Table Of Contents
1. INTRODUCTION ....................................................................................................... 3
2. LITERATURE REVIEW ................................................................................................ 4
2.1 Introduction to Solar Energy and Photovoltaics ...................................................... 4
2.2 Solar Cell Technologies ........................................................................................ 5
2.3 Modeling and Simulation of Solar Cells .................................................................. 6
2.4 Effect of Environmental Factors on Solar Cell Performance ..................................... 6
2.5 Recent Advances in Solar Cell Technology ............................................................. 7
2.6 Challenges and Future Directions ......................................................................... 8
2.7 Key Parameters in Solar Cell Modeling ................................................................... 9
3. DESIGN METHODOLOGY ........................................................................................ 10
3.1 Data Collection .................................................................................................. 11
3.1.1 Selection of Solar Module Datasheets ........................................................... 11
3.1.2 Data Extraction .......................................................................................... 11
3.2 Model Building ................................................................................................... 12
3.3 Simulation Results Analysis: ............................................................................... 13
3.4 Theoretical Calculations..................................................................................... 14
4. RESULTS AND DISCUSSION ................................................................................... 16
4.1 Designing of Solar Model .................................................................................... 16
4.1.1 Comparison of Datasheet ............................................................................ 16
4.1.2 Simulink Modelling....................................................................................... 17
4.1.3 Analyzing Characteristics of IV and PV curve at STC ....................................... 19
4.1.4 Analyzing Efficiency Through Simulation ........................................................ 22
4.2 Analyzing the Effects of Irradiance, Temperature, RS and RSh ............................... 23
4.2.1 Effects of Irradiance ..................................................................................... 23
4.2.2 Effects of Temperature ................................................................................. 26
4.2.3 Effects of Series Resistance ......................................................................... 29
4.2.4 Effects of Shunt Resistance .......................................................................... 31
4.3 Ongoing Research and Future Directions ............................................................. 34
5. CONCLUSION ........................................................................................................ 35
6. REFERENCE ........................................................................................................... 36
APPENDIX.................................................................................................................. 37
Jinko Solar Cheetah HC 72M - JKM400M – Datasheet ................................................. 38
Trina Solar TSM-400-DEG15M(II) Datasheet ............................................................ 38
List of Table
Table 1 Comparison of values needed for simulation between Jinko Solar Cheetah HC 72M
- JKM400M and Trina Solar TSM-400-DEG15M(II) ........................................................... 16
Table 2 Comparison between Data Sheet values and Simulation values ......................... 22
Table 3 Calculated Parameters under STC .................................................................... 23
Table 4 Calculated Is, Iph And I under Varying Irradiance ............................................... 25
Table 5 Calculated Efficiency under Varying Irradiance .................................................. 25
Table 6 Calculated Is, Iph And I under Varying Temperature ........................................... 28
Table 7 Calculated Efficiency under Varying Temperature .............................................. 28
Table 8 Calculated Is, Iph And I under Varying Series Resistance .................................... 30
Table 9 Calculated Is, Iph And I under Varying Shunt Resistance .................................... 33
Table 10 Calculated Efficiency under Varying Shunt Resistance ..................................... 34
1. INTRODUCTION
Grid-connected solar photovoltaic (PV) systems represent a pivotal advancement in renewable
energy technology, harnessing the abundant and inexhaustible power of sunlight to generate
electricity. Using solar panels, these systems capture solar radiation and convert it into usable
electrical energy. Crucially, these systems are integrated with the existing utility grid infrastructure,
allowing individuals, communities, and businesses to both consume and contribute electricity to
the grid. One of the key advantages of grid-connected solar PV systems is their versatility and
scalability. They can be installed on rooftops, open land, or even integrated into building facades,
making them suitable for a wide range of environments and applications. This flexibility empowers
homeowners, businesses, and entire communities to take control of their energy production and
consumption, reducing reliance on centralized power generation and distribution. Moreover, the
ability to sell excess electricity back to the grid through net metering or feed-in tariffs incentivizes
investment in solar PV systems. This not only provides financial benefits to system owners but
also helps stabilize the grid by balancing supply and demand. In addition to economic advantages,
grid-connected solar PV systems play a crucial role in mitigating climate change and reducing
carbon emissions. By displacing electricity generated from fossil fuels, these systems contribute
to a cleaner and more sustainable energy future. This is especially crucial in areas where coal, oil,
or natural gas are the primary energy sources for the grid, as solar photovoltaics (PV) provide a
greener option that can drastically lower greenhouse gas emissions and air pollution. Furthermore,
the environmental benefits of solar energy extend beyond emissions reduction. Solar PV systems
operate silently and produce no harmful pollutants during operation, minimizing their impact on
local ecosystems and public health. Another compelling aspect of grid-connected solar PV systems
is their reliability and resilience. Unlike traditional centralized power plants, which are vulnerable
to disruptions such as equipment failures, natural disasters, or fuel supply interruptions, distributed
solar PV installations are decentralized and distributed across multiple sites. This enhances energy
security and resilience, as power generation is less susceptible to single points of failure. The
longevity of solar PV technology is also noteworthy. With proper maintenance, solar panels can
reliably produce electricity for 25 years or more, providing a stable and enduring source of energy
over their lifespan. In conclusion, grid-connected solar photovoltaic systems represent a
transformative solution to the dual challenges of energy sustainability and climate change. By
harnessing the power of the sun, these systems offer a clean, reliable, and economically viable
alternative to fossil fuels, paving the way towards a more sustainable and resilient energy future.
This assignment’s goal is to develop and design a solar PV system that is connected to the grid.
The primary focus of this paper will be on the essential design considerations and implementation
strategies that need to be made to ensure that the system integrates well with the current electricity
infrastructure. One of the main objectives of this paper is to provide a comprehensive
understanding of the benefits and challenges associated with establishing grid-connected solar PV
systems.
2. LITERATURE REVIEW
2.1 Introduction to Solar Energy and Photovoltaics
Solar energy, harnessed from the sun's rays, stands as a pivotal, inexhaustible source of power that
has significantly shaped the landscape of renewable energy. Central to tapping this vast, clean
energy reservoir are photovoltaic (PV) systems, which convert sunlight directly into electricity
through the use of semiconductor materials. This technology not only offers a sustainable
alternative to fossil fuels but also plays a crucial role in mitigating climate change, reducing carbon
footprints, and promoting energy independence. PV systems, ranging from small, rooftop
installations to large, utility-scale solar farms, have become increasingly efficient and costeffective, making solar energy a cornerstone in the global transition towards a greener, more
sustainable energy future. Their widespread adoption underscores the growing recognition of solar
power's potential to meet the world's energy demands while preserving the environment for future
generations.
2.2 Solar Cell Technologies
For an in-depth exploration of solar cell technologies, the key distinctions between
monocrystalline, polycrystalline, and thin-film solar cells lie in their efficiency, cost, and
application suitability. Monocrystalline panels, known for their high efficiency (17% to 22%) and
longevity, are considered premium due to their single-crystal silicon composition, making them
ideal for limited space applications. However, they are also the most expensive among the solar
panel types due to their complex manufacturing process Polycrystalline solar cells, characterized
by their blue hue and slightly lower efficiency (15% to 17%), offer a more budget-friendly option.
They are made from multiple silicon crystals, which slightly reduces their efficiency compared to
monocrystalline cells. Despite this, they represent a balanced choice between performance and
affordability for residential and commercial uses [1].
Thin-film solar cells stand out for their flexibility and lower cost, achieved through a simple
deposition process of photovoltaic material on a substrate. This technology includes several types
such as amorphous silicon, cadmium telluride (CdTe), and copper indium gallium selenide (CIGS).
Despite their lower efficiency (generally in the 10–13% range for commercially available
products), thin-film cells excel in low-light conditions and can be applied to diverse surfaces,
including clothing. They are, however, less durable and require more space to generate comparable
power, making them less suitable for residential applications.
Each solar cell technology serves different needs based on efficiency, cost, and application.
Monocrystalline cells offer high efficiency and aesthetics at a higher cost, making them suitable
for areas with limited space. Polycrystalline cells are a middle ground, offering a balance of cost
and efficiency. Thin-film technology, while offering the lowest costs and unique applications due
to its flexibility, presents limitations in efficiency and durability, making it ideal for specific niche
applications rather than mainstream residential use.
2.3 Modeling and Simulation of Solar Cells
The role of modeling and simulation in the development of solar cells is indispensable, serving as
a cornerstone in the design, analysis, and optimization of these renewable energy sources. Through
sophisticated computational models, researchers and engineers can predict the behavior of solar
cells under various environmental conditions, identify potential inefficiencies, and explore new
materials and configurations without the need for costly and time-consuming physical prototypes.
Key methodologies in this domain include numerical simulations that solve the equations
governing the photovoltaic effect, finite element analysis for structural and thermal aspects, and
ray tracing for optical performance.
Among the plethora of software tools available for this purpose, MATLAB-Simulink stands out
as a particularly powerful platform. It offers a versatile environment for simulating the electrical,
thermal, and optical characteristics of solar cells. With its extensive library of predefined blocks
and the ability to create custom functions, MATLAB-Simulink enables detailed modeling of solar
cell behavior, including the effects of temperature, irradiance, and material properties on efficiency.
This capacity for detailed and accurate simulation makes MATLAB-Simulink an invaluable tool
in the iterative process of solar cell design and optimization, allowing for the rapid testing of
hypotheses and refinement of designs to push the boundaries of solar cell efficiency and reliability.
Through these simulations, advancements in technology are accelerated, paving the way for more
efficient and cost-effective solar energy solutions.
2.4 Effect of Environmental Factors on Solar Cell Performance
Environmental factors play a crucial role in determining the performance and efficiency of solar
cells, impacting their current-voltage (I-V) and power-voltage (P-V) characteristics significantly.
Studies in this realm have methodically explored how variables such as irradiance, temperature,
and shading affect solar energy conversion. Irradiance levels directly influence the amount of
electrical power a solar cell can generate; higher irradiance increases the photovoltaic output,
enhancing the I-V curve's slope and consequently raising the maximum power point on the P-V
curve. Temperature variations, conversely, have a nuanced effect: as temperature rises, the
semiconductor material's efficiency decreases, leading to a reduction in the open-circuit voltage
(Voc) and a slight increase in the short-circuit current (Isc). This thermal effect can cause a notable
dip in overall efficiency, underscoring the need for thermal management in solar panel installations.
Shading, even partial, introduces another layer of complexity, significantly distorting I-V and P-V
characteristics [2]. It leads to a reduction in the output power due to the non-linear decrease in the
illuminated cell's performance, which can also induce hot spots, potentially damaging the solar
cells. These studies underscore the importance of considering environmental conditions in the
design and placement of photovoltaic systems to optimize performance. For instance, strategic
positioning to minimize shading, using materials with lower temperature coefficients, and
implementing maximum power point tracking (MPPT) technologies are critical to mitigating the
adverse effects of these environmental factors. Understanding the intricate relationship between
environmental conditions and solar cell performance is essential for maximizing efficiency and
reliability in solar energy systems.
2.5 Recent Advances in Solar Cell Technology
Recent advancements in solar cell technology herald a promising future for solar energy generation,
pushing the boundaries of efficiency and application. Tandem solar cells, which layer multiple
types of solar cells on top of each other to capture a broader range of the solar spectrum, have
shown significant potential in surpassing the efficiency limits of traditional single-junction cells.
By combining materials with different bandgaps, tandem cells can achieve efficiencies previously
deemed unattainable, making solar power even more competitive with conventional energy
sources.
Perovskite solar cells have emerged as a groundbreaking development due to their remarkable
efficiency gains and lower production costs. These cells utilize perovskite-structured materials that
are capable of achieving high efficiency levels comparable to, and in some cases surpassing, those
of silicon-based solar cells. The versatility and ease of manufacturing of perovskite solar cells offer
a pathway to rapid deployment and scaling, potentially transforming solar energy's role in the
global energy mix [3].
Bifacial solar cells, which can absorb light from both the front and back sides, offer another avenue
for efficiency improvement. By harnessing sunlight reflected off the ground or surrounding
surfaces, bifacial cells can generate more electricity than traditional monoracial cells, especially
in conditions with high albedo surfaces or on dual-axis trackers. This technology enhances the
energy yield of photovoltaic installations, making solar power systems more cost-effective over
their lifespan.
Together, these innovations represent a leap forward in solar cell technology, promising to
significantly increase efficiency and reduce the cost of solar energy. As these technologies
continue to mature and enter the market, they will play a pivotal role in accelerating the adoption
of solar energy and its integration into the global energy system, moving us closer to a sustainable
and clean energy future.
2.6 Challenges and Future Directions
The solar energy industry, while on a trajectory of rapid growth and technological innovation,
faces several critical challenges that must be addressed to realize its full potential. One of the
primary hurdles is the continuous improvement of solar cell efficiency. Despite significant
advancements, there's a pressing need to surpass current efficiency ceilings to make solar power
even more competitive with fossil fuels. Research in emerging technologies such as tandem solar
cells and new materials like perovskites offers promising avenues to break through these limits.
Cost reduction remains a pivotal challenge, not just in terms of the initial manufacturing and
installation but also in the operational and maintenance phases. Innovations in materials science,
manufacturing processes, and scalable production methods are essential to driving down costs.
Additionally, developing more durable and longer-lasting solar panels would reduce the lifecycle
costs of solar installations, making solar energy more accessible and appealing to a broader range
of users [4].
Grid integration presents another complex challenge, as the intermittent nature of solar power
requires advancements in energy storage and grid management technologies to ensure reliability
and stability. Future research directions include the development of high-capacity, cost-effective
energy storage solutions and smart grid technologies that can dynamically balance supply and
demand. Implementing such technologies would facilitate the integration of solar energy into the
existing energy infrastructure, enabling a higher penetration of renewable sources in the energy
mix.
2.7 Key Parameters in Solar Cell Modeling
In solar cell modeling, several key parameters are crucial for accurately predicting and analyzing the
performance of solar cells. These parameters include the open-circuit voltage (Voc), short-circuit current
(Isc), fill factor (FF), and efficiency (η). Voc represents the maximum voltage a solar cell can produce when
not connected to an external circuit or load, and it is directly influenced by the material properties and the
temperature of the cell. Isc is the maximum current delivered by the solar cell when its terminals are shorted,
with its value being highly dependent on the intensity of the incident light and the cell's surface area.
The fill factor, another vital parameter, is a measure of the solar cell's quality, indicating the
maximum obtainable power divided by the product of Voc and Isc. It is affected by the cell's
internal resistance and provides insight into the efficiency of the cell at converting sunlight into
electricity. Efficiency (η) is the most critical parameter, representing the ratio of electrical power
output to solar power input, and it summarizes the overall performance of the solar cell.
Together, these parameters are essential for designing, optimizing, and comparing solar cells. By
understanding and manipulating these variables, researchers and engineers can improve solar cell
technologies, making them more efficient and suitable for a wider range of applications. Advanced
modeling techniques, often using software like MATLAB-Simulink, enable the simulation of these
parameters under different conditions, providing valuable insights into the behavior and potential
performance improvements of solar cells.
In conclusion, the development and optimization of solar cell technologies are at a pivotal juncture,
driven by the crucial role of modeling and simulation. These computational tools enable the
prediction and analysis of solar cell performance under varied environmental conditions,
facilitating the exploration of new materials and configurations without the extensive cost and time
associated with physical prototypes. Software platforms like MATLAB-Simulink have emerged
as instrumental in simulating the electrical, thermal, and optical characteristics of solar cells,
thereby accelerating the innovation process.
Environmental factors, including irradiance, temperature, and shading, significantly influence
solar cell performance, affecting key parameters such as the open-circuit voltage, short-circuit
current, fill factor, and overall efficiency. Addressing these influences through strategic design and
placement of photovoltaic systems is essential for optimizing performance and reliability.
Recent technological advancements, including tandem, perovskite, and bifacial solar cells,
represent significant strides towards overcoming the efficiency and cost barriers associated with
traditional solar technologies. These innovations promise to enhance the competitiveness of solar
energy, potentially transforming its role in the global energy mix.
However, the industry faces challenges in efficiency improvement, cost reduction, and grid
integration. Addressing these challenges requires a multidisciplinary approach, emphasizing the
need for ongoing research in materials science, manufacturing processes, and energy storage
solutions. Moreover, the integration of solar energy into the existing grid infrastructure highlights
the importance of developing smart grid technologies and advanced energy storage solutions to
manage the intermittent nature of solar power.
The key parameters involved in solar cell modeling underscore the complexity of optimizing solar
cell design. Understanding and manipulating these variables is crucial for advancing solar cell
technologies, making them more efficient and suitable for a wide range of applications. As the
solar energy industry continues to evolve, the focus on innovation, coupled with the strategic
addressing of existing challenges, will be paramount in shaping a sustainable and clean energy
future.
3. DESIGN METHODOLOGY
The process of designing solar cells follows a structured method to meet specific needs and achieve
optimal performance. Initially, the requirements of the application, such as power output and
efficiency, are analyzed to select the most suitable solar cell technology based on factors like cost
and scalability. Then, the physical design of the solar cell is determined, including the selection of
semiconductor materials and surface modifications to enhance light absorption. Electrical
characteristics such as open-circuit voltage and efficiency are evaluated to ensure effectiveness.
The solar cells are then integrated into photovoltaic modules, considering factors like size and
packaging. Continuous optimization processes refine the design and materials used. Reliability
assessments ensure the solar cells can withstand various conditions, while cost and environmental
impact analyses consider economic and sustainability factors. By following this method, designers
can create solar cells that meet performance and cost requirements for different applications.
3.1 Data Collection
3.1.1 Selection of Solar Module Datasheets
Two datasheets from distinct solar module manufacturers, namely Jinko Solar and Trina Solar,
were chosen for comparison. The decision to compare Jinko Solar and Trina Solar stemmed from
their esteemed reputation in the renewable energy industry. Jinko Solar is renowned for its
production of solar modules known for their outstanding efficiency, whereas Trina Solar is
acknowledged for its utilization of innovative technology and consistent performance. By selecting
these two manufacturers, we aimed to leverage their expertise and market standing to inform our
study effectively. The datasheets from Jinko Solar and Trina Solar served as valuable sources for
making relevant comparisons, allowing us to derive insights crucial for advancing our research in
the field of solar energy.
3.1.2 Data Extraction
For each datasheet of Trina Solar and Jinko Solar, a meticulous extraction process was undertaken
to gather the necessary parameters for comparison. These parameters, including the open-circuit
voltage (VOC), short-circuit current (ISC), cell area, efficiency, temperature coefficients,
maximum power voltage (VMPP), and maximum power current (IMPP), were systematically
collected to ensure a comprehensive analysis and comparison. This rigorous extraction method
ensured the accurate acquisition of all relevant data points required for the study from both sources.
In conducting the comparison between Trina Solar and Jinko Solar, a thorough analysis was
conducted across various parameters extracted from their respective datasheets. Firstly, the opencircuit voltage (VOC) and short-circuit current (ISC) values were scrutinized. Higher values of
VOC and ISC typically indicate better power generation potential, reflecting the modules'
efficiency in converting sunlight into electricity. Additionally, the examination of cell area
specifications provided insights into how the physical dimensions of the modules influence their
performance. Larger cell areas often correlate with higher efficiency and power output, thus
contributing to the comparison. Furthermore, the efficiency values outlined in the datasheets were
meticulously scrutinized. Efficiency serves as a key indicator of how effectively the module
converts sunlight into electricity, with higher efficiency modules being preferred for their ability
to produce more power for a given area.
The temperature coefficients of VOC and ISC were also compared for both manufacturers. Lower
temperature coefficients suggest better stability in performance over a range of temperatures,
thereby aiding in the assessment of each module's efficiency under varying temperature conditions.
Ultimately, based on the comparison results, the optimal choice between Trina Solar and Jinko
Solar was determined. Factors such as performance, efficiency, cost, reliability, warranty, and
compatibility with project requirements and location were carefully considered to make an
informed decision on which solar module best suited the project's objectives. By systematically
evaluating these parameters, a comprehensive comparison was conducted, enabling the selection
of the most suitable solar module for the project's needs.
3.2 Model Building
To select the design for replicating the electrical properties of a high-efficiency solar cell, an
extensive research effort was undertaken, involving an analysis of diverse designs available online.
This comprehensive review encompassed scrutinizing published research papers, technical articles,
and industry-standard practices to grasp the different methodologies and approaches employed in
constructing Simulink models of solar cells. Emphasis was placed on identifying designs that were
well-documented, validated through either experimentation or simulation, and aligned with the
specific objectives of the project, particularly focusing on high-efficiency solar cells.The process
of building the circuit model of a solar cell within Simulink was executed systematically. It
commenced with the inclusion of fundamental elements such as the solar cell itself, along with
components like a current sensor, a voltage sensor, and a variable resistor. These components were
interconnected to simulate various load conditions encountered by the solar cell.
Furthermore, the analysis of the I-V and P-V traits was paramount. By adjusting the load resistor,
the complete I-V and P-V characteristics of the solar cell under Standard Test Conditions (STC)
were scrutinized. This examination enabled a comprehensive understanding of how the solar cell
behaves under different operational scenarios. As the simulation progressed, additional
components were incorporated for deeper analysis. Elements like the PS constant, representing
light intensity, a temperature source for simulating temperature variations, and a shunt resistor
were added. These augmentations facilitated an in-depth exploration of the effects of irradiance,
temperature, and shunt resistance on the circuit.
Each component integrated into the Simulink model served a distinct purpose. The solar cell,
current sensor, and voltage sensor were instrumental in capturing the electrical properties of the
solar cell. The variable resistor replicated various load conditions, while the PS constant and
temperature source simulated light intensity and temperature changes, respectively. The shunt
resistor accounted for shunt resistance effects, contributing to a more comprehensive analysis.
Through simulation with the completed model, the behavior of the solar cell under different
conditions was analyzed. Results were then validated against experimental or theoretical data,
providing valuable insights into the performance of the high-efficiency solar cell and elucidating
how external factors influence its operation.
By meticulously following this process, a comprehensive Simulink model was constructed,
accurately replicating the electrical properties of a high-efficiency solar cell. This model facilitated
detailed analysis under diverse conditions, enabling a deeper understanding of solar cell behavior.
3.3 Simulation Results Analysis:
In the simulation process within Simulink, various parameters such as irradiance, temperature,
series resistance, and shunt resistance are systematically varied to analyze their impact on the
electrical characteristics of the solar cell. To detail this process, we begin with parameter variation.
Irradiance, representing light intensity, is adjusted by manipulating the PS constant, allowing
simulation across different sunlight exposure levels. Temperature fluctuations are simulated by
adjusting the temperature source, facilitating analysis of temperature's effect on the solar cell's
performance. Series resistance, reflecting internal resistance, is altered by adjusting the value of
the series resistor in the circuit model to comprehend its influence on the solar cell's behavior.
Similarly, shunt resistance is varied by adjusting the shunt resistor value in the circuit model to
understand its effects on the solar cell's characteristics.
Furthermore, a range of variation is established for each parameter. For irradiance, the PS constant
is adjusted across a spectrum of light conditions, from low sunlight levels to peak intensity,
providing a comprehensive analysis. Temperature variations are simulated over a relevant range,
spanning from low temperatures during winter to high temperatures in summer, mirroring real-
world operating conditions. Similarly, series and shunt resistances are varied across typical ranges
encountered in solar cell designs and applications, ensuring a thorough exploration of their impacts.
Moving on to simulated I-V and P-V curves, these represent the relationship between output
current (I) and voltage (V) across the solar cell under different conditions. The P-V curves depict
the relationship between output power (P) and voltage (V), aiding in the assessment of power
output. From these curves, critical PV parameters are extracted. Maximum power (Pmax), opencircuit voltage (Voc), and short-circuit current (Isc) are obtained from the curves, representing key
performance metrics. Additionally, maximum voltage (Vmax), maximum current (Imax), and
output voltage (Vout) are derived, providing insights into the solar cell's behavior at specific
operating points. By leveraging MATLAB to analyze the simulated data, valuable insights are
gained into the solar cell's performance, crucial for optimizing designs and predicting real-world
behavior.
3.4 Theoretical Calculations
To verify the simulation results with theoretical calculations, we will utilize a series of formulas
mentioned below to compute crucial parameters. These comparisons encompass a range of factors,
including the reverse saturated current (Io), representing the diode's leakage current under reverse
bias, calculated through the diode equation, and the photovoltaic current (Iph), which is the current
generated by the solar cell due to incident light, determined using the equation describing the
relationship between incident light intensity and the DC current source. Additionally, the output
current, summing the photovoltaic current (Iph) and reverse saturated current (Io), and accounting
for losses attributed to series resistance (Rs) and shunt resistance (Rsh), will be assessed alongside
the fill factor (FF), a measure of solar cell efficiency computed as the ratio of maximum power
point (Pmax) to the product of open-circuit voltage (Voc) and short-circuit current (Isc). Input
power (Pin), representing the power delivered to the solar cell from incident light, will be
determined as the product of incident light intensity and the cell's surface area. Finally, efficiency,
gauging the solar cell's ability to convert incident light into electrical energy, will be calculated as
the ratio of output power (Pout) to input power (Pin), accounting for losses due to series resistance,
shunt resistance, and other factors. Through this comparative analysis, we aim to validate the
simulation model's accuracy and gain insights into the impact of irradiance, temperature, shunt
resistance, and series resistance variations on solar cell performance.
−1
𝑉
𝐼𝑠 = [𝑒 𝑛𝑉𝑜𝑐 − 1] ........................................................................................... (1)
𝑇
𝐼𝑝ℎ =
𝐼𝑟𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒
1000
[𝐼𝑆𝐶 + 𝐾(𝑇 − 𝑇𝑆𝑇𝐶 )] ...................................................................(2)
𝑉
𝐼 = 𝐼𝑝ℎ − 𝐼𝑠 (𝑒 𝑛𝑉𝑇 − 1) ......................................................................................(3)
𝑉+𝐼𝑅
𝑉+𝐼𝑅𝑠
𝐼 = 𝐼𝑝ℎ − 𝐼𝑠 [𝑒 ( 𝑛𝑉 𝑠 − 1)] − ( 𝑅
𝑇
𝐹𝑖𝑙𝑙 𝐹𝑎𝑐𝑡𝑜𝑟, 𝐹𝐹 =
𝑃𝑚𝑎𝑥
𝑉𝑂𝐶 𝐼𝑆𝐶
𝑠ℎ
) ................................................................(4)
................................................................................(5)
𝑃𝑖𝑛 = 𝐴𝑟𝑒𝑎 𝑜𝑓 𝑠𝑜𝑙𝑎𝑟 𝑐𝑒𝑙𝑙 × 𝐼𝑟𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒 𝐼𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦 ..........................................(6)
𝑛=
𝑉𝑂𝐶 𝐼𝑆𝐶 𝐹𝐹
𝑃𝑖𝑛
× 100% ......................................................................................(7)
To validate the accuracy of our simulation results, we will compare them with theoretical
calculations using various formulas. These formulas cover essential parameters such as the reverse
saturated current (Is) and photovoltaic current (Iph), representing the diode's leakage current and
solar cell's light-induced current, respectively. We will also assess the output current considering
losses from series and shunt resistances, along with the fill factor (FF) reflecting solar cell
efficiency. Input power (Pin) from incident light and efficiency, indicating the cell's ability to
convert light into electricity, will be calculated. This analysis aims to validate our simulation model
and understand how variations in irradiance, temperature, and resistance affect solar cell
performance.
4. RESULTS AND DISCUSSION
4.1 Designing of Solar Model
4.1.1 Comparison of Datasheet
The choice to compare Jinko Solar and Trina Solar was made since they are both well regarded
manufacturers of solar panels in the renewable energy sector. Jinko Solar is well-known for
producing solar modules with exceptional efficiency, while Trina Solar is acknowledged for its
cutting-edge technology and dependable performance. This study aims to evaluate key
performance parameters, such as open-circuit voltage (Voc), short-circuit current (Isc), maximum
power (Pmax), and temperature coefficient of short-circuit current (Kisc), by comparing the solar
panel specifications of two manufacturers: the Jinko Solar Cheetah HC 72M - JKM400M and the
Trina Solar TSM-400-DEG15M(II)
Table 1 Comparison of values needed for simulation between Jinko Solar Cheetah HC 72M JKM400M and Trina Solar TSM-400-DEG15M(II)
Parameter
Jinko Solar Cheetah HC 72M
Trina Solar TSM-400-
- JKM400M
DEG15M(II)
Maximum Power (Pmax)
400 W
400W
Maximum Voltage (Vmax)
40.16V
40.3
Maximum Current (Imax)
9.96 A
9.92
Weight
21.6 kg
22.0 kg
Cell Dimension
1979×1002×30mm
2015 × 996 × 35 mm
Number of Cells
72
144
Open-Circuit Voltage (Voc)
49.1 V
49.0 V
Open-Circuit Voltage (Voc) / cell
0.68 V
0.34 V
Short-Circuit Current (Isc)
10.61 A
10.45 A
Temperature Coefficient (Isc)
0.048 %/°C
0.04 %/°C
Module Efficiency
20.17&
19.9%
Ideality Factor
1.2
1.2
The Jinko Solar Cheetah HC 72M - JKM400M and the Trina Solar TSM-400-DEG15M(II) panels
differ in several important aspects that can affect their appropriateness for simulation and
modelling. Firstly, the Jinko Solar panel has a better module efficiency of 20.17% compared to
Trina Solar's 19.9%, even if both panels have the same maximum power output of 400 W. The
higher efficiency of the Jinko Solar panel signifies its ability to convert a greater proportion of
sunlight into electricity, resulting in a superior overall efficiency. In addition, the Jinko Solar panel
has somewhat lower values for both maximum voltage (Vmax) and current (Imax) when compared
to the Trina Solar panel. Nevertheless, this discrepancy is negligible and should not have a
substantial effect on performance.
In addition, the Jinko Solar panel weighs 21.6 kg, which is lighter than Trina Solar's panel that
weighs 22.0 kg. This can be beneficial in installations where the weight of the panel is a significant
factor to consider. In addition, the Jinko Solar panel has a reduced cell dimension and a lower cell
count (72) in comparison to the Trina Solar panel, which features a larger cell dimension and a
higher cell count (144). The reduced cell size and lower cell count of the Jinko Solar panel may
lead to a more condensed and potentially more economical panel configuration. The Jinko Solar
panel has a temperature coefficient of short-circuit current (Isc) of 0.048 %/°C, which is slightly
higher than Trina Solar's coefficient of 0.04 %/°C. The greater temperature coefficient of the Jinko
Solar panel suggests that it is more likely to perform well in high-temperature conditions, making
it advantageous in specific geographical areas.
After evaluating the crucial factors and criteria for simulation and modelling, it is evident that the
Jinko Solar Cheetah HC 72M - JKM400M panel is the more appropriate option. The increased
module efficiency, less weight, and potentially more cost-effective design of this option make it
an attractive choice for further analysis and modelling in this study.
4.1.2 Simulink Modelling
A Simulink model was created to replicate the electrical properties of a high-efficiency solar cell.
The model includes essential elements including a solar cell, current sensor, voltage sensor, and a
variable resistor connected to mimic load situations on the solar cell. By modifying this load, we
may analyze the complete I-V (Current-Voltage) and P-V (Power-Voltage) traits of the solar cell
under Standard Test Conditions (STC). Furthermore, other elements like the PS constant,
temperature source, and shunt resistor were included in the model in subsequent stages to analyze
the impact of irradiance, temperature, and shunt resistance on the circuit. The values selected for
examination under STC are displayed in Figure 4.3.
Figure 4.1 Simulink Solar Model
Figure 4.2 Simulink Solar Module with added Temperature sensor and Shunt Resistor
Figure 4.3 Solar Cell parameters under STC condition
4.1.3 Analyzing Characteristics of IV and PV curve at STC
The I-V curve derived from the simulation offers crucial insights into the performance possibilities
of the solar cell. The curve at the open-circuit voltage (Voc) is the highest voltage the cell can
generate when there is no current flowing. The short-circuit current (Isc) is the greatest current
produced when there is no external load, meaning zero voltage. The numbers align with the
datasheet, suggesting a well-parameterized model that accurately represents the inherent
semiconductor characteristics of the cell. The maximum voltage (Vmax) and maximum current
(Imax) values calculated from the curve differed from the values provided in the datasheet.
The P-V curve, generated from the simulation data, displays the maximum power point (Pmax) as
a result of the voltage and current values at this ideal operating point. The maximum power output
from the simulation slightly surpassed the figure specified in the datasheet. Discrepancies in data
can occur because of the idealized assumptions made during simulations, such perfect cell
conditions and 0% losses, which are seldom seen in real-world situations. Table 2 displays a
comparison between the results produced during simulation and the values provided in the data
Sheet
.
Figure 4.4 Curve with points indicating (Voc), (Isc), (Imax) and (Vmax)
Figure 4.5 PV Curve with points indicating (Voc), (Pmax)
Figure 4.6 MATLAB coding to generate IV and PV Curve under STC
Table 2 Comparison between Data Sheet values and Simulation values
Parameters
Open-Circuit Voltage (Voc) / cell
Short-Circuit Current (Isc)
Maximum Power (Pmax)
Maximum Voltage (Vmax)
Maximum Current (Imax)
Data Sheet Values
0.68V
10.61 A
400 W
40.16V
9.96 A
Simulation Values
0.68 V
10.61
427.68 W
41.76
10.12 A
4.1.4 Analyzing Efficiency Through Simulation
Upon analyzing the waveform captured by the scope during the simulation, the output voltage was
measured at 681.5 millivolts (mV). The output current (I) was calculated by determining the
reversed saturated current (Is) and photovoltaic current (Iph) values using Equations 1, 2, and 3,
which resulted in an output current value of -0.52 V. The efficiency of solar cells was determined
by analyzing values such as the maximum power from the P-V curve, fill factor (FF) and input
power (Pin) and implementing them into Equation 7. The simulated efficiency of 23.47%
exceeded the efficiency stated in the datasheet, which was 20.17%.
Various causes account for this disparity. The Simulink model functions under idealized settings,
which may not consider all losses, such as those from wiring, connectors, and the inefficiencies of
the real materials used in manufacture. Secondly, the figures in the datasheet are often obtained
under controlled settings that may not account for local environmental variables like temperature
changes and differences in sunshine spectrum, which might affect the performance of the cell.
Furthermore, manufacturing procedures may result in discrepancies in material characteristics
across individual cells, a factor not accounted for in a typical simulation model. The accuracy of
measurements obtained when creating the datasheet may vary from the theoretical calculations and
simulations conducted in the research.
Although the Simulink model closely approximates the solar cell's behavior under STC, it is crucial
to acknowledge the inherent limits of simulation. Discrepancies between simulation findings and
datasheet values highlight the importance of confirming simulation-based predictions with actual
data.
Table 3 Calculated Parameters under STC
Parameters Calculated
𝑅𝑒𝑣𝑒𝑟𝑠𝑒𝑑 𝑆𝑎𝑡𝑢𝑟𝑎𝑡𝑒𝑑 𝐶𝑢𝑟𝑟𝑒𝑛𝑡, 𝐼𝑆 (𝐴)
𝑃ℎ𝑜𝑡𝑜𝑣𝑜𝑙𝑡𝑎𝑖𝑐 𝑐𝑢𝑟𝑟𝑒𝑛𝑡, 𝐼𝑝ℎ (𝐴)
𝑂𝑢𝑡𝑝𝑢𝑡 𝑐𝑢𝑟𝑟𝑒𝑛𝑡, 𝐼 (𝐴)
𝐹𝑖𝑙𝑙 𝐹𝑎𝑐𝑡𝑜𝑟, 𝐹𝐹
𝑃𝑖𝑛 (𝑊)
𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 (%)
Values Obtained
2.817 × 10−9
10.61
−0.52
0.82
7.2148
23.47
4.2 Analyzing the Effects of Irradiance, Temperature, RS and RSh
4.2.1 Effects of Irradiance
4.2.1.1 Analyzing IV and PV curve with Varying Irradiance
As irradiance declines from 1000 W/m² to 200 W/m², the open-circuit voltage (Voc) slightly
lowers. Voc is less affected by variations in irradiance due to its greater susceptibility to the
temperature and material characteristics of the solar cell. The short-circuit current (Isc) decreases
dramatically from 10.61 A to 2.12 A. The tendency is anticipated due to the direct proportionality
between Isc and the incident light on the solar panel. Reduced irradiance decreases the number of
photons available to create charge carriers, resulting in a decrease in the generation of electronhole pairs and subsequently lowering the photocurrent, which then reduces the short circuit current
[5]. The output power of a solar cell is directly proportional to the irradiation due to the
considerable decrease in Isc. This is seen in the P-V curve as a downward movement in the peak
power point (Pmax).
Figure 4.1 IV curve under Varying Irradiance
Figure 4.2 PV curve under Varying Irradiance
4.2.1.2 Analyzing Output Current and Efficiency of Varying Irradiance
The efficiency of a solar cell under varying irradiance levels shows a noticeable decrease from
23.7% at 1000 W/m² to 21.3% at 200 W/m². This trend is attributed to the nonlinear characteristics
of solar cells, where the efficiency is not strictly proportional to the irradiance. At lower irradiance,
while both the photocurrent (Isc) and the output power decrease, the reduction in photocurrent is
more significant due to the diminished photon flux available to generate electron-hole pairs[6].
Consequently, the solar cell's ability to convert the available light into electrical energy becomes
less efficient, as evidenced by the reduction in efficiency. The output current, primarily determined
by the photocurrent (Isc), sees a significant decrease with the reduction in irradiance. A lower
irradiance means fewer photons are available, leading to a proportional decrease in the generated
photocurrent and, hence, the output current.
Table 4 Calculated Is, Iph And I under Varying Irradiance
𝐼𝑟𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒 W/m2
1000
800
600
400
200
𝑅𝑒𝑣𝑒𝑟𝑠𝑒𝑑 𝑆𝑎𝑡𝑢𝑟𝑎𝑡𝑒𝑑 𝐶𝑢𝑟𝑟𝑒𝑛𝑡,
𝐼𝑆 (𝐴)
2.817 × 10−9
2.732 × 10−9
2.571 × 10−9
2.455 × 10−9
2.422 × 10−9
𝑃ℎ𝑜𝑡𝑜𝑣𝑜𝑙𝑡𝑎𝑖𝑐 𝑐𝑢𝑟𝑟𝑒𝑛𝑡,
𝐼𝑝ℎ (𝐴)
10.61
6.78
3.81
1.69
0.42
𝑂𝑢𝑡𝑝𝑢𝑡 𝑐𝑢𝑟𝑟𝑒𝑛𝑡,
𝐼 (𝐴)
−0.52
−1.93
−2.31
−2.68
−2.80
Table 5 Calculated Efficiency under Varying Irradiance
𝐼𝑟𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒 W/m2
1000
800
600
400
200
𝐹𝑖𝑙𝑙 𝐹𝑎𝑐𝑡𝑜𝑟, 𝐹𝐹
0.823
0.820
0.817
0.812
0.800
𝑃𝑖𝑛 (𝑊)
25.20
20.16
15.12
10.08
5.04
𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 (%)
23.7
23.2
22.9
22.4
21.3
4.2.2 Effects of Temperature
4.2.2.1 Analyzing IV and PV curve with Varying Temperature
Solar cells' IV curve and PV curve alter with temperature changes because of the semiconductor
material characteristics. As the temperature rises from -25°C to 75°C, the open-circuit voltage
(Voc) falls from 0.761 V to 0.591 V. The fall in Voc is caused by the narrower bandgap of the
semiconductor, which lowers the energy needed for electron transition between the valence and
conduction bands, resulting in a reduced Voc[7].
The Isc remains constant despite temperature fluctuations, demonstrating its resistance to thermal
changes. The maximum power output decreases with rising temperature mostly because to the
decrease in open-circuit voltage (Voc). As the temperature changes from -25°C to 75°C, the drop
in Voc results in a decrease in Pmax from 6.48 W to 4.66 W, as Pmax is proportional to the product
of Isc and Voc (Pmax ∝ Isc × Voc). This highlights the significance of temperature control in
maximising solar cell performance and total efficiency.
Figure 4.9 IV curve under Varying Temperature
Figure 4.10 PV curve under Varying Temperature
4.2.2.2 Analyzing Efficiency and Output Current with Varying Temperature
The current produced by a solar cell, mainly influenced by the photocurrent (Isc), rises as the
temperature increases. Certain materials have enhanced light absorption at elevated temperatures,
resulting in a greater quantity of photons being absorbed and transformed into electron-hole pairs,
thereby boosting the output current. The semiconductor material's bandgap energy drops as
temperature rises. Photons with lower energy (longer wavelengths) may now create electron-hole
pairs, leading to more charge carriers and higher output current. Increased temperatures cause more
electrons and holes in the semiconductor material to achieve sufficient thermal energy to escape
their bound state, resulting in the generation of more electron-hole pairs. Some pairings do not
contribute to generating electrical current; instead, they recombine, causing thermalization losses
and decreased efficiency. The efficiency of a solar cell decreases significantly from 25.59% at 25°C to 18.38% at 75°C. Higher temperatures can cause an increase in thermal energy, resulting
in more recombination events, which in turn decreases the efficiency of the solar cell. Increased
carrier recombination at higher temperatures is one of the factors contributing to the reduced
efficiency of solar cells due to thermal deterioration [8]. This process shortens the lifespan of
charge carriers, leading to a reduction in the potential electrical output produced by a certain
amount of incoming light. This effect may be seen in solar panels put in hot climes or exposed to
direct sunshine for long durations in real-life situations.
Table 6 Calculated Is, Iph And I under Varying Temperature
𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 °C
𝑅𝑒𝑣𝑒𝑟𝑠𝑒𝑑 𝑆𝑎𝑡𝑢𝑟𝑎𝑡𝑒𝑑 𝐶𝑢𝑟𝑟𝑒𝑛𝑡, 𝑃ℎ𝑜𝑡𝑜𝑣𝑜𝑙𝑡𝑎𝑖𝑐 𝑐𝑢𝑟𝑟𝑒𝑛𝑡, 𝑂𝑢𝑡𝑝𝑢𝑡 𝑐𝑢𝑟𝑟𝑒𝑛𝑡,
𝐼𝑆 (𝐴)
𝐼𝑝ℎ (𝐴)
𝐼 (𝐴)
75
3.771 × 10−8
10.61
2.55
50
9.971 × 10−9
10.61
1.93
25
2.817 × 10−9
10.61
−0.52
0
1.062 × 10−9
10.61
−3.92
−25
2.911 × 10−10
10.61
−4.89
Table 7 Calculated Efficiency under Varying Temperature
𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 °C
75
50
25
0
−25
𝐹𝑖𝑙𝑙 𝐹𝑎𝑐𝑡𝑜𝑟, 𝐹𝐹
0.74
0.76
0.80
0.81
0.81
𝑃𝑖𝑛 (𝑊)
25.20
25.20
25.20
25.20
25.20
𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 (%)
18.38
20.15
23.7
23.91
25.59
4.2.3 Effects of Series Resistance
4.2.3.1 Analyzing IV and PV curve with Varying Series Resistance
The presence of series resistance (Rs) in a solar cell significantly affects its performance
parameters. The short-circuit current (Isc) is not significantly influenced by the series resistance
(Rs) as it is determined when the external circuit resistance is zero (short-circuit situation). Thus,
the internal series resistance does not have a direct impact on the measurement of Isc. The opencircuit voltage, Voc, is not directly influenced by series resistance.
Nevertheless, Rs does affect the maximum power point (Pmax) of the solar cell. Increased series
resistance results in higher resistive losses within the cell, particularly during maximum power
point operation when the cell is generating substantial current [9]. This diminishes the voltage and
power that can be harnessed from the cell under a specific degree of light, resulting in a reduction
in Pmax. As resistance increases from 5mΩ to 45mΩ, the maximum power (Pmax) drops from
5.43 W to 2.34 W in this case.
Figure 4.11 IV curve under Varying Series Resistance
Figure 4.3 PV curve under Varying Series Resistance
4.2.3.2 Analyzing Efficiency with Varying Series Resistance
Efficiency is significantly affected by Rs. High series resistance in a solar cell leads to a substantial
amount of the produced current being dissipated as heat within the cell. This decreases the useable
electrical power generated from absorbed sunlight, reducing the overall efficiency of the cell.
Higher series resistance can restrict current flow, resulting in decreased power output.
Minimising series resistance in a solar cell is crucial for maximising efficiency and converting
more absorbed sunlight into useful electrical power. Utilising low-resistance materials and
optimising cell design are effective strategies to decrease series resistance and enhance the
efficiency of solar cells [10]. Thus, as Rs increases, both the fill factor and Pmax decrease, leading
to a noticeable drop in efficiency. For instance, with Rs increasing from 5mΩ to 45mΩ, the
efficiency decreases from 21.50% to 9.13%.
Table 8 Calculated Efficiency under Varying Series Resistance
𝑆𝑒𝑟𝑖𝑒𝑠 𝑅𝑒𝑠𝑖𝑠𝑡𝑎𝑛𝑐𝑒, mΩ
45
35
25
15
5
𝐹𝑖𝑙𝑙 𝐹𝑎𝑐𝑡𝑜𝑟, 𝐹𝐹
0.32
0.39
0.49
0.61
0.75
𝑃𝑖𝑛 (𝑊)
25.20
25.20
25.20
25.20
25.20
𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 (%)
9.13
11.10
14.03
17.48
21.50
4.2.4 Effects of Shunt Resistance
4.2.4.1 Analyzing IV and PV Curve with Varying Shunt Resistance
The short-circuit current (Isc) in a solar cell remains stable despite changes in shunt resistance,
which typically ranges from 100mΩ to 300mΩ. Isc is mainly influenced by the amount of photons
absorbed by the solar cell and its efficiency in converting these photons into electron-hole pairs.
Shunt resistance influences internal losses in a solar cell, such as leakage currents and
recombination losses, but has minimal effect on the absorption of incident light or the generation
of electron-hole pairs [11]. However, a decrease in shunt resistance leads to a reduction in the
open-circuit voltage (Voc) of a solar cell. A decreased shunt resistance enables more current flow
through the shunt channels, hence decreasing the voltage differential across the cell's terminals.
The Voc reduces from 0.672 to 0.650 as the voltage needed to halt the current flow through the
cell lowers. The effect of low shunt resistance on Pmax is significant. Leakage currents grow as
voltage increases, causing more of the produced current to be lost to alternative channels, which
decreases the efficiency of power conversion as the voltage approaches the open-circuit voltage
(Voc). The fall in efficiency results in a reduction in Pmax from 4.79 to 2.78, since the cell is
unable to maintain a high current under load because of shunt losses.
Figure 4.10 IV curve under Varying Shunt Resistance
Figure 4.11 PV curve under Varying Shunt Resistance
4.2.4.2 Analyzing Efficiency and Output Current with Varying Shunt Resistance
Shunt resistance (Rsh) significantly affects the output current and efficiency of photovoltaic (PV)
cells, particularly in relation to the internal current flow dynamics within the cell. A reduction in
shunt resistance (Rsh) results in more current bypassing the external load through internal leakage
channels, especially evident when the cell nears open-circuit conditions. Transitioning from -6.56
A to -2.37 A signifies a decrease in leakage current, or in other words, an enhancement in the
available current for external work. The higher absolute value of -2.37 A compared to -6.56 A
implies that less current is being internally lost .
The efficiency of the PV cell is significantly affected by variations in Rsh. Reducing the shunt
resistance (Rsh) may lead to a loss in efficiency because of higher leakage currents. The values
indicate a decrease in efficiency from 19% to 10.94%, revealing how PV cell performance
deteriorates over time or in certain conditions, leading to increased leakage and decreased
conversion of solar energy into electrical power.
Table 9 Calculated Is, Iph And I under Varying Shunt Resistance
𝑆ℎ𝑢𝑛𝑡 𝑅𝑒𝑠𝑖𝑠𝑡𝑎𝑛𝑐𝑒, Ω
300
250
200
150
100
𝑅𝑒𝑣𝑒𝑟𝑠𝑒𝑑 𝑆𝑎𝑡𝑢𝑟𝑎𝑡𝑒𝑑 𝐶𝑢𝑟𝑟𝑒𝑛𝑡,
𝐼𝑆 (𝐴)
3.651 × 10−9
3.890 × 10−9
4.292 × 10−9
4.881 × 10−9
7.450 × 10−9
𝑃ℎ𝑜𝑡𝑜𝑣𝑜𝑙𝑡𝑎𝑖𝑐 𝑐𝑢𝑟𝑟𝑒𝑛𝑡,
𝐼𝑝ℎ (𝐴)
10.61
10.61
10.61
10.61
10.61
𝑂𝑢𝑡𝑝𝑢𝑡 𝑐𝑢𝑟𝑟𝑒𝑛𝑡,
𝐼 (𝐴)
−2.37
−2.94
−3.71
−4.43
−6.56
Table 10 Calculated Efficiency under Varying Shunt Resistance
𝑆ℎ𝑢𝑛𝑡 𝑅𝑒𝑠𝑖𝑠𝑡𝑎𝑛𝑐𝑒, Ω
300
250
200
150
100
𝐹𝑖𝑙𝑙 𝐹𝑎𝑐𝑡𝑜𝑟, 𝐹𝐹
0.67
0.64
0.60
0.52
0.40
𝑃𝑖𝑛 (𝑊)
25.20
25.20
25.20
25.20
25.20
𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 (%)
19.00
18.05
16.84
14.51
10.94
4.3 Ongoing Research and Future Directions
Ongoing research is focused on optimising solar energy conversion by studying the interaction
between various parameters and photovoltaic cell performance. Advanced material science
research aims to create novel semiconductor materials that can withstand temperature changes
better and have enhanced light absorption properties. Methods like surface passivation and antireflective coatings are being studied to improve cell efficiency and decrease recombination losses.
Novel technologies like perovskite solar cells and organic photovoltaics show potential for
increased efficiency and reduced production expenses, possibly addressing constraints of
conventional silicon-based cells. Advancements in cell design, such tandem and multi-junction
cells, strive to exceed the Shockley-Queisser limit of single-junction cells, opening up new
possibilities for enhancing solar energy conversion efficiency [12]. System-level enhancements
like maximum power point tracking (MPPT) technologies and thermal management systems are
being created to enhance the efficiency of solar panels in real-world situations, guaranteeing that
PV systems can produce maximum output under different environmental conditions.
The relationship between irradiance, temperature, Rs, and Rsh on PV cell performance highlights
the intricate nature of solar energy conversion. Further research and innovation in materials, cell
design, and system optimisation are crucial for improving the efficiency, longevity, and costefficiency of solar energy technologies, leading to a more sustainable and renewable energy future.
5. CONCLUSION
We conducted a thorough experiment to investigate the intricate dynamics influencing the
performance and efficiency of photovoltaic (PV) systems. We utilised advanced design approaches,
simulations, and theoretical studies to examine how irradiance, temperature, series resistance (Rs),
and shunt resistance (Rsh) impact the performance of solar cells. This project provided valuable
insights, emphasising the crucial role of ideal sunlight exposure. We found a clear connection
between irradiance levels and the electrical performance of solar cells. Decreases in irradiance
resulted in significant reductions in short-circuit current (Isc) and maximum power output (Pmax).
The study highlighted how temperature rises negatively impact open-circuit voltage (Voc) and
Pmax by reducing the bandgap of the semiconductor material, emphasising the need of efficient
thermal management. The investigation of series and shunt resistances revealed their significant
impact on solar cell efficiency. Increased series resistance results in higher resistive losses, while
lower shunt resistance creates internal leakage paths, both negatively affecting the efficiency and
performance of photovoltaic systems. The study also explored the latest developments in solar cell
technologies, including tandem solar cells, perovskite materials, and bifacial cells, indicating a
bright future for solar energy conversion by potentially surpassing current efficiency constraints
and cost obstacles. This work enhances our knowledge of the elements affecting PV system
performance and highlights the essential requirement for ongoing innovation and research to fully
use solar energy, leading to a sustainable and renewable energy future.
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APPENDIX
Jinko Solar Cheetah HC 72M - JKM400M – Datasheet
Trina Solar TSM-400-DEG15M(II) Datasheet