},H2} \times \frac{M\_{\mathrm{H}2}}{1000}\right) \times \mathrm{CP}\_{H} \times \frac{T\_{\mathrm{com}}}{\eta\_{\mathrm{DC}/AC} \times \eta\_{\mathrm{com}}} \left\{ \left(\frac{P\_{\mathrm{th}}}{P\_{\mathrm{E}}}\right)^{\left(\frac{\omega}{r}\right)} - 1 \right\} \tag{21}$$

It must be noted that the hydrogen produced by the electrolyzer goes to the compressor as they are connected in series. Therefore, the flow of hydrogen through compressor is the same as the rate of hydrogen production in electrolyzer. In addition, the compressor operating pressure is also assumed to be the same as the electrolyzer operating pressure as hydrogen directly goes to the compressor after production. The pressure at inlet of compressor is the same as the operating pressure of electrolyzer "PE" and the pressure at the outlet of compressor is the same as the pressure inside storage tank "Pta."

#### 4.5. Hydrogen storage tank

NEC <sup>¼</sup> PMJC,max � NCM � NPCPV

operational power requirement.

108 Advances In Hydrogen Generation Technologies

surface area for a single cell is taken as 300 cm<sup>2</sup>

4.3. Fuel cell

given by Eq. (17).

given by Eq. (19).

4.4. Hydrogen compressor

 � <sup>L</sup>min VEC,max � IEC,max

If the electrolyzer cells are assumed to be connected in series, then the current flow through the electrolyzer is given by Eq. (16). It must be noted that the excess power available must be calculated after fulfilling the total load requirements, that is, consumer load plus the system

> IE <sup>¼</sup> <sup>η</sup>CDC � Pexcess NEC � UE

For the current study, the proton exchange membrane (PEM) type of fuel cell is considered for which the performance characteristics are given in literature [25] and the performance model is

Like the electrolyzer, the gas consumption by fuel cell depends upon its total current flow, as per required power, which is given by Eq. (18). The amount of current flow through the fuel cell is given by Eq. (19), which is the same for each cell as they are connected in series. It must be noted that the fuel cell is assumed to be operated with a Faraday efficiency of 70% and the

.

NFCIF nF <sup>¼</sup> <sup>2</sup><sup>n</sup> •

ηCDC � ηDC=AC � NFC � UF

ηCDC � ηDC=AC � PFC,max

As mentioned earlier, the produced hydrogen is compressor by a mechanical compressor to be stored in the storage tank as the mechanical compression system provides a most compact and reliable storage solution. The performance model for mechanical compressor, for its power

Like CPV and electrolyzer design, the fuel cell design is also based upon the maximum possible number of cells needed to meet the total load demand. As per the considered fuel cell characteristics [25], the maximum power per cell is 114.6 W. Therefore, for maximum load requirement of the customer, the total number of cells required in the design of a fuel cell is

AF  � <sup>R</sup> IF AF 

UF <sup>¼</sup> Uo � <sup>b</sup>: log IF

n •

F,H<sup>2</sup> ¼ ηFF

IF <sup>¼</sup> Preq

NFC <sup>¼</sup> <sup>L</sup>max

(15)

(16)

(17)

(19)

(20)

F,O<sup>2</sup> (18)

In order to model the hydrogen storage tank, ideal gas equation is considered with compressibility factor "Z," as given by Eq. (22), to find out the tank pressure according to the stored amount of hydrogen.

$$P\_{\rm tot} = \frac{n\_{\rm tot}RT\_{\rm tot}}{V\_{\rm tot}} \times Z\_H \tag{22}$$

$$Z\_H = 3.5 \times 10^{-11} n\_H{}^2 + 4.3 \times 10^{-5} n\_H + 1\tag{23}$$

A cylindrical tank of 3.34 m3 capacity is considered and the Eq. (23) for compressibility factor for hydrogen storage tank is obtained from the actual gas pressure data obtained at 33�C from REFPROP (Reference Fluid Thermodynamic and Transport Properties), provided by NIST (National Institute of Standards and Technology) standard reference database Version 8.0 [34]. Eq. (22) for tank pressure, after putting the compressibility factor "Z," takes the final form as Eq. (24).

$$P\_H = 2.666 \times 10^{-8} n\_H{^\circ} + 0.0032872 n\_H{^\circ} + 761.7476 n\_H \tag{24}$$

#### 5. Multiobjective optimization criteria

It has been mentioned that the main motivation of this study is to optimize the individual component of the CPV-Hydrogen system for uninterrupted power supply to consumer load, while meeting the system operational power requirements. However, for such system design problem, there can be many system size configurations that can fulfill the condition of steady power supply to the consumer. The main purpose of this optimization is to define a set of objective functions to look for system configuration which will not only provide uninterrupted power supply but at minimum cost and optimum system performance. Therefore, to achieve such a target, three objective functions are defined to be met as per proposed optimization strategy to find optimum system size configuration.

First, the main objective function defined is the power supply failure time (PSFT) which defines the number of seconds for which the system was unable to meet the total load requirements, that is, consumer load plus system operational power requirements. Such PSFT factor must be zero for any selected configurations, as given by Eq. (25). This objective function is of prime importance and has main priorities, before proceeding to the second objective function.

$$PSFT = \sum\_{year} t\_{\text{PF}} = 0\tag{25}$$

CSTH<sup>2</sup> ¼ STMH<sup>2</sup> � ½ � CCSTH<sup>2</sup> þ ð Þ OMCSTH<sup>2</sup> � CRF (31)

Concentrated Photovoltaic (CPV): Hydrogen Design Methodology and Optimization

CSTO<sup>2</sup> ¼ STMO<sup>2</sup> � ½ � CCSTO<sup>2</sup> þ ð Þ OMCSTO<sup>2</sup> � CRF (32)

CRF <sup>¼</sup> <sup>i</sup> � ð Þ <sup>1</sup> <sup>þ</sup> <sup>i</sup> <sup>L</sup>

SPPW <sup>¼</sup> <sup>1</sup>

It must be noted that the cost associated with the voltage converters is assumed to be included in the cost of the primary component of the system. The solar trackers cost is included in the cost of the CPV system. However, cost for water storage is not considered due to its negligible

Component CC OMC RC Replacement

Hydrogen storage 666 \$/kg 2% of CC N.A. N.A. Oxygen storage 44.4 \$/kg 2% of CC N.A. N.A. Electrolyzer 3.774 \$/W 2% of CC 0.777 \$/W 10 years Hydrogen compressor 3000 \$/kW 20% of CC N.A. N.A. Fuel cell 2.997 \$/W 2% of CC 0.888 \$/W 10 years Concentrated photovoltaic (CPV) 2.62 \$/WP 2.125% of CC N.A. N.A.

6. System optimization algorithm and strategy with micro-GA

Table 2. Costing parameters considered for techno-economic evaluation of CPV-hydrogen system [17].

As mentioned earlier, the micro-genetic-algorithm (micro-GA) is considered as the main optimization algorithm to search for the optimum system configuration, as per defined objective functions. Based upon the defined performance model of CPV-Hydrogen system, the simulation and optimization program was developed in FORTRAN as per strategy shown in Figure 5. The program consists of two parts. The first part is associated with the performance simulation of CPV-Hydrogen system, based upon the developed model. The second part is associated with the system optimization, based upon the defined objective function, to find the optimum system size configuration using micro-GA. There are only two sizing parameters which are given as input to the program to be optimized, that is, the total number of CPV modules needed and the amount of initial hydrogen needed at the start of simulation and optimization cycle. However, the remaining parameters are calculated from these input parameters. The micro-GA is run with a population size of 5 and maximum 300 generations. The results of the

effect on the overall system cost.

study are presented in the next section.

ð Þ <sup>1</sup> <sup>þ</sup> <sup>i</sup> <sup>L</sup> � <sup>1</sup> " #

Ccom ¼ Pcom � ½ � CCcom þ ð Þ OMCcom � CRF (33)

ð Þ <sup>1</sup> <sup>þ</sup> <sup>i</sup> <sup>y</sup> (35)

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(34)

111

In order to start the optimization simulation cycle, it is assumed that the gas storage tanks are filled with a certain amount of gases so that the optimization cycle can be started. Otherwise, in case of empty tanks, the simulation cycle will be stuck in achieving the first objective function if the input weather data value is poor at the start. However, at the end of the simulation cycle, such initial amounts of stored gases must be stored so that it can be assured that the system is self-sustaining and it was not operating because of the initial stored energy. Therefore, the second objective function for such a study is defined by Eq. (26).

$$L1 < S T\_{H2(f)} - S T\_{H2(i)} < L2 \tag{26}$$

where STH2(f) and STH2(i) define the state of the hydrogen storage tanks after and before the simulation cycle, respectively. On the other hand, L1 and L2 define the upper and lower limit of the difference between state of hydrogen tank before and after simulation cycle. The current simulation cycle is operated in a yearly manner. Therefore, the second objective function is computed at the end of each year. The value of L1 and L2 are selected as "�10" and "35," respectively, for the current study. The lower value L1 is kept minimum because it is desired that the system recovers to initial state at the end of the simulation cycle. However, the upper limit is kept a bit high so that system is well prepared for the simulation cycle and it can handle the load requirements with enough available storage, in case of poor weather conditions.

The last and the most important objective function is the overall system cost, including investment cost, operational cost and replacement cost. The overall system cost function is given by Eq. (27). The cost functions associated with individual components of the system are given by Eqs. (28)–(33). The system is operated for a lifetime of 20 years. Therefore, the cost parameters associated with each component of the system are given in Table 2 [35]. In addition, the CRF (capital recovery factor) and the SPPW (single payment present worth) factor are also given by Eqs. (34) and (35). The interest rate considered in the current analysis is 6% [36].

$$\mathbf{C}\_{AT} = \mathbf{C}\_{CPV} + \mathbf{C}\_{EL} + \mathbf{C}\_{FC} + \mathbf{C}\_{STH2} + \mathbf{C}\_{STO2} + \mathbf{C}\_{com} \tag{27}$$

$$\mathsf{C\_{CPV}} = \left(\mathrm{NP\_{CPV}} \times \mathrm{N\_{CM}} \times \mathrm{P\_{MIC,max}}\right) \times \left[\mathsf{CC\_{CPV}} + \left(\mathrm{OMC\_{CPV}} \times \mathsf{CRF}\right)\right] \tag{28}$$

$$\mathbf{C\_{EL}} = (\mathbf{N\_{EC}} \times \mathbf{P\_{EL,max}}) \times \left[ \mathbf{C} \mathbf{C\_{EL}} + (\mathbf{R} \mathbf{C\_{EL}} \times \mathbf{SPPW}) + (\mathbf{OMC\_{EL}} \times \mathbf{CRF}) \right] \tag{29}$$

$$\mathbf{C\_{FC}} = (\mathbf{N\_{FC}} \times P\_{\mathbf{FC,max}}) \times \left[ \mathbf{C} \mathbf{C\_{FC}} + (\mathbf{R} \mathbf{C\_{FC}} \times \mathbf{SPPW}) + (\mathbf{OMC\_{FC}} \times \mathbf{CRF}) \right] \tag{30}$$

Concentrated Photovoltaic (CPV): Hydrogen Design Methodology and Optimization http://dx.doi.org/10.5772/intechopen.78055 111

$$\mathbf{C\_{STH2}} = \mathbf{STM\_{H2}} \times \left[ \mathbf{CC\_{STH2}} + \left( \mathbf{OMC\_{STH2} \times \mathbf{CRF}} \right) \right] \tag{31}$$

$$\mathbf{C\_{STO2}} = \mathbf{STM\_{O2}} \times \left[ \mathbf{CC\_{STO2}} + \left( \mathbf{OMC\_{STO2} \times \mathbf{CRF}} \right) \right] \tag{32}$$

$$\mathbf{C}\_{\rm com} = P\_{\rm com} \times \left[ \mathbf{C} \mathbf{C}\_{\rm com} + \left( \mathbf{OMC}\_{\rm com} \times \mathbf{C} \mathbf{R} \mathbf{F} \right) \right] \tag{33}$$

$$\text{CRF} = \left[ \frac{i \times (1 + i)^L}{(1 + i)^L - 1} \right] \tag{34}$$

$$SPPW = \frac{1}{(1+i)^{\mathcal{Y}}} \tag{35}$$

It must be noted that the cost associated with the voltage converters is assumed to be included in the cost of the primary component of the system. The solar trackers cost is included in the cost of the CPV system. However, cost for water storage is not considered due to its negligible effect on the overall system cost.

such a target, three objective functions are defined to be met as per proposed optimization

First, the main objective function defined is the power supply failure time (PSFT) which defines the number of seconds for which the system was unable to meet the total load requirements, that is, consumer load plus system operational power requirements. Such PSFT factor must be zero for any selected configurations, as given by Eq. (25). This objective function is of prime impor-

year

In order to start the optimization simulation cycle, it is assumed that the gas storage tanks are filled with a certain amount of gases so that the optimization cycle can be started. Otherwise, in case of empty tanks, the simulation cycle will be stuck in achieving the first objective function if the input weather data value is poor at the start. However, at the end of the simulation cycle, such initial amounts of stored gases must be stored so that it can be assured that the system is self-sustaining and it was not operating because of the initial stored energy. Therefore, the second

where STH2(f) and STH2(i) define the state of the hydrogen storage tanks after and before the simulation cycle, respectively. On the other hand, L1 and L2 define the upper and lower limit of the difference between state of hydrogen tank before and after simulation cycle. The current simulation cycle is operated in a yearly manner. Therefore, the second objective function is computed at the end of each year. The value of L1 and L2 are selected as "�10" and "35," respectively, for the current study. The lower value L1 is kept minimum because it is desired that the system recovers to initial state at the end of the simulation cycle. However, the upper limit is kept a bit high so that system is well prepared for the simulation cycle and it can handle the load requirements with enough available storage, in case of poor weather conditions.

The last and the most important objective function is the overall system cost, including investment cost, operational cost and replacement cost. The overall system cost function is given by Eq. (27). The cost functions associated with individual components of the system are given by Eqs. (28)–(33). The system is operated for a lifetime of 20 years. Therefore, the cost parameters associated with each component of the system are given in Table 2 [35]. In addition, the CRF (capital recovery factor) and the SPPW (single payment present worth) factor are also given by

CAT ¼ CCPV þ CEL þ CFC þ CSTH<sup>2</sup> þ CSTO<sup>2</sup> þ Ccom (27)

� � � ½ � CCCPV <sup>þ</sup> ð Þ OMCCPV � CRF (28)

CEL ¼ ðNEC � PEL,maxÞ � ½ � CCEL þ ðRCEL � SPPWÞ þ ð Þ OMCEL � CRF (29)

CFC ¼ ðNFC � PFC,maxÞ � ½ � CCFC þ ðRCFC � SPPWÞ þ ð Þ OMCFC � CRF (30)

Eqs. (34) and (35). The interest rate considered in the current analysis is 6% [36].

CCPV ¼ NPCPV � NCM � PMJC,max

tPF ¼ 0 (25)

L1 < STH<sup>2</sup>ð Þ<sup>f</sup> � STH<sup>2</sup>ð Þ<sup>i</sup> < L2 (26)

tance and has main priorities, before proceeding to the second objective function.

PSFT <sup>¼</sup> <sup>X</sup>

strategy to find optimum system size configuration.

110 Advances In Hydrogen Generation Technologies

objective function for such a study is defined by Eq. (26).


Table 2. Costing parameters considered for techno-economic evaluation of CPV-hydrogen system [17].

#### 6. System optimization algorithm and strategy with micro-GA

As mentioned earlier, the micro-genetic-algorithm (micro-GA) is considered as the main optimization algorithm to search for the optimum system configuration, as per defined objective functions. Based upon the defined performance model of CPV-Hydrogen system, the simulation and optimization program was developed in FORTRAN as per strategy shown in Figure 5. The program consists of two parts. The first part is associated with the performance simulation of CPV-Hydrogen system, based upon the developed model. The second part is associated with the system optimization, based upon the defined objective function, to find the optimum system size configuration using micro-GA. There are only two sizing parameters which are given as input to the program to be optimized, that is, the total number of CPV modules needed and the amount of initial hydrogen needed at the start of simulation and optimization cycle. However, the remaining parameters are calculated from these input parameters. The micro-GA is run with a population size of 5 and maximum 300 generations. The results of the study are presented in the next section.

consumer load demand. The actual load data were in megawatt units, which are scaled down to watts. The shown ambient temperature data were obtained from NEA (national environment agency) Singapore. The weather data shown in Figure 6 act as the primary input for which the system performance is calculated and the optimum configuration of CPV-Hydrogen

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Figure 6. DNI weather data and consumer electric load for Singapore [19].

As per the proposed system performance model, presented weather and load input data, energy management and optimization strategies and objective functions, the system optimization was performed by the developed program in FORTRAN using micro-GA. The optimization results are shown in Figure 7. From these results, it can be seen that the optimization calculations converge after 52 generations, with minimum overall system cost. It can also be seen that for all generations, the PSFT factor is zero and the difference between states of stored hydrogen, before and after the simulation cycle, is within the defined limits of L1 and L2. However, the stored hydrogen difference is closer to L1 limit, which shows that the system has successfully restored its state to initial conditions and it is ready for the next-year performance cycle. If the overall system cost is broken down, then the details are shown in Figure 7. It can be seen that the electrolyzer has the major cost proportion as 51%, followed by the CPV with 35%. In total, these two sub-systems account for 86% of the total system cost. It is important to mention here that the higher cost for electrolyzer is due to its replacement cost as the system has a lifetime period of 10 years and the overall CPV-Hydrogen system is targeted for a 20-year

In order to see the variations in the stored hydrogen energy against the operational period, the state of hydrogen and oxygen storage tanks is shown in Figure 8. It can be seen that the state of stored gases is decreasing during initial months of operation. This is because of the fact that for

system is proposed.

lifetime period.

Figure 5. System size optimization strategy for standalone operation of CPV-hydrogen system [17].

### 7. Results and discussion

In order to simulate the system performance, the direct normal irradiance (DNI) data, obtained under tropical conditions of Singapore, at the rooftop of EA building of National University of Singapore, are shown in Figure 6. The DNI data were collected for a 1-year period from September 2014 to August 2015, at an interval of 1 s. To capture the DNI data, the pyrheliometer from Eppley Laboratory was mounted onto a two-axis solar tracker with tracking accuracy of 0.1�. Figure 6 also shows the electrical load data, obtained from EMA (energy market authority) Singapore at an interval of 30 min. This acts as the input to the simulation cycle for

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Figure 6. DNI weather data and consumer electric load for Singapore [19].

7. Results and discussion

112 Advances In Hydrogen Generation Technologies

In order to simulate the system performance, the direct normal irradiance (DNI) data, obtained under tropical conditions of Singapore, at the rooftop of EA building of National University of Singapore, are shown in Figure 6. The DNI data were collected for a 1-year period from September 2014 to August 2015, at an interval of 1 s. To capture the DNI data, the pyrheliometer from Eppley Laboratory was mounted onto a two-axis solar tracker with tracking accuracy of 0.1�. Figure 6 also shows the electrical load data, obtained from EMA (energy market authority) Singapore at an interval of 30 min. This acts as the input to the simulation cycle for

Figure 5. System size optimization strategy for standalone operation of CPV-hydrogen system [17].

consumer load demand. The actual load data were in megawatt units, which are scaled down to watts. The shown ambient temperature data were obtained from NEA (national environment agency) Singapore. The weather data shown in Figure 6 act as the primary input for which the system performance is calculated and the optimum configuration of CPV-Hydrogen system is proposed.

As per the proposed system performance model, presented weather and load input data, energy management and optimization strategies and objective functions, the system optimization was performed by the developed program in FORTRAN using micro-GA. The optimization results are shown in Figure 7. From these results, it can be seen that the optimization calculations converge after 52 generations, with minimum overall system cost. It can also be seen that for all generations, the PSFT factor is zero and the difference between states of stored hydrogen, before and after the simulation cycle, is within the defined limits of L1 and L2. However, the stored hydrogen difference is closer to L1 limit, which shows that the system has successfully restored its state to initial conditions and it is ready for the next-year performance cycle. If the overall system cost is broken down, then the details are shown in Figure 7. It can be seen that the electrolyzer has the major cost proportion as 51%, followed by the CPV with 35%. In total, these two sub-systems account for 86% of the total system cost. It is important to mention here that the higher cost for electrolyzer is due to its replacement cost as the system has a lifetime period of 10 years and the overall CPV-Hydrogen system is targeted for a 20-year lifetime period.

In order to see the variations in the stored hydrogen energy against the operational period, the state of hydrogen and oxygen storage tanks is shown in Figure 8. It can be seen that the state of stored gases is decreasing during initial months of operation. This is because of the fact that for

Figure 7. Optimization curve and cost breakdown of the system [19].

these months, the received DNI is also decreasing, which can be seen in Figure 6. These months represent the rainy season of Singapore and that is why the received DNI is lower for these months. The share of fuel cell, to meet the load demand during diurnal period, is also shown in Figure 8. It can be seen that the fuel cell share is also increasing for these months and for December, it hits about 60%. However, after this rainy season period, the state of energy storage tanks starts to recover and the received DNI as well as the fuel cell share also stabilize. But there is still about 25% fuel cell load share for each month. This is because of the tropical weather conditions as one cannot get clear sky for the whole day. However, the good thing is the stabilized weather conditions with less variations, which is good for the reliable operation of the designed system.

normalized for per m2 area. The trend of system output for both parameters is similar to the received monthly DNI, except for rainy season. The electrical output of the system dropped about three times in December than the usual operating month. That is why, a sharp decrease in the state of stored hydrogen was observed during this period. As the presented data are in per m2 format, therefore, if the main objective of the system is to produce electricity or hydrogen, instead of standalone operation, then the system can be designed based upon the presented

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Figure 9. Summary of CPV-hydrogen system performance for electricity and hydrogen production [19].

Table 3 shows the overall summary of optimized CPV-Hydrogen system for standalone operation for defined objective functions and with minimum system cost. The interesting thing to be observed is that the power rating of electrolyzer is same as CPV. This is because of the fact that the design of electrolyzer is depending upon the maximum excess power available, which is proportional to the size of CPV. That is why a higher portion of cost was associated with the electrolyzer, even higher than the CPV. That was due to the replacement cost. It can also be seen that the power rating of CPV system is very large as compared to the consumer load. First, this is because of the fact that the system is designed to be operated in standalone mode while also meeting the system operational power needs, and at night, there is only stored hydrogen which can supply power to the load. Therefore, enough excess power is generated

Parameter description Units Value No. of CPV modules 758 Rated power of CPV system kW 94.75 Electrolyzer rated power kW 91 No. of electrolyzer cells connected in series 67 Total hydrogen production kg 622.22 Total oxygen production kg 2469.23 Total water consumption kg 2780.28 Fuel cell rated power kW 7.33 No. of cells of fuel cell connected in series 64

performance data.

In order to further analyze the performance of CPV-Hydrogen system from its power production point of view, Figure 9 shows the monthly average values of CPV electrical output and the Hydrogen output of CPV-Hydrogen system for the period of 1 year. The presented data are

Figure 8. State of energy storage tanks and fuel cell share on monthly basis [19].

Concentrated Photovoltaic (CPV): Hydrogen Design Methodology and Optimization http://dx.doi.org/10.5772/intechopen.78055 115

Figure 9. Summary of CPV-hydrogen system performance for electricity and hydrogen production [19].

these months, the received DNI is also decreasing, which can be seen in Figure 6. These months represent the rainy season of Singapore and that is why the received DNI is lower for these months. The share of fuel cell, to meet the load demand during diurnal period, is also shown in Figure 8. It can be seen that the fuel cell share is also increasing for these months and for December, it hits about 60%. However, after this rainy season period, the state of energy storage tanks starts to recover and the received DNI as well as the fuel cell share also stabilize. But there is still about 25% fuel cell load share for each month. This is because of the tropical weather conditions as one cannot get clear sky for the whole day. However, the good thing is the stabilized weather conditions with less variations, which is good for the reliable operation

Figure 7. Optimization curve and cost breakdown of the system [19].

114 Advances In Hydrogen Generation Technologies

Figure 8. State of energy storage tanks and fuel cell share on monthly basis [19].

In order to further analyze the performance of CPV-Hydrogen system from its power production point of view, Figure 9 shows the monthly average values of CPV electrical output and the Hydrogen output of CPV-Hydrogen system for the period of 1 year. The presented data are

of the designed system.

normalized for per m2 area. The trend of system output for both parameters is similar to the received monthly DNI, except for rainy season. The electrical output of the system dropped about three times in December than the usual operating month. That is why, a sharp decrease in the state of stored hydrogen was observed during this period. As the presented data are in per m2 format, therefore, if the main objective of the system is to produce electricity or hydrogen, instead of standalone operation, then the system can be designed based upon the presented performance data.

Table 3 shows the overall summary of optimized CPV-Hydrogen system for standalone operation for defined objective functions and with minimum system cost. The interesting thing to be observed is that the power rating of electrolyzer is same as CPV. This is because of the fact that the design of electrolyzer is depending upon the maximum excess power available, which is proportional to the size of CPV. That is why a higher portion of cost was associated with the electrolyzer, even higher than the CPV. That was due to the replacement cost. It can also be seen that the power rating of CPV system is very large as compared to the consumer load. First, this is because of the fact that the system is designed to be operated in standalone mode while also meeting the system operational power needs, and at night, there is only stored hydrogen which can supply power to the load. Therefore, enough excess power is generated



A detailed energy management technique, performance model and optimization strategy is proposed for standalone operation of CPV-Hydrogen system. The proposed model and strategy is successfully developed and implemented using micro-GA in FORTRAN programming. The overall system size is optimized for uninterrupted power supply to the consumer load with minimum cost. The proposed dynamic strategy is not based upon hourly performance of the system but it also restores the system to its initial state and prepares it for varying weather conditions. The system is not only designed to handle hourly weather variations but it also efficiently performs during seasonal weather variations. Such tech-economic optimization and analysis can be performed for any load demand and at any condition. Moreover, the proposed methodology can be integrated into commercial simulation tools so that they can become

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)

)

capable of handling CPV in their analysis.

Ib direct normal irradiance (DNI), (W/m<sup>2</sup>

)

CC solar concentration at solar cell area (Suns)

Acon area of solar concentrator (m2

NCM number of solar cells in one panel

ISC solar cell short circuit current (A)

VOC solar cell open circuit voltage (V)

STH2 state of hydrogen storage (kg)

TC solar cell temperature (�C) STO2 state of oxygen storage (kg)

STW state of water storage (kg)

PC solar cell power (W)

ELC electrical load demand of consumer (W)

Imppt solar cell maximum power point current (A)

Vmppt solar cell maximum power point voltage (V)

STMH2 maximum hydrogen storage capacity (kg)

AC solar cell area (m2

IC solar cell current (A)

VC solar cell voltage (V) NPCPV number of CPV panels

Nomenclature

Table 3. Summary of optimized CPV-hydrogen system design for standalone operation [17].

during the daytime to have enough storage for night operation. Second, as the weather input data were based upon tropical climate conditions, therefore, the system is oversized to have enough hydrogen generated to sustain during rainy period. This design summary is only for the mentioned location and the consumer load data. However, the main objective of proposing design methodology for standalone operation of CPV-Hydrogen system is achieved which can be easily implemented for any load requirement and weather conditions.
