Front. Energy Res., 15 January 2026
Sec. Solar Energy
Volume 13 – 2025 | https://doi.org/10.3389/fenrg.2025.1703511
Frontiers in Energy Research
Edited by
Praveen Kumar Balachandran
Reviewed by
Suganthi Ramasamy
Dr. Vankadara Sampath Kumar
Outline
Abstract
Introduction
System configuration
Control approach
Result and discussion
Conclusion
Data availability statement
Author contributions
Funding
Conflict of interest
Generative AI statement
Correction note
Publisher’s note
References
FIGURE 1
FIGURE 2
FIGURE 3
FIGURE 4
FIGURE 5
FIGURE 6
FIGURE 7
FIGURE 8
FIGURE 9
FIGURE 10
FIGURE 11
FIGURE 12
TABLE 1
Theoretical calculations for boost converter design.
TABLE 2
PV array parameters.
Front. Energy Res., 15 January 2026
Sec. Solar Energy
Volume 13 – 2025 | https://doi.org/10.3389/fenrg.2025.1703511
Manish Kumar ¹
Kumar Shubham ¹
Kshitij Tiwari ¹
Asha Anu Kurian ¹
Kamaraj Jamuna ¹
Odiyur Vathanam Gnana Swathika ^2*
1. School of Electrical Engineering, Vellore Institute of Technology, Chennai, India
2. Centre for Smart Grid Technologies, Vellore Institute of Technology, Chennai, India
Article metrics
Introduction:
Reliable electricity in remote regions requires standalone photovoltaic (SAPV) systems, which operate without grid support. However, solar energy fluctuation due to irradiance and temperature significantly affects power generation. Maximum Power Point Tracking (MPPT) techniques are essential to ensure that PV panels operate at peak power under changing environmental conditions. This work integrates a Perturb & Observe (P&O) MPPT algorithm with a Proportional-Integral (PI) controller to enhance power extraction and voltage regulation. Real-time validation is performed using OPAL-RT Hardware-in-the-Loop (HIL) testing to assess performance under dynamic scenarios.
Methods:
A boost converter is designed for an 18 V PV module, boosting the output to around 60 V. The PV system model is developed in MATLAB/Simulink and linked to OPAL-RT RT-LAB for real-time execution. The MPPT controller uses voltage and current measurements to adjust duty cycle, while a PI controller stabilizes voltage near the peak power point. Simulations and HIL testing are carried out under variable irradiance.
Results and Discussion:
Simulation and HIL results confirm voltage boosting with improved stability compared to standalone P&O control. Minor oscillations around MPP are reduced through PI tuning. Efficiency exceeds 95%, demonstrating reliable tracking under varying irradiance. The real-time platform validates design scalability for off-grid deployment.
The necessity of solar energy harvesting in off-grid areas stems from the need for sustainable, cost-effective, and reliable electricity in regions without infrastructure. Solar power provides a clean energy source that improves living standards, supports businesses, and enables essential services like healthcare and education in rural communities. SAP) system is an independent solar power setup that operates without grid connection. It includes solar panels, batteries for storage, a charge controller, and an inverter to convert DC to AC power. SAPV systems harness solar energy to supply electricity in remote areas where grid access is unavailable or unreliable. Solar energy systems are heavily influenced by factors like irradiance, temperature, and partial shading. To meet load demands effectively, PV systems must consistently operate at maximum power, which is achieved through various maximum power point tracking (MPPT) techniques. Each method involves a trade-off between accuracy and stability near the maximum power point. Additionally, the performance is affected by the DC-DC converter and varying DC load conditions. Hence, designing an efficient system that maintains high performance under dynamic environmental and load variations is essential for reliable solar energy utilization (Sharma, 2023).
OPAL-RT technology plays a vital role in advancing research into microgrid, distributed generation, renewable energy and power systems. Research areas include optimizing fuel cell microgrid, active power compensation filters, MPPT in photovoltaic systems, DC microgrid stability, and protection mechanisms such as anti-islanding techniques. For real-time control validation, OPAL-RT is employed in simulations of custom power devices like Static VAR Compensators (SVCs) and HVDC transmission systems. It also integrates artificial intelligence to enhance control strategies in electric drives and induction motors. Its real-time capabilities provide deeper insights into system dynamics, making it an essential tool for advancing sustainable energy solutions (Mohanty et al., 2018). Software in loop (SIL) is a simulation method where the control system code is tested within a virtual simulation environment. The code runs on a computer or simulator, which models the system dynamics, without involving any actual hardware. HIL (Hardware in loop) is a technique where real hardware components are integrated into a simulation loop. The actual hardware, such as controllers or sensors, interacts with a real-time simulation of the system that it would control in real life. This allows engineers to test how the hardware performs in a safe, controlled environment before using it in the real world.
Photovoltaic (PV) system serves as a highly used electrical generator, spurred by the increasing need for clean energy and its ease of deployment. The rising use of PV arrays is largely attributed to the significant potential of renewable energy. However, these systems encounter obstacles in terms of conversion efficiency and variable power output, mainly caused by changes in irradiation and temperature (Subrata et al., 2019).
Throughout the day, the intensity of solar radiation varies as a result of the shifting azimuth angle of the sun. On overcast days, solar energy received is considerably less than on clear, sunny days. Furthermore, the power output of a PV panel is influenced by its resistive load, which can reduce electricity generation even under consistent irradiance and temperature conditions. For each specific load, a maximum power point (MPP) can be identified; however, this point continuously shifts due to changing environmental factors such as sunlight and temperature. As a result, accurately locating and maintaining operation at the MPP presents a significant challenge (Mann et al., 2023).
MPPT is an essential component in PV systems as they enhance the overall efficiency by maximizing the power output. MPPT algorithms are required because PV arrays exhibit a non-linear voltage-current relationship, with a specific point where power generation is at its peak. The output power from solar panels fluctuates due to factors like solar irradiance and temperature. To optimize energy extraction, it is crucial to operate the PV system at the MPP. Common algorithms, such as perturb and observe (P&O) and incremental conductance (INC), control the DC-DC boost converter to continuously track and maintain operation at the MPPT (Gomathy et al., 2012; Suryavanshi et al., 2012). PSO shows greater capability in tracking for true maximum power point under partial shaded conditions with average output efficiency up to 99.92% compared to P&O which is only 76.76% (Tiong et al., 2019; Zdiri et al., 2021).
Different techniques are used to optimize the output of this PV system few already tried earlier with significant result as a boost converter connected to the PV system. To the output voltage of the PV system, control strategies are applied based on the boost converter’s characteristics. A combination of Particle Swarm Optimization (PSO) and a Proportional-Integral (PI) controller is employed to maximize power extraction from the PV system. The PI controller ensures that maximum power is consistently tracked from the PV panel, even under varying atmospheric conditions (Nagarajan et al., 2017).
Section 2 discusses about System Configuration and Section 3 elaborates about the control strategy deployed in the proposed system. Section 4 elaborates the results and discussion of the system under consideration.
Figure 1 shows the overall block diagram of the proposed system. PV array captures solar energy and converts it into direct current (DC) electricity. Under standard conditions, it produces an output voltage of approximately 18 V, with a maximum power generation of around 35 W. Variations in solar irradiance and environmental factors may affect these values, requiring power regulation for stable operation. As for the boost converter the basic circuit is designed based on the relevant calculation and constraints. The Boost converter is designed for typical 80 W panels and to boost it approximately to 60 V under steady dc voltage supply conditions. The next phase is to stabilise the output and track down the maximum power and adjust the duty cycle accordingly. Relevant blocks like comparator, reference signal and gain are added to do the same. The MPPT controller implementing the P&O algorithm comprises components such as delay units, summation operators, multiplication blocks, gain elements, switching logic and saturation limit. Proportional integral (PI) controller stabilizes the processed signals from these algorithmic blocks. This control architecture operates by iterating through successive cycles of photovoltaic array output measurements, systematically comparing current-cycle parameters (voltage, current, and power) against those from the preceding cycle. These comparative evaluations trigger whenever measurable parameter variations occur in the PV output characteristics.
FIGURE 1
Block diagram for overall work.
Compared with conventional MPPT techniques, the intelligent MPPT like FLC, ANN and ANFIS techniques show high tracking efficiency of MPP and less steady-state oscillation in rapidly changing weather conditions without prior knowledge of the mathematical model. However, these methods suffer from implementation complexity, long response times, big data processing, and high realization cost (Boubaker, 2023).
Studies show that combining PI control with P&O algorithms offers a highly effective strategy for enhancing voltage stability in PV systems. Traditional direct duty ratio control methods, while commonly used for MPPT, often suffer from drawbacks such as excessive stress on power electronic components and increased power losses due to abrupt changes in duty cycles. To address these challenges, researchers explore the integration of a PI controller with the P&O algorithm, leveraging the PI controller’s ability to regulate the panel voltage more smoothly and maintain system stability (Habibah et al., 2024).
By incorporating a PI controller into the control loop, the system mitigates oscillations around the MPP and reduce the sudden fluctuations that typically arise in conventional P&O implementations. This combined PI-P&O strategy ensures a more refined control mechanism, leading to improved steady-state performance, reduced power losses and enhanced overall system efficiency. Additionally, the closed-loop nature of this approach contributes to a more stable response under varying environmental conditions, such as fluctuations in solar irradiance and temperature. As a result, this hybrid control method is widely recognized for its ability to optimize power extraction while minimizing adverse effects on the power conversion process in PV systems (Banerjee, 2014; Lotfy and Hussein, 2025).
The MPPT algorithm is an effective method for maximizing the performance of solar panels, particularly in systems that do not track the sun’s position. These techniques enhance the efficiency of PV systems by ensuring maximum power output from the PV array. Various MPPT methods are developed for PV applications, with the P&O technique being among the most commonly implemented. In this method, the output voltage (V_out) and current (I_out) of the converter are monitored using voltage and current sensors, respectively (Kabalci et al., 2015; Nataraj et al., 2024).
The system dynamically adjusts the duty cycle value through pulse-width modulation to maintain operation at the photovoltaic array’s maximum power point. Three comparator switches that form decision-making nodes evaluates whether the voltage (ΔV) and power (ΔP) differentials show positive or negative trends. A gain block configured with a factor of −1 feeds into subsequent circuitry to invert polarity when required by the control logic. The architecture implements a feedforward loop incorporating a PI controller combined with a saturation block that constrains output signals between operational thresholds of 16.34 and 18.92 units to bring the voltage in stability limits so that V_max = 1.1p.u and V_min = 0.95p.u. A delay component completes this regulatory circuit by introducing temporal sampling intervals that enable error signal analysis and subsequent duty cycle optimizations.
With an input voltage of 17.7 V, output voltage of 60 V, switching frequency of 10 kHz and power of 80 W, the corresponding input and output currents are 4.51 A and 1.33 A respectively. To ensure stable and efficient operation, the inductor current ripple is limited to 1% of input current, minimizing losses and component stress. Likewise, the output voltage ripple is set to 1% of the output voltage, maintaining voltage stability for the load. Based on these values, the required inductance (27.73 mH) and capacitance value greater than (156 µF) are calculated to support smooth energy transfer and ripple reduction at the output. Selection of capacitor for simulation is done to reduce the ripple voltage A duty ratio of 0.705 is determined for proper voltage boosting.
Table 1 shows the theoretical calculation of the boost converter.
TABLE 1
Theoretical calculations for boost converter design.
Figure 2 shows the Simulink model of the overall circuit which is further implemented in OPAL-RT RT-LAB software. As in Figure 3, the first block namely PV Array takes the input as irradiance and it directly feeds to the boost converter specially designed for the PV specifications. Also, PV array block feeds its output to next set of subsystems whose ultimate purpose is to serve as a controller unit.
FIGURE 2
Simulink Model of Circuit with controller.
FIGURE 3
Control method.
The final output from this controller is fed in as a pulse to the boost converter. For now, a resistive load is considered for observing the output of the boost converter and the characteristic of the same is observed. The PV array specifications are shown in Table 2. The irradiance is set at 1000 W/m² and temperature at 25 °C for the initial testing of controller and converter. The model works well for those values.
TABLE 2
PV array parameters.
As depicted in Table 2, a single-string, single-module PV array is considered with design parameters: open-circuit voltage (Voc) of 20.23 V, maximum power voltage (Vmp) of 17.2 V, short-circuit current (Isc) of 2.18 A, and maximum power current (Imp) of 2.04 A. The module consists of 30 cells, with irradiance and temperature set to 1000 W/m² and 25 °C, respectively. The boost converter is designed with an inductance of 27.53 mH and a capacitance of 156 µF for a 45 Ω load.
The switching block continuously monitors voltage levels, adjusting power iteration through a gain block with a step size of −1 to maintain peak power for direction of gradient updates. A PI controller, regulated via MATLAB’s auto-tuned Kp and Ki values, ensures power stability within predefined limits. Figures 4a,b depicts the basic architectures of centralized and decentralized controlling while hybrid architecture is nothing but a mixture of other two.
For real-time validation, the circuit is implemented in OPAL-RT’s RT-LAB with minor parameter modifications. The simulation demonstrated that an 17 V input is successfully boosted to 38.46 V. In RT-LAB, the Master unit manages real-time computations, synchronizing tasks and handling inter-process communication. The Console serves as the user interface, allowing users to control simulations, adjust parameters and analyze real-time results. When integrating MATLAB/Simulink models into RT-LAB, the Master executes real-time tasks while the Console facilitates deployment and interaction (Nath et al., 2024).
FIGURE 4
(a) PV Voltage waveform. (b) Boost converter input Voltage and (c) Power input of the boost converter.
As shown in Figure 3, the control approach revolves around ultimately varying the duty cycle of the converter according to irradiance of the area. The aim of the proposed work is to increase the duty cycle of the converter when the irradiance becomes low i.e. in a weather which is slightly cloudy whereas the duty cycle is to be decreased when the irradiance passes a certain threshold or upper value. The circuit aims to hold on to MPP within that specific region of power curve by adjusting the duty ratio. Figures 4, 5 shows the input and output of PV array and boost converter respectively. The PV array gives a voltage of around 17 V with current and power being 3.5 A and 64 W respectively. After being through the boost converter and controller the outputs received were 1.2 A, ∼55 V, ∼64 W respectively. Thus, a significant amplification in voltage is observed. Figure 4a represents the a) PV Voltage waveform b) Boost Converter input Voltage and c) Power input of the boost converter illustrates the dynamic response of a PV–Boost Converter system over a simulation duration of 10 s. The voltage exhibits a very small settling transient at the start and quickly stabilizes near its rated operating voltage. This indicates that the voltage regulation or MPPT control is effectively holding the panel at its optimal operating point.
FIGURE 5
Master’s window in OPAL RT for the Simulink model.
For practical considerations and real-world constraints, the circuit is implemented in OPAL RT. The result obtained matches with the simulation results obtained earlier. Figure 6 denotes the output voltage obtained in software in loop simulation which illustrates that voltage ripple is 0.01 and voltage is 38.04 V. The master and console are as shown in Figures 5, 7 respectively. Figure 11 shows the output waveform that depicts how the system adjusts the environment when tested HIL mode. The magnitude of noise was higher when the execution was done in HIL setup but the change in parameters were noticed in real time.
FIGURE 6
Output waveform in OPAL RT 4610 in DSO by Software in loop simulation in hardware synchronized mode.
FIGURE 7
Console’s window in OPAL RT for the Simulink model.
The driver circuit is observed as in Figure 8. The tesing of driver circuit is as shown in Figure 9 and the output waveform is as shown in Figure 10. In HIL, the driver circuit, boost converter and controller are all interfaced to OPAL RT as shown in Figure 11. The testing of Driver circuit for pulse output was done separately by interfacing it through OPAL RT to check the working of boost converter. The setup prototype can be tested through any RPS. The designed boost converter was designed to operate around 17 V which was boosted to 24 V which was measured with multimeter. The OPAL RT crucial capability of HIL testing allows integration of software and hardware by parts thus the controller can be as it is as in Simulink block.
FIGURE 8
Gate driver circuit.
FIGURE 9
Testing of driver circuit.
FIGURE 10
Output of driver circuit.
FIGURE 11
Overall setup required for Hardware implementation.
The voltage trajectory in Figure 12 is partitioned into five operating zones to illustrate the dynamic influence of irradiance variations on load performance in software in loop simulation. In Zone 1 (0–3.5 s), the irradiance exhibits a gradual increment, causing a corresponding rise in the load terminal voltage as the operating point moves toward the maximum power region. Upon transition to Zone 2 (3.5–5.9 s), a moderate decrease in irradiance is introduced, leading to an observable reduction in voltage. The voltage stabilizes after settling to a new operating point, indicating the system’s steady-state adaptation under reduced insolation. In Zone 3 (5.9–6.5 s), the irradiance is further curtailed, mimicking a heavier shading condition. As expected, the PV voltage drops more noticeably before reaching another equilibrium point. Zone 4 (6.5–7.7 s) represents a period of nearly constant low irradiance, during which the voltage maintains a flat voltage profile, confirming stable low-irradiance operation without additional disturbances. Finally, in Zone 5 (7.7–9 s), the irradiance level is restored to a high value, and the PV voltage promptly increases to its elevated operating region, demonstrating rapid system recovery following irradiance enhancement. Overall, the distinct voltage plateaus and transitions across the five zones confirm the strong irradiance–voltage dependency of PV systems. Higher irradiance conditions consistently yield higher terminal voltages, whereas shading or irradiance reduction produces proportionate voltage drops. The presented waveform further highlights the capability of the PV system—and the associated control mechanism—to track and respond reliably to dynamically changing environmental conditions. Settling time in the voltage response for zone 1 0.12 s in zone 1. Performance metrics from simulation results reveals the system is capable of PV tracking efficiency of 99.8% and voltage ripple of 1% after proper selection of output capacitance of 1000 uF. Overall efficiency of the converter is 94.71%. The mean voltage in steady-state matches the reference value, Steady state error is neglible.
FIGURE 12
Output voltage waveform for varying irradiance in different zones with irradiance.
The proposed system is tested under varying irradiance conditions to evaluate the effectiveness of the P&O, MPPT algorithm combined with a PI controller in maintaining a stable output voltage. The simulation was conducted using Simulink and OPAL-RT real-time simulation tools, ensuring high-fidelity results that closely mimic real-world scenarios. The MPPT structure is built using fundamental blocks such as comparators, adders, delay blocks and logic gates, allowing a real-time graphical representation of the power-tracking process. Under fluctuating solar irradiance, the MPPT algorithm efficiently tracks the maximum power point (MPP), adjusting the duty cycle of the DC-DC converter accordingly. However, minor oscillations around the MPP are observed due to the inherent nature of the P&O technique. These oscillations are minimized by fine-tuning the step size of the algorithm.
The PI controller plays a crucial role in stabilizing the voltage output despite variations in solar input. By dynamically adjusting the control effort based on the error signal, the PI regulator effectively compensated for sudden changes in irradiance. The transient response was analysed, showing that the voltage deviation was minimal and the system quickly returned to the desired set point, confirming the controller’s robustness.
Comparing the system’s performance with and without the PI controller demonstrated a significant reduction in voltage fluctuations. The steady-state error was reduced, and the system exhibited improved dynamic response. Additionally, efficiency calculations revealed that the MPPT controller achieved an efficiency of nearer to 95% under most test conditions, reinforcing the suitability of the proposed approach.
Overall, the results validate that the combined MPPT P&O and PI control strategy provides a reliable solution for maintaining stable PV output voltage, ensuring optimal energy harvesting even under inconsistent solar conditions. Input transients exist in the system. Future improvements could involve optimizing the PI tuning parameters using adaptive or intelligent control techniques to further enhance stability and response time.
This study demonstrates the effectiveness of integrating the MPPT Perturb and Observe (P&O) algorithm with a PI controller to maintain a stable voltage output for photovoltaic (PV) systems under varying irradiance conditions. By implementing the MPPT algorithm using MATLAB Simulink blocks, the approach becomes more intuitive, modular, and adaptable for further refinements. The PI controller successfully minimizes voltage fluctuations, ensuring efficient power delivery and improved transient response.
One of the significant advantages of this technique is its ease of implementation and scalability, making it a practical solution for off-grid solar applications. With minimal computational requirements, this method can be deployed in remote areas where grid connectivity is unavailable, ensuring a reliable power supply for rural communities, agricultural setups, and standalone renewable energy systems.
Further improvements in the controller design can enhance the system’s dynamic response, increasing bandwidth and reducing the impact of intermittency in irradiance. By optimizing PI tuning parameters or implementing adaptive control strategies, the system can respond more efficiently to rapid fluctuations in solar input, further stabilizing power output.
For future work, incorporating fuzzy logic or artificial intelligence (AI)-based MPPT algorithms can further improve tracking efficiency and reduce oscillations around the MPP. Additionally, implementing a hybrid energy storage system, such as battery integration with super capacitors, can enhance power stability and provide backup during periods of low solar availability. Real-time hardware-in-the-loop (HIL) testing using platforms like OPAL-RT could also refine the control strategy before practical deployment.
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
MK: Writing – original draft. KS: Writing – original draft. KT: Writing – original draft. AAK: Writing – review and editing. KJ: Writing – review and editing. OG: Writing – review and editing.
The authors declare that financial support was received for the research and/or publication of this article. The authors declare that financial support was received from Vellore Institute of Technology Chennai for the research and publication of this article.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The authors declare that no Generative AI was used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
This article has been corrected with minor changes. These changes do not impact the scientific content of the article.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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Keywords
HIL (hardware-in-the-loop), OPAL-RT, MPPT (maximum power point tracking), P&O (perturb and observe), RPS (regulated power supply), SAPV (stand-alone photovoltaic)
Citation
Kumar M, Shubham K, Tiwari K, Kurian AA, Jamuna K and Gnana Swathika OV (2026) Optimal power extraction and voltage regulation in standalone photovoltaic system. Front. Energy Res. 13:1703511. doi: 10.3389/fenrg.2025.1703511
Received
11 September 2026
Revised
20 November 2025
Accepted
24 November 2025
Published
15 January 2026
Corrected
22 January 2026
Volume
13 – 2025
Edited by
Praveen Kumar Balachandran, Universiti Kebangsaan Malaysia, Malaysia
Reviewed by
Suganthi Ramasamy, University of Cagliari, Italy
Vankadara Sampath Kumar, National Institute of Technology Mizoram, India
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Copyright
© 2026 Kumar, Shubham, Tiwari, Kurian, Jamuna and Gnana Swathika.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Odiyur Vathanam Gnana Swathika, gnanaswathika.ov@vit.ac.in
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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