Simulation of Hybrid PV Solar System with Fuel Cell in MATLAB

Simulink

FERNANDA REMACHE, JESSICA CASTILLO, CARLOS QUINATOA, LUIS CAMACHO

Department of Electrical Engineering,

University Technical of Cotopaxi,

Av. Simón Rodríguez s/n Barrio El Ejido Sector San Felipe,

ECUADOR

Abstract: - This paper discusses the simulation of a fuel cell hybrid solar photovoltaic system in MATLAB

Simulink. To achieve the stated objective, it is proposed to dynamically model a hybrid system using Simulink

to analyze its performance and optimize its operation, improving the efficiency and stability of the power supply

under different operating conditions, then the dynamic simulation model employs energy conversion equations

of the solar PV panel and the fuel cell to meet the electrical load requirements, where the solar panel uses solar

radiation and ambient temperature, while the fuel cell produces electricity by electrolysis of water, which separates

hydrogen from oxygen. In the first graph, the power at load increases sharply at 0.5 seconds and stabilizes between

0.5 and 1.5 seconds, with a sharp drop at 1.6 seconds. In the second graph, PV power increases at 0.5 seconds,

reaching 80 kW between 0.5 and 1.5 seconds, dropping to zero at 1.6 seconds. The fuel cell shows similar behavior.

These results indicate a coordinated operation for a stable power supply to the load, responding efficiently to

changes in demand and generation.

Key-Words: - Problems, Workers, Radiation, Electromagnetic, Electric.

Received: April 1, 2024. Revised: August 2, 2024. Accepted: September 2, 2024. Published: October 2, 2024.

1 Introduction

Renewable energy sources are essential to mitigate

the environmental impacts associated with traditional

energy sources. Several studies highlight the positive

results of renewable energy on the environment,

such as reducing pollution, fighting global warming

and combating climate change, [1]. However, it

is essential to evaluate the environmental impacts

of renewable energy projects themselves, with a

specific focus on wind and solar energy as primary

sources. While energy installations can pose

challenges to aesthetics, ecosystems, and public

health, solar energy stands out for its minimal adverse

environmental effects. In addition, [2], [3], academic

research emphasizes the influence of transitioning to

renewable sources of energy to address issues related

to economic progress, greenhouse gas emissions, and

the advancement of sustainable economies in middle-

and high-income countries, [4].

Renewable energies, such as solar panels

and fuel cells, are vital elements of sustainable

energy production. Solar panels harness sunlight

to produce electricity, representing a renewable

and environmentally friendly source of energy,

[5]. In cases of insufficient sunlight, fuel cells

act as supplementary energy sources, ensuring

uninterrupted power supply and system reliability,

[6]. The integration of solar panels and fuel cells in

hybrid systems improves the efficiency of energy

production and reduces the environmental impact

by avoiding the emission of harmful substances

and using renewable sources, [7]. In addition, fuel

cells can store surplus energy from solar panels by

generating hydrogen through electrolysis, allowing

energy to be stored and used during periods of low

sunlight. This partnership between solar panels and

fuel cells embodies a sustainable strategy to meet

energy demands while addressing environmental

concerns.

Increasing energy demand and the urgency to

decrease greenhouse gas emissions have driven the

advancement of renewable energy systems. Hybrid

systems, which combine solar PV with other sources

such as fuel cells, are gaining importance due to

their ability to provide a more reliable and efficient

power supply. However, the complexity associated

with integrating and monitoring these systems

requires advanced simulation tools to evaluate

their functionality and improve their efficiency.

MATLAB Simulink is presented as a robust platform

for modeling and simulation of dynamic systems,

including renewable energy systems.

To address these difficulties, it has been proposed

to integrate hybrid systems that combine solar PV

with alternative generation sources, such as fuel cells.

Merging these two technologies into a hybrid system

has the potential to fix the stability and efficiency of

energy supply. Although, the inclusion of multiple

energy sources brings additional complexities

in system design, monitoring and operation.

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Improving these hybrid systems requires advanced

simulation tools that can accurately replicate the

dynamic performance of each component and their

interactions.

Simulation of a hybrid solar PV system with a

fuel cell in MATLAB Simulink presents a promising

approach to address the growing energy demand

while mitigating environmental impact. The

integration of solar power and fuel cells enables

the establishment of a reliable and efficient power

generation configuration, [5]. The predominant

use of photovoltaic panels for power generation,

supplemented by backup fuel cells during periods

of low sunlight or peak demand, ensures a constant

power supply, [8], [9]. The addition of a fuel

cell allows energy storage to be utilized during

non-generation periods, which improves system

reliability and meets various load requirements,

[7]. Through simulations performed in MATLAB

Simulink, the efficiency and performance of these

hybrid systems can be thoroughly evaluated

and improved, demonstrating their potential for

sustainable energy production.

The performance of the solar photovoltaic hybrid

fuel cell system involves the integration of solar

panels and fuel cells to provide a consistent and

environmentally friendly energy source for domestic

use. Typically, this hybrid configuration comprises

a photovoltaic array, a fuel cell, an electrolyzer and

power control units, [10]. During the day, the solar

panels harness solar energy to generate electricity,

while the fuel cell batteries use hydrogen produced

through the electrolysis of water from the solar

panels to produce energy in low light conditions or

at night, [11]. System efficiency can range from

1.75% to 7.66%, depending on variables such as

current density and overall system design, [11]. By

combining these technologies and including energy

storage solutions such as supercapacitors, this hybrid

system ensures a reliable and uninterrupted power

supply for residential energy consumption, improving

the intelligence and energy efficiency of the home.

The efficiency of a hybrid solar photovoltaic

fuel cell system exceeds that of traditional energy

sources, illustrating a promising approach to clean

energy generation and utilization. By combining

photovoltaics (PV) and hydrogen fuel cells (FC),

these integrated systems can achieve impressive

operating efficiencies, [11], with an overall efficiency

of up to 47.9%, significantly higher than typical

energy sources. Harnessing sustainable sources

of energy such as solar and wind, together with

energy technologies such as hydrogen photovoltaic

fuel cells (HPF), presents a viable solution to the

world’s energy dilemma by lowering costs and

improving reliability in power generation, [12]. The

integration of photovoltaic and PV technologies not

only improves efficiency, but also helps reduce

greenhouse gas emissions, making it a sustainable

and environmentally friendly energy production

alternative for the future, [13].

Model and dynamically simulate a fuel cell hybrid

solar PV system using MATLAB Simulink to analyze

its performance and optimize its operation, improving

the efficiency and stability of the power supply under

various operating conditions.

2 Dynamic Model of Fuel Cell

Hybrid Solar Photovoltaic Solar

System

This chapter is structured in several sections, each

of which addresses the specific cell types to be used

and the corresponding formulas for determining the

voltage, current, power and efficiency of solar PV

panels, fuel cells and hybrid systems.

2.1 PEM type fuel cell

PEM batteries use hydrogen as a fuel and oxygen

as oxidizer to facilitate the chemical reaction that

generates electrons. These electrons then flow

through an external electrical circuit, resulting in

the production of water as a by-product of the

process. This particular type of battery system is

widely recognized for its environmentally sustainable

characteristics, [14].

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Figure 1 shows the general performance of a fuel

cell, showing the input of fuel, such as hydrogen, on

the anode side, and the input of the oxidant, oxygen,

on the cathode side, [15]. At the anode, the electron

separates from the hydrogen atom and, following

an external electrical circuit, moves through the

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charge until it reaches the cathode. Meanwhile,

the hydrogen atoms travel through the electrolyte

to combine with the oxygen atom, resulting in the

production of wastewater. This process generates

heat, with temperatures ranging from 20 °C to 100 °C,

highlighting the additional benefit of heat generation.

2.2 Photovoltaic solar panel system

Each of the equations used to perform the dynamic

modeling of the hybrid solar PV/fuel cell system is

described below.

Solar photovoltaic panels are composed of

modules, and each module contains arrays and cells.

Equation 1 must be used to achieve a dynamic current

output, [16].

I=Np∗Iph−Np∗Io∗"exp U

Ns+I∗Rs

n∗Vt!−1#−Ish

(1)

The parameter Nprepresents the number of cells

connected in parallel, while Nsindicates the number

of cells connected in series. The variable Uindicates

the voltage of each individual cell. To determine the

actual photo Iph, it is essential to use equation 2, [16].

Iph = [Isc +Ki(T−298)] ∗Ir

1000 (2)

Isc is the abbreviation of the short-circuit

current expressed in amperes (A). Kiindicates

the short-circuit current of the cell under standard

test conditions at 25 °C with a solar irradiance of

1000 W/m². T represents the operating temperature

measured in Kelvin (K), while Irrepresents the solar

irradiance in units of watts per square meter (W/m²).

To obtain the current saturation Irs, equation 3

introduced by [16], should be used.

Irs =Isc

hexp(qVoc

NsKnT)−1i(3)

The electronic charge q, denoted 1.16x10−19 C,

is a fundamental constant. The open circuit voltage,

Voc, is a crucial parameter in electrical circuits. The

ideality factor of the diode, represented by n, plays

an important role in its performance. Boltzmann’s

constant, denoted K and equal to 1.3805 x 10−23 J/K,

is essential in statistical mechanics.

The saturation current of the Iomodule changes

with cell temperature. Equation 4 is used to determine

this current, [16], [17].

Io=Irs T

Tr3

exp q∗Ego

nk 1

−1

Tr (4)

The location of Irs has been identified as

corresponding to equation 3, where Trrepresents

the nominal temperature set at 298.15 K, and

Ego indicates the broadband energy of the

semi-conductor, which is equivalent to 1.1 eV.

To determine the current Ish, information on the

series resistance Rsand shunt resistance Rsh in

ohms, along with the diode thermal voltage, must be

possessed. Subsequently, equation 5 is used once

these parameters have been identified, [16].

Ish =

Ns+I∗Rs

Rsh

(5)

Once the asset current of the PV solar panel I

(Ipv)is established, the power can be calculated by

applying Ohm’s law (equation 6).

Ppv =Ipv ∗Vpv (6)

The value of Vpv is set according to the

specifications provided in the data sheet of the solar

PV panel.

To evaluate the efficiency of the solar PV system,

a crucial step is to have a clear understanding of the

input power Qin, which is determined by equation 7

described by [18].

Qin =Ir∗Sp∗αabs (7)

The solar irradiance in W/m² is denoted as Ir,

while the area of the solar PV panel is represented

by Sp, and αabs represents the overall absorption

coefficient.

By acquiring the powers Qin and Ppv, the

efficiency can be determined using equation 8, [18].

ηpv =Qin

Ppv

(8)

2.3 Theoretical model of PEM fuel cell

system

For measuring the voltage of a fuel cell, a first step is

to analyze the dynamic model. Research in the field

of PEM fuel cell articles has been ongoing for many

years. As a result, models that have been presented in

advance will be used.

To convert the excess solar energy generated into

hydrogen, it is essential that the electrolyzer matches

the power output of the solar photovoltaic panels.

To determine the hydrogen production rate,

equation 9 described by [19], is used.

xH= 5,18 ∗e−6∗Ie(9)

When xHis expressed in moles/s, Ierepresents

the electric current flowing through the electrode

terminals.

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To determine the energy content represented

by hydrogen, equation 10 should be used for its

calculation, [19].

EH2=Load

ηF C

(10)

The load represents the upper and lower limits of

energy storage in kilowatt-hours (kWh), while F C

indicates the fuel cell efficiency, calculated using

equation 10.

Once the amount of energy stored in hydrogen is

identified, it is essential to establish the corresponding

mass of hydrogen mH2associated with this energy.

To achieve this, the gross calorific value (GCV) of the

hydrogen molecule is used using equation 11, [19].

mH2=EH2

P CSH2

(11)

The GCV of hydrogen is 142 MJ/kg or 39.4 kW/h.

In equation 11, it is given as P CSH2and the mass of

hydrogen is quantified in kg/sec.

At a temperature of 15 °C and an atmospheric

pressure of 1 atm (1.013 bar full), the thickness of

hydrogen is calculated to be 0.085 kg/m³. Using this

density value, the volume of hydrogen (VH2) can be

determined by applying equation 12, [19].

VH2=mH2

0,085 (12)

In mH2is the mass of hydrogen calculated above

with equation 11.

Gas compression systems are often identified as

significant power consumers. By using Equation

13, the amount of compression power required to

compress a gas from an inlet pressure P e(atm)to an

outlet pressure P s(atm)can be calculated, [19].

Pcomp =qgasTeCp

ηc(Ps

)γ−1

γ−1(13)

The symbol qgas represents the mass flow rate

of the kg/s, specifically hydrogen. Teindicates the

access temperature of the gas, which is measured in

Kelvin (K). Cprepresents the calorific value of the

gas (J/kg.k). The symbol ηcstands for the efficiency

of the compressor output. Finally, γrepresents the

isentropic gas coefficient.

Using equation 14, the compression energy Ecomp

can be determined, [19].

Ecomp =Mass∗γ−1

PeVo

ηc(Ps

)γ−1

γ−1(14)

The mass of hydrogen can be given as mass in

kg/s, while the initial volume of the gas is represented

by Vo. In the specific hydrogen scenario, the initial

volume is calculated as 11.11 m³/kg.

The theoretical framework of a PEM is presented

below to obtaining voltage, power and efficiency

acquisition. The transport of protons across the

membrane and electrons through the external circuit

generates a voltage disparity between the fuel cell

terminals.

The stress produced can be determined by

equation 15, [20]

Us=Uth −Uact −Uohm −Uconc (15)

The theoretical Uth stress is determined

using equation 16 in this model, which requires

consideration of temperature variations with respect

to the reference temperature, [20] .

Uth = 1,2297+(T−298,15)∆So

nF +RT

nF ln Ph2Po21/2

Po1/3 

(16)

The operating temperature of the cell indicated by

T, Sorepresents the change in reaction entropy of

liquid water under standard test conditions, with a

value of -0.1634 kJ/k/mol. The variable nrepresents

the number of electrons by mole (n = 2), while F

indicates the Faraday constant (96485.309 C/mol)

and Rrepresents the universal gas constant (8,31451

J/k/mol).

To determine the transient voltage, which

variations with time, it is essential to use equation 17

for the calculation, [21].

Uact =dVact

dt =Is

−Uact

(17)

The location of Isis associated with the current

(A), while Crepresents a fixed value of 108.75 F

and, finally, there is an activation resistance Ra. To

determine Ra, one must use equation 18 and have

information on the decrease of the activation voltage

ηact and Isin relation to the current, [21].

Ra=−ηact

(18)

Equation 19 is applicable in cases where there is a

decrease in the activation voltage, [22].

ηact = 0,9514 + 0,00312T−0,000187T ln(i)+

7,4x10−5Tln(Co2)(19)

The temperature of the fuel cell, denoted as T in

kelvin, correlates closely with the temperature of the

cell. The static current through the stack, represented

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as i (A), and the dissolved oxygen concentration,

denoted Co2, are determined by equation 20, [22].

Co2=Po2

5,08x106exp −498

T(20)

The oxygen pressure on the cathode side is

indicated as Po2, while the temperature is represented

by the symbol T.

By determining the voltage at Us, it is possible to

calculate the power output of the fuel cell by applying

Ohm’s law (equation 21):

Ppc =Ipc ∗Us(21)

The Ipc value is set according to the specifications

provided by the fuel cell manufacturer.

To evaluate the efficiency of the ηpv fuel cell,

equation 22 is used.

ηstack = 0,83 ∗Us

Uth

(22)

2.4 Model of charge controller

The output power of the charge controller is usually

defined by equation 23, [18], [23], [24].

PCont−dc =VBat (Irect +IP V +IF C )(23)

VBat represents the product of the nominal DC

voltage of a given system, while Irect indicates the

rectifier output current. IP V indicates the current

produced by the solar photovoltaic panels, and IF C

stands for the current produced by the fuel cell.

2.5 Battery charging and discharging model

The batteries are responsible for accumulate the

surplus energy regulated by the charge controller

(Figure 2). In addition, their function includes

monitoring voltage levels to ensure that they remain

within the specified range, thus avoiding excessive

discharges and preventing overcharging.

During the charging period, the correlation

between voltage and current is represented by

equation 24, [23].

V=Vr+I(0,189

1,142−soc+Ri)

AH + (soc −0,9) ln

300 I

AH + 1,0(24)

The value of Vris calculated using equation 25.

Vr= 2,94(1,0−0,001(T−25◦C)) (25)

Battery current, indicated as I, represents the

flow of electrical charge in amperes. Ah, which

stands for ampere-hour, quantifies the total charge

capacity of the battery as it discharges, soc or state

of charge, indicates the proportion of charge being

used at a specific time relative to the maximum charge

capacity.

However, equation 26 is used for the discharge

cycle, [23].

V=Vr+I

AH +0,189

soc +Ri(26)

The internal resistance of the cell, indicated as Ri,

and the ambient temperature, represented by T, are

considered in the determination of Riby establishing

equation 27.

Ri= 0,15(1,0−0,02(T−25◦C)) (27)

2.6 Inverter model

The characteristics are defined by the input power of

the inverter and the output power it delivers. Inverters

invariably incur losses during the conversion process,

which underscores the importance of considering the

manufacturer’s specifications, [25].

Equation 28 is used to calculate the output inverter

power.

Pinv−ipηinv =Pinv−op (28)

2.7 Hybrid system efficiency

Ultimately, the energy conversion efficiency of solar

PV panels and fuel cell systems is determined by

applying equation 29.

ηsh = (Ppv +Ppc)/(Ir∗Sp)(29)

2.8 General flow diagram

The schematic diagram presented in Figure 2 shows

the sequential progression within each numerical

iteration of the dynamic simulation model designed

for the hybrid system. These iterations correspond to

equations 1 to 29, as described in chapter three.

The process begins with the input of the essential

parameters required for the calculation. These

parameters include Ir, which represents the radiation

in W/m², T, which indicates the ambient temperature,

I, which represents the rated current, and V, which

represents the rated voltage of the solar PV panel, as

distributed by the manufacturer. She represents the

area of the solar PV panel, along with fuel cell-related

parameters such as I and V, among others. Once

the input of these parameters is finalized, each of

the aforementioned equations is solved sequentially,

starting from equation 1 to equation 8. This sequence

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allows the extraction of energy from the solar PV

panel system. Moving on to equations 9 through

22, the calculation involves determining the power

output of the fuel cell, which encompasses the energy

conversion process for hydrogen mass determination

and energy storage. Equation 23 is dedicated to

obtaining power from the charge controller. In

addition, equations 24 to 27 provide information

on the loading and unloading voltage of the system

batteries. Equation 28 facilitates the calculation of the

alternating current for the system, while equation 29

evaluates the efficiency of the hybrid method.

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3 Validation of a Hybrid

Photovoltaic and Fuel Cell Method

This chapter presents the results of simulations

performed within the program, illustrating various

voltage curves, current trends, load variations,

efficiency levels and other relevant data through

screenshots.

Figure 3 shows the fundamental components of a

hybrid solar PV panel and fuel cell system. In this

schematic, the interconnection of each component is

depicted, starting with the solar PV panel linked to

the charge controller, which establishes a connection

between the battery bank and one of its terminals. A

main capacity of the charge controller is to protect the

battery bank against overcharging by maintaining a

specific voltage level at its terminal.

In cases where there is a sudden voltage surge that

exceeds the current threshold allowed for the load, the

load will shed; as both current and voltage normalize,

it is reconnected, similar to the process observed when

current is at a minimum in the PV solar panel.

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The system consists of the following:

• 6 kW Fuel Cel

• 85.2 KW PV Solar System

• 80 KW

• 100 KW Inverter with controller based on PI

Controller.

The system supplies from 0.5 seconds to 1.5

seconds.

3.1 Fuel cell general data

To facilitate the fuel cell simulation, additional

subsystems must be integrated to monitor the fuel

cell behavior. This includes the incorporation of a

gas supply system and a Boost converter with RL

load, operating at a constant voltage of 100 V, as

shown in Figure 4. The simulink block is equipped

with predefined outputs that allow observing various

parameters of the fuel cell, such as efficiency, gas

consumption, flow ratio, composition, Tafel curve

slope, exchange current, Nernst voltage, open circuit

voltage, as well as voltage and current, [26].

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Simulink incorporates three PEM-type fuel cell

stacks that are precharged to power levels of 1.26 kW,

6 kW, and 50 kW. The 6 kW battery was chosen to be

used for this particular scenario as detailed in Table 1

with its corresponding parameters.

Table 1. Parameters of the 6 kW fuel cell stack

Fuel Cell Stack Parameters

Battery power rating 5998.5 W

Maximum battery power 8325 W

Fuel cell resistance 0.07833 Ω

Single cell Nerst voltage (En)1.1288 V

Nominal use of H299.56 %

Nominal utilization O259.3 %

Nominal fuel consumption 60.38 SLPM

Nominal air consumption 143.7 SLPM

Exchange current (io)0.29197

Exchange ratio (α) 0.60645

Fuel composition (xH2)99.95 %

Oxidant Composition (yO2)21 %

Ratio of nominal fuel flow to utilization

H2nominal

50.06 LPM

Maximum fuel flow ratio at nominal

utilization (H2)

84.5 LPM

Maximum air flow ratio at nominal

utilization of (O2)

506.4 LPM

System temperature (T) 338 K

Fuel feed pressure (PF uel)1.5 bar

Air supply pressure (PF air )1 bar

3.2 General solar panel data

A PV field is established by implementing strings of

PV section connected in parallel, as shown in detail

in Table 2 below.

Table 2. Data of PV array

PV array

Parallel strings 40

Modules linked in series through a chain 20

Module data

Maximum Power (W) 213.15

Open circuit voltage Voc (V) 36.3

Voltage at maximum power point Vmp

(V)

Temperature coefficient of Voc

(%/deg.C)

-0.36099

Cells per module (Ncell) 60

Short-circuit current Isc (A) 7.84

Current at maximum power point Imp

(A)

7.35

Temperature coefficient of Isc

(%/deg.C)

0.102

T cell (deg. C) 45-25

Model parameters

Light-generated current IL (A) 7.8654

Diode saturation current I0 (A) 2.9273e-10

Diode ideality factor 0.98119

Shunt resistance Rsh (ohms) 313.0553

Series resistance Rs (ohms) 0.39381

Each string is composed of section connected in

series, which allows modeling a series of predefined

PV modules present in the model, together with

user-defined PV modules.

It is essential to reach the required power of 213.15

W, for which a total of 10 panels with a power

of 85.2 kW each are modeled. The solar cell is

adjusted according to the specifications provided in

the technical documentation of the solar panel. A

radiation profile is formulated based on the ambient

solar conditions, and specific sensors to measure

current and voltage are strategically placed inside a

singular unit that houses the 10 panels.

Figure 5 shows two graphs: the first graph

illustrates the current-voltage (I-V) characteristics,

while the second graph illustrates the power-voltage

(P-V) characteristics of a photovoltaic (PV) array at

two different temperatures: 25 °C and 45 °C.

Top graph: Current-voltage characteristics (I-V)

• The short circuit current (ISC)remains constant

at both temperatures (25 °C and 45 °C),

approximately 320 A, suggesting minimal (ISC)

variation with temperature. The open circuit

voltage (VOC)is higher at 25 °C (about 700 V)

than at 45 °C (about 640 V). The reduction of

(VOC)with increasing temperature is attributed

to the reduction of the energy band gap in

semi-conductor materials. The I-V curve

presents the typical profile of a PV module:

constant current at lower voltages and a sharp

decrease near the (VOC). At 45 °C, the curve

shifts towards lower voltages compared to 25 °C.

Fig. 5: Solar panel diagram

• The maximum power point (MP P )at 25 °C

is placed at a higher voltage level (about 600

V) and has a higher power output compared

to the (MP P )at 45 °C, which is placed at a

lower voltage (about 550 V). This observation

suggests that the temperature increase leads to a

reduction in both voltage and maximum output

power. The maximum power generated at 25 °C

is high (approximately 2 x 10 ∧5 W) compared

to the maximum power at 45 °C, which is slightly

lower. It is observed that the efficiency and

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power output of the PV modules decrease with

increasing temperature. The P-V curve shows a

linear progression from 0 to its maximum point,

followed by a sudden decrease, which occurs at a

lower voltage at the highest temperature (45 °C)

than at the lowest (25 °C).

3.3 Model applied to hybrid system

The show depicts a block diagram that may constitute

a segment of a hybrid PV system incorporating a fuel

cell. This diagram shows the correlation between

currents, voltages within a three-phase system and

load power. In the context of a hybrid PV system

with a fuel cell, this diagram could exemplify the

calculation of the instantaneous or average power

delivered to a specific load. Hybrid systems combine

various energy sources to ensure an uninterrupted and

reliable power supply. This scheme is often used

to monitor and regulate the power flow within these

systems. Figure 6 below shows the details:

Fig. 6: Load power calculation

In this model, we observe a mathematical model in

which the total load power of the panel is determined.

This calculation includes the current Iabc and voltage

Vabc, with the application of a specific gain of 0.001

for total efficiency.

The block diagram depicted in Figure 7 is

a component of a hybrid photovoltaic system

incorporating a fuel cell. This schematic delineates

the merging of the power outputs derived from these

sources.

Fig. 7: Total power calculation

In the schematic, the PF C1input represents the

power output of the fuel cell, while the PP V input

represents the power output of the PV system. These

power outputs are aggregated by an adder block (+),

and the resulting output [Load1] indicates the total

power supplied to the load, which comprises the

accumulated power from both sources.

In the context of a hybrid PV system integrating a

fuel cell, this scheme serves to elucidate how power

outputs from various energy sources are combined

to ensure a constant and reliable supply. Hybrid

systems take advantage of multiple energy sources to

compensate for each other’s various limitations.

3.4 Load demand of PV Array

Figure 8 shows three graphs showing the power

distributed to the load, the energy generated by

the photovoltaic panel (PV Power) and the energy

produced by the fuel cell (Fuel Cell Power). A review

of each graph is presented below:

Fig. 8: Load demand of PV Array

Power to the Load:

• The ability to supply power to the load is

shown in the graph, which shows variations over

time. There is a noticeable increase in power

around the 0.5 point on the x-axis, followed by

a stabilization phase with recurring variations.

These variations appear to be due to variations

in power generation or load consumption.

PV Power:

• The power output of the PV panel starts from

zero and subsequently undergoes a rapid increase

close to 0.5 on the x-axis. After this initial

increase, the power output stabilizes until it

reaches 1.5 on the x-axis, at which point it

drops sharply to zero. This observation suggests

that the PV panel produces power steadily for a

defined period before stopping its generation.

Fuel Cell Power:

• The fuel cell power starts at a negative

level, which could indicate an initial power

consumption. At about point 0.5 on the x-axis,

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Fernanda Remache, Jessica Castillo,

Carlos Quinatoa, Luis Camacho

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there is a sudden change and the power remains

consistently negative until point 1.5 on the

x-axis, where it changes back to a value close

to zero. This pattern of behavior implies that

the fuel cell is adjusting to variations in power

generation from the PV array to ensure a constant

supply of power to the load.

Together these graphs show the dynamic

interaction between the PV array and the fuel

cell to meet the load demand. It appears that the fuel

cell serves as a key element in ensuring the stability

of the power supplied to the load amidst variations in

PV power output.

3.5 Load demand, PV array and fuel cell

power

Figure 9 shows three graphs showing the relationship

between power and time in a hybrid power generation

system incorporating photovoltaic (PV) panels and

fuel cells. The graphs specifically show the power

supplied to the load, the power produced by the

PV panels, and the power generated by the fuel

cells. A subsequent analysis of each of the graphs is

performed.

Fig. 9: Hybrid system load demand

Power supplied to the load:

• The x-axis of the graph shows the time in seconds

(from 0 to 2 seconds), while the y-axis represents

the power in kW (from -50 to 150 kW). Initially,

the load power remains stable at a low level. At

about 0.5 seconds, there is a sudden increase in

power consumption, which reaches a maximum

of about 75 kW. From 0.5 to 1.5 seconds, there

is a continuous increase in power. At about 1.6

seconds, there is a sharp drop in power, which

returns to a low, but positive value.

Photovoltaic array power (PV):

• The graph shows the time in seconds (0 to 2

seconds) on the x-axis and the power in kW

(0 to 80 kW) on the y-axis. At the beginning,

the PV panel generates no power. A sudden

increase in power occurs around 0.5 seconds,

with a maximum of about 25 kW. From 0.5 to

1.5 seconds, there is a gradual increase in PV

power up to about 80 kW. At about 1.6 seconds,

the power drops sharply back to zero.

Fuel cell power:

• The graph shows the time in seconds (0 to 2

seconds) on the x-axis and the power in kW (-100

to 50 kW) on the y-axis. At the beginning, the

power output of the fuel cell is slightly negative.

At about 0.5 seconds, there is a sudden increase

in the power output, which becomes positive and

stabilizes near 40 kW. After 0.5 and 1.5 seconds,

the power output remains relatively stable with

slight variations. After 1.6 seconds, there is an

abrupt drop in power output, which stabilizes

near zero, albeit slightly positive.

At 0.5 seconds, the PV system and the fuel

cell begin to generate power almost simultaneously,

indicating a synchronized reaction to an initial event

or circumstance. Approximately 1.6 seconds later,

both systems stop generating power at almost the

same time, showing that the system shuts down or

shuts off in a coordinated manner. The combined

power output of the PV system and the fuel cell allows

for a constant power supply to the load. The system

shows remarkable responsiveness to variations in

load demand and power generation conditions.

Sudden power variations within the system involve

the emulation of scenarios such as load connections

or disconnections, as well as variations in solar

irradiance that affect the performance of the PV

panels.

The observed behavior is commonly found in

simulations of hybrid power generation systems, in

which the stability and responsiveness of the system

to sudden variations in operating conditions and load

demand are evaluated.

3.6 PV array and fuel cell energy

production results

Figure 10 shows two graphs showing the power

generation of a hybrid system consisting of

photovoltaic (PV) panels and fuel cells over a

period of time. Subsequently, a detailed analysis of

the results shown in the graphs is presented below.

The graphs presented illustrate the advantages and

limitations of photovoltaic panels and fuel cells. A

thorough examination of these findings is essential for

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developing sustainable energy systems that optimize

the efficiency and stability of electricity supply

by leveraging each technology’s advantages and

addressing its shortcomings.

Fig. 10: Load demand of PV Array

PV Power:

• The graph shows the x-axis indicating the time

in seconds (0 to 2 seconds) and the y-axis

representing the power output in kilowatts (0

to 100 kW). Initially, the power output of

the solar array is non-existent. At around

0.5 seconds, there is a sudden increase in the

power output of the PV array, which reaches

a maximum of approximately 25 kW. Between

0.5 and 1.5 seconds, the output power of the

PV system gradually increases to about 85 kW.

Subsequently, after 1.6 seconds, there is a sudden

decrease in the power output to zero.

Fuel Cell Power :

• The graph illustrates the x-axis representing time

in seconds (from 0 to 2 seconds) and the y-axis

representing power in kilowatts (from -100 to

50 kW). Initially, the fuel cell generates slightly

negative power. At about 0.5 seconds, there

is a sudden increase in power, which reaches a

maximum of about 40 kW. Between 0.5 and 1.5

seconds, the fuel cell power remains relatively

stable, with small variations, hovering around 40

kW. After 1.6 seconds, there is a sudden decrease

in power output, which eventually stabilizes at

zero.

At 0.5 seconds, both the PV and fuel cell

begin to generate power almost simultaneously,

suggesting a coordinated response to a start-up event

or condition. At about 1.6 seconds, both systems stop

generating power almost simultaneously, indicating

a coordinated shutdown or system shutdown. The

combination of the power generated by the PV

and fuel cell allows for a stable power supply,

showing good responsiveness to changes in load

demand and generation conditions. Abrupt changes

in power suggest the simulation of events such as load

connection or disconnection or variability in solar

irradiance for the PV panels.

The observed behavior is commonly observed

in simulations of hybrid power generation systems,

where the stability and responsiveness of the system

to sudden variations in operating conditions and load

requirements are evaluated.

4 Discussion

Analysis of the results illustrates the dynamic

behavior of a microgrid incorporating photovoltaic

generation and a fuel cell in response to changing

load demands. The PV system generates increasing

amounts of power with increasing solar irradiance,

stabilizing at a high level to meet most of the demand

during periods of maximum solar irradiance. In cases

where the PV system’s power output is insufficient

to meet the load demand, the fuel cell turns on

and modulates its power output to make up the

shortfall, as shown in the figures opposite. This

behavior means that both systems are effectively

synchronized and that the transition to electricity

supply is smooth, with minimal impact from load

fluctuations. This highlights the effectiveness of

the implemented control strategy, which ensures a

continuous and stable power supply to the microgrid

even with fluctuations in PV generation and load

demand.

5 Conclusion

Simulation provided by Matlab/Simulink software

was used for the implementation. Data acquired

from a dynamic generation model including a PV

panel and a fuel cell were used for this purpose.

Different voltage, current and power behaviors were

observed in these two devices as a function of solar

PV absorption. These parameters are incorporated

into a consumption model of 6 kW for the fuel cell and

85.2 kW for the PV panels. The whole process was

carried out using energy conversion methodologies.

Consequently, it is feasible to state that the integration

of hybrid power generation is practical for large-scale

deployment, marking an important milestone to

enable and ensure the utilization of clean energy

consumption.

In analyzing all of the results obtained, there is

clear documentation of the operational functionality

of the hybrid generation system, facilitated by the

inclusion of the PEM cell that provides the system

with a reliable energy reserve. It is evident that

the performance and efficiency of the system will be

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Fernanda Remache, Jessica Castillo,

Carlos Quinatoa, Luis Camacho

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Volume 23, 2024

influenced by the solar irradiance experienced during

specific time periods.

However, any energy shortfall will be

supplemented by a backup system and an energy

storage mechanism that encompasses both hydrogen

and battery systems. A noticeable decrease in

the energy supplied to the system will trigger a

systematic energy cycle, as demonstrated by system

load management, culminating in a sustainable and

efficient operational framework.

Acknowledgment:

I would like to express my sincere thanks in the

preparation of my article to Engineers Jessica Castillo

and Carlos Quinatoa, who were my guides in this

process. To the research assistant Luis Camacho for

his support and guidance, which were fundamental for

the success of this work. I am grateful for the time,

patience and knowledge he has shared. I sincerely

appreciate his generosity and professionalism. My

sincere thanks to all.

Declaration of Generative AI and AI-assisted

Technologies in the Writing Process

While preparing this work, the authors used

Grammarly to edit the language. After using this

service, the authors reviewed and edited the content

as needed and took full responsibility for the content

of the publication.

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

I, Fernanda Remache, am responsible for writing the

initial and final version of the article after extensive

research, data collection and simulation analysis.

Professors Jessica Castillo and Carlos Quinatoa

were guides and were crucial in guiding this research.

Luis Camacho also provided technical advice to

ensure the research results. The experience and

knowledge of each of them was essential to maintain

ethical research standards.

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No funding was received for conducting this study.

Conflicts of Interest

The authors state that they have no financial interests

or personal relationships that could affect the work

done in this study.

Creative Commons Attribution License 4.0

(Attribution 4.0 International , CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

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DOI: 10.37394/23201.2024.23.18

Fernanda Remache, Jessica Castillo,

Carlos Quinatoa, Luis Camacho

E-ISSN: 2224-266X

183

Volume 23, 2024