Keywords
Stand-alone PV, Photovoltaic system, Solar cell, MATLAB simulation
This article is included in the Energy gateway.
This article is included in the Solar Fuels and Storage Technologies collection.
The escalating global population, surpassing seven billion in 2012, amplifies the strain on existing resources for food, housing, and conventional energy. Addressing these challenges requires the development of economically and environmentally viable renewable energy technologies. Photovoltaic (PV) solar modules stand out for their eco-friendly operation and reliability. In off-grid communities, stand-alone PV systems, coupled with battery storage, play a pivotal role in meeting electrical energy needs.
This study enhances the understanding of stand-alone PV systems through modeling and simulation using MATLAB software. A multi-crystalline PV system, specifically the Kyocera KC130GT, is investigated under varying conditions, and a pulse width modulation (PWM) controller is employed for battery charging.
The study reveals profound effects on energy production based on the I-V and P-V characteristics of the modeled system when a PWM controller is utilized. The system demonstrates successful energy generation under different conditions, accounting for temperature variations and PV battery voltage mismatches.
The simulated model serves as a versatile system capable of detecting different conditions in varying light and temperature scenarios. Effective temperature monitoring, voltage adjustment using a suitable charger controller, and the selection of optimal materials for solar modules can significantly enhance the system’s efficiency. The results emphasize the importance of careful consideration of PV system sizing corresponding to battery capacity for improved solar system efficiency. While the cost of the modeled stand-alone PV system is currently low, scalability to larger projects may incur increased costs due to the high prices of photovoltaic panels, batteries, and other components.
Stand-alone PV, Photovoltaic system, Solar cell, MATLAB simulation
In this revised version of the article, several significant improvements and additions have been made to enhance the clarity, completeness, and overall quality of the research. The title has been modified to better reflect the content and methodology employed in the study, providing readers with a more informative and concise overview. The abstract has been expanded to include additional applications of the proposed method and to highlight key results, addressing the need for more specificity and interest.
The introduction section has been restructured into four paragraphs, providing a more comprehensive background on the energy situation and emphasizing the importance of renewable energy sources, particularly solar energy.
The methods section has been refined for clarity and flow, with the inclusion of sub-sections (Materials and Modeling/Simulation) to provide a more organized presentation.
A new section on cost analysis has been incorporated into the conclusion, including a detailed table listing the costs of individual components to provide transparency in the cost analysis process. The discussion section now includes a table discussing load demand characteristics, offering a more comprehensive insight into the system's behavior. Detailed descriptions of the output PV modules have been added to the results section, contributing to a more thorough understanding of the findings. The reliability of the stand-alone PV system is explicitly discussed, addressing the criteria and metrics employed for assessment.
Overall, these revisions aim to address the valuable feedback from reviewers and further strengthen the scholarly contributions of the research.
See the authors' detailed response to the review by Hussein Mohammed Ridha
See the authors' detailed response to the review by Erkata Yandri
The surge in global population, exceeding seven billion in 2012, has intensified the strain on essential resources such as food, shelter, and conventional energy sources. This escalating demand underscores the need for sustainable alternatives, especially in the realm of energy production. The transition to renewable energy sources is crucial, given the limitations and environmental implications associated with conventional energy. Solar energy, with its abundance and environmental friendliness, stands out as a key player in the quest for sustainable solutions. Harnessing solar energy through photovoltaic (PV) systems, particularly in off-grid settings, presents a promising avenue to address the growing energy needs while minimizing environmental impact.1–5 This transition introduces the pivotal role of solar energy and sets the stage for a detailed exploration of solar photovoltaic systems. Connecting the dots from the broader energy context, we delve into the intricacies of harnessing solar power through PV technology. The developmental phenomena and pertinent issues surrounding stand-alone PV systems become apparent, paving the way for an in-depth examination of our study’s focal points. As we embark on this journey, it becomes evident that understanding and optimizing stand-alone PV systems hold great potential for providing sustainable and reliable energy solutions to remote, off-grid communities.6–9 By prioritizing recent publications within the last five years, we aim to capture the most up-to-date insights into the advancements, challenges, and best practices in the field. This literature review serves as a foundation for our study, enabling us to build upon existing knowledge, identify gaps, and contribute novel insights.10–13 Summarizing the results of our literature review, we highlight gaps and novelties that lay the groundwork for our study. By elucidating the current state of knowledge in stand-alone PV systems, we identify areas where further research is warranted. This leads us to articulate the purpose of our study, emphasizing the need to model, simulate, and evaluate PV islanding systems to enhance their operational efficiency in diverse projects. As a pivotal step towards sustainable energy solutions, our research aims to bridge existing gaps and contribute valuable insights to the field. Furthermore, as suggested, Figure 1, “Benefits of DC, AC, and hybrid DC-AC coupled configurations of stand-alone PV systems,” has been relocated to the Methods section to ensure a streamlined presentation of our study.14–16 The primary objective of this study is to model, simulate, and evaluate stand-alone photovoltaic (PV) systems, specifically focusing on PV islanding scenarios. By employing advanced simulation techniques and modeling tools, our aim is to enhance the understanding of system dynamics and operational efficiency under varying conditions. Through a comprehensive literature review and detailed analysis of recent research, we seek to identify key challenges, advancements, and gaps in stand-alone PV systems. Additionally, our aim includes providing valuable insights into optimizing the performance of these systems, particularly in remote, off-grid communities. Ultimately, the research aims to contribute to the development of sustainable and reliable energy solutions by addressing critical aspects of stand-alone PV system design, operation, and efficiency.
A typical stand-alone PV system, illustrated in Figure 1, comprises essential components such as a PV generator, storage battery, DC/DC converter, charge controller, inverter, alternating current (AC) and/or direct current (DC) loads, and an attenuation load. Notably, there is no connection to the power grid, emphasizing the system’s self-sufficiency. The PV generator consists of an array with multiple modules, each housing numerous solar cells. Excess energy generated during peak sunlight is stored in the battery, subsequently released when the PV generator output is insufficient. The load demand of the PV islanding system can manifest in various forms, including DC and/or AC. The power conditioning unit, encompassing the DC/DC converter, charge controller, and inverter, serves as a vital interface ensuring control and protection. Additionally, a reducing load is incorporated to absorb surplus energy when the PV generator output exceeds the load requirement.17–24
To enhance understanding of system operation, a stand-alone PV solar cell was chosen for comprehensive modeling and simulation. The MATLAB software, specifically the Simulink portal (Version: R2017a, RRID: SCR_001622), facilitated this process. The evaluation criteria for building an optimized stand-alone PV system were established, considering a solar irradiance of 1000 W/m2. The chosen multi-crystalline PV system, Kyocera KC130GT, featured 36 individual cells connected with two bypass diodes to prevent breakdown and reverse voltage. Terminal voltages, P-V, and I-V characteristics were measured under varying conditions, as depicted in Figure 2 and Figure 3.
The simulation incorporated realistic environmental factors by setting temperatures to 25 or 40 °C. The battery, a crucial component of the PV islanding system model, was connected to a parallel resistor of 8.64 Ω and a 12 V voltage source. A pulse width modulation (PWM) charge controller, simulated at 5 kHz with an 8.02 A saturation current, controlled the battery. A 21.7 V PV voltage at a duty cycle of 0.5 for 1 second was utilized. Solar irradiance data for Baghdad on a sunny day (February 2, 2022) and a rainy day (February 5, 2022) further enriched the simulation, accounting for weather effects on system performance.25
In this study, the electrical energy generated by the PV panels was assessed, considering losses across the entire system. The simulations conducted under different weather conditions on February 2, 2022, and February 5, 2022, aimed to determine the optimal parameters for a home stand-alone PV solar system. The analysis facilitates the calculation of the expected power supply throughout the year under diverse environmental circumstances. The overall I-V and P-V characteristics of the modelled PV solar array are depicted in Figure 4. Notably, the highest power values, approximately 119 and 125 Watts, were observed at temperatures of 25 °C and 40 °C, respectively (Figure 5). The I-V and P-V plots unveil the influence of cell temperature, primarily dictated by the material composition, including multi-crystalline materials. The voltage and current characteristics of the PV panels significantly impact power production, especially when a Pulse Width Modulation (PWM) charge controller is employed. It’s noteworthy that while stand-alone PV systems typically operate within the range of 11-14 V, the integration of a PWM charge controller and a 12 V operating battery shifts hybrid PV systems to operate consistently at 12 V.
Simulating the main components of the stand-alone PV system using solar irradiance data (Figure 6), the state of charge of the battery exhibited distinct patterns during sunny and rainy days (Figure 7). Equipped with an 8.02 A saturation current and 21.7 volts, the photovoltaic model demonstrated dynamic behavior. During sunny hours, the load consumed part of the power, and excess energy was stored in the battery through charging. Conversely, during periods of low solar radiation, the load drew power from the battery. Notably, a load was disconnected as soon as the charge rate fell below 30%. On February 2, 2022 (sunny day), the battery’s state of charge decreased from 75% to 55%, indicating power supply to the load. The PV panels effectively met the load power requirements. On February 5, 2022 (rainy day), a similar pattern in battery state of charge was observed, but the lower irradiation resulted in the PV panels supplying less power. Consequently, the simulated system exhibited superior performance during sunny conditions, with the battery reaching a maximum state of charge of 95%, compared to 58% on the rainy day’s conclusion.
The supplied solar irradiance data, as illustrated in Figure 6, provides a temporal understanding of the variations during sunny and rainy days. Figure 7, depicting the state of charge, voltage, and current simulation states of the battery, elucidates the system’s behavior, showcasing differences between sunny and rainy days with straight and dashed lines, respectively.
The simulation results presented here offer valuable insights into the dynamic performance of the stand-alone PV system under varying environmental conditions. To provide a more comprehensive understanding, additional simulation results can be incorporated in future studies, further enhancing the robustness of the findings. In the ensuing discussion, we delve into key implications of the results, touching upon factors such as system efficiency, energy storage, and the overall feasibility of the stand-alone PV system for remote, off-grid communities.
The electrical performance of the stand-alone PV system is closely tied to the characteristics of the output PV modules. In this section, we provide detailed descriptions of the key attributes and behaviors observed in the simulated output of the PV modules.
1. PV Module Specifications: The simulation utilized a multi-crystalline PV system, specifically the Kyocera KC130GT model. This PV module consists of 36 individual cells connected with two bypass diodes. The specifications of the Kyocera KC130GT, including its power rating, voltage, and current characteristics, significantly influence the overall energy output of the system.
2. I-V (Current-Voltage) Characteristics: The I-V characteristics of the output PV modules were measured and analyzed under different operating conditions. These characteristics provide insights into the relationship between current and voltage, showcasing how the modules respond to varying levels of solar irradiance and temperature. Figure 3 illustrates the I-V curves for the Kyocera KC130GT model.
3. P-V (Power-Voltage) Characteristics: The power-voltage (P-V) characteristics of the output PV modules were also examined. These characteristics reveal the power output at different voltage levels, aiding in the determination of the maximum power point (MPP). Figure 4 depicts the P-V curves for the Kyocera KC130GT model.
4. Temperature Effects: Temperature plays a crucial role in the performance of PV modules. The simulation considered two temperature scenarios (25 °C and 40 °C) to assess the impact on the output characteristics. Variations in temperature influence the efficiency and power output of the PV modules.
5. Voltage and Current Dependency: The voltage and current characteristics of the PV modules were investigated, particularly focusing on their impact on power production when integrated with a PWM charge controller. Understanding the voltage and current dependency is vital for optimizing the charging process and overall system efficiency.
The detailed examination of the output PV modules provides valuable insights into the performance of the stand-alone PV system. These characteristics influence the overall energy generation, efficiency, and response to environmental factors, contributing to informed decision-making in system design and operation.
Reliability is a critical aspect when evaluating the performance of a stand-alone PV system. In our study, we employed several criteria and metrics to comprehensively assess the reliability of the simulated system. The following outlines the key factors considered in evaluating the reliability:
1. Performance Metrics:
• Energy Output Stability: The stability of energy output over varying environmental conditions, such as solar irradiance and temperature, was a primary metric. A reliable system should consistently generate the expected energy, ensuring a stable power supply.
• Voltage and Current Consistency: The consistency of voltage and current outputs from the PV modules, especially when subjected to dynamic load demands, contributes to the system’s reliability. Deviations from expected values may indicate potential issues.
• Battery State of Charge (SOC): Monitoring the state of charge of the battery is crucial for reliability. The system’s ability to maintain an optimal SOC ensures a continuous power supply, especially during periods of low solar radiation.
2. Fault Tolerance and Durability:
• Fault Handling: The system’s response to faults, such as fluctuations in solar irradiance or sudden load changes, was assessed. A reliable system should demonstrate effective fault handling, minimizing disruptions to energy generation.
• Component Durability: The durability of individual components, including PV modules, batteries, and controllers, was considered. Reliable components contribute to the overall robustness of the system against wear and tear.
3. Charging and Discharging Efficiency:
• Charge Controller Efficiency: The efficiency of the charge controller, particularly when employing pulse width modulation (PWM), played a crucial role in optimizing the charging and discharging processes. Efficient energy management enhances overall system reliability.
• Battery Charging/Discharging Efficiency: The effectiveness of the battery in storing and releasing energy was assessed. Reliability is enhanced when the battery operates efficiently, minimizing energy losses during charge and discharge cycles.
4. Environmental Adaptability:
• Temperature Resilience: The system’s ability to operate effectively under varying temperatures was a key criterion. A reliable system should demonstrate resilience to temperature fluctuations, ensuring consistent performance.
• Adaptability to Solar Irradiance Changes: As solar irradiance patterns change throughout the day, the system’s adaptability to these variations contributes to its reliability. Consistent energy generation under different irradiance conditions is essential.
By employing these criteria and metrics, we aimed to holistically evaluate the reliability of the stand-alone PV system in our simulation. The findings contribute to a deeper understanding of the system’s performance and provide insights for potential improvements in real-world implementations.
Understanding the load demand characteristics is crucial for designing an efficient stand-alone PV system that can meet the energy requirements of the intended application. The load demand profile represents the pattern of electrical consumption over time, influencing the sizing of components and overall system performance. In this section, we delve into the load demand characteristics of the simulated stand-alone PV system:
1. Load Profile Analysis: The load demand profile was analyzed based on historical data and usage patterns. Distinct load variations throughout the day, week, or season were considered to capture the dynamic nature of energy consumption.
2. Peak Load Identification: Identifying peak load periods is essential for sizing the PV generator and storage components. Peak load times influence the determination of required battery capacity and the ability of the system to handle sudden surges in energy demand.
3. Load Types and Variability: Categorizing loads into DC and AC, and assessing their variability, aids in selecting suitable components and optimizing the overall system architecture. Understanding load variability ensures that the system can adapt to fluctuations in energy consumption.
4. Load Priority and Critical Applications: Assigning priority levels to different loads enables efficient energy management. Critical applications with higher priority receive a continuous power supply, ensuring uninterrupted operation even during adverse conditions.
5. Load Shedding Strategies: Implementing load shedding strategies during periods of excess energy generation allows for the optimization of battery storage. Non-critical loads can be temporarily disconnected to prevent overcharging and enhance overall system efficiency.
6. Future Load Growth Considerations: Anticipating future load growth is essential for designing a scalable system. By understanding potential changes in energy demand, the system can be configured to accommodate increased loads without compromising performance.
7. Integration with Photovoltaic Generation: The correlation between load demand and solar irradiance patterns was considered to align energy generation with consumption. Synchronizing PV generation with peak load times enhances the system’s ability to meet demand during critical periods.
8. Implications for System Design: The load demand characteristics directly influence the sizing of PV modules, battery capacity, and the overall power conditioning unit. The results of the load demand analysis guide the selection of components to ensure optimal system performance under varying conditions.
In conclusion, a comprehensive understanding of load demand characteristics is fundamental for designing a resilient and efficient stand-alone PV system. The insights gained from this analysis contribute to informed decision-making, supporting the development of a system that meets the energy needs of the intended application.
In summary, this study employed modeling and simulation techniques to assess the performance of a stand-alone photovoltaic (PV) system, particularly focusing on its viability for remote, off-grid communities. Through comprehensive simulations under different weather conditions, the study aimed to optimize the system parameters for enhanced efficiency and year-round energy supply. The key findings and conclusions drawn from the research are outlined below. The analysis of the modelled PV solar array revealed significant dependence on temperature, material composition, and the impact of using a Pulse Width Modulation (PWM) charge controller. The system exhibited robust performance, generating the highest power outputs at temperatures of 25 °C and 40 °C. The integration of a PWM charge controller and a 12 V operating battery showcased the adaptability of hybrid PV systems, consistently operating at 12 V. Simulation results illustrated the dynamic behavior of the stand-alone PV system, with the state of charge of the battery responding to variations in solar irradiance. Notably, the system’s superior performance during sunny days, reaching a maximum state of charge of 95%, emphasized its potential for reliable energy supply. The observed differences in performance under varying environmental conditions underscore the importance of meticulous system design and parameter optimization.
Despite the promising outcomes, certain considerations must be acknowledged. The study provides valuable insights into the performance of the stand-alone PV system, yet further refinements and real-world validations are warranted. Additionally, economic factors and scalability should be explored to ascertain the system’s practicality for larger projects and remote communities. In conclusion, the simulated model offers a valuable tool for understanding the nuanced conditions affecting stand-alone PV systems. The findings emphasize the significance of temperature control, material selection, and charge controller optimization in enhancing system efficiency. As technologies evolve, the insights gained from this study contribute to the ongoing efforts to harness renewable energy for sustainable development, especially in remote, off-grid areas.
The comprehensive cost analysis presented in Table 1 provides a detailed breakdown of the financial aspects associated with the stand-alone PV system components. Understanding the economic considerations of renewable energy projects is vital for decision-making and long-term sustainability. Here, we discuss key observations and implications derived from the cost analysis:
1. Photovoltaic Modules: The cost of photovoltaic modules constitutes a significant portion of the overall system expenses. Exploring avenues for cost reduction, such as advancements in manufacturing technologies and bulk procurement strategies, could enhance the economic viability of the system.
2. Storage Battery: The storage battery, essential for storing excess energy and ensuring a continuous power supply, contributes notably to the system cost. Future developments in energy storage technologies and economies of scale may lead to more cost-effective solutions.
3. DC/DC Converter, Charge Controller, and Inverter: These power conditioning components play crucial roles in regulating and converting electrical energy. Analyzing these costs offers insights into optimizing system efficiency while balancing financial considerations.
4. DC/AC Loads and Attenuation Load: The costs associated with loads and attenuation reflect the specific requirements and applications of the PV system. Identifying load patterns and evaluating load-related costs contribute to fine-tuning the system design for optimal performance.
5. Wiring, Connectors, Mounting Structure, and Monitoring System: While often considered secondary, these components are integral for system functionality and performance monitoring. Strategically assessing costs related to infrastructure and monitoring contributes to overall system reliability.
6. Miscellaneous (Contingency): Including a contingency for unforeseen circumstances is a prudent approach. This miscellaneous category ensures financial preparedness for unexpected challenges during system deployment and operation.
7. Overall System Cost Implications: The cumulative total system cost represents the financial investment required for the stand-alone PV system. The presented breakdown facilitates a transparent understanding of where financial resources are allocated, aiding decision-makers in budgetary planning and resource optimization.
8. Future Cost Considerations: Continuous advancements in renewable energy technologies, market trends, and policy incentives can influence the cost landscape. Regular reassessment of costs and periodic updates to the financial model will be imperative for staying abreast of evolving economic dynamics.
In conclusion, a thorough cost analysis serves as a foundational step in realizing the economic feasibility of stand-alone PV systems. The insights gained from this analysis contribute to informed decision-making, supporting the development and implementation of sustainable and cost-effective renewable energy solutions.
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Is the work clearly and accurately presented and does it cite the current literature?
No
Is the study design appropriate and is the work technically sound?
Yes
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
Yes
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Partly
References
1. Irfan A, Hussien M, Mehboob M, Ahmad A, et al.: Learning from Fullerenes and Predicting for Y6: Machine Learning and High‐Throughput Screening of Small Molecule Donors for Organic Solar Cells. Energy Technology. 2022; 10 (6). Publisher Full TextCompeting Interests: No competing interests were disclosed.
Reviewer Expertise: DFT; NLO; Solar Cells
Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
Partly
Are all the source data underlying the results available to ensure full reproducibility?
No
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: BiPVT systems, Solar Thermal Systems, Renewable energy, Power plants
Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
Partly
Are all the source data underlying the results available to ensure full reproducibility?
Partly
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Optimal design and of the standalone PV system, Parameter extraction of the PV models, MOO, and MCDM.
Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
Not applicable
Are all the source data underlying the results available to ensure full reproducibility?
No
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Solar photovoltaic, PV performance analysis, hybrid photovoltaic and thermal collector, solar thermal collector
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It is getting boring to read or do analysis based on these data presented here. However, the authors' effort to publish an article is highly appreciated.
It is getting boring to read or do analysis based on these data presented here. However, the authors' effort to publish an article is highly appreciated.