Dynamic Modeling of on Grid-Connected Photovoltaic Setup in Pakistan using MATLAB

This research presents a computational modeling and simulation framework for grid-connected photovoltaic (PV) systems in Pakistan utilizing MATLAB/Simulink. Employing a Perturb and Observe (P&O) MPPT algorithm, the research develops a detailed model of a 7.5 kW PV installation in Lahore, analytically examining power generation dynamics, inverter efficiency, and grid interaction. The simulation demonstrates the system’s capability to mitigate grid dependency, with results showing effective power optimization and smooth transitions between solar generation, grid supply, and battery storage. The study bridges knowledge gaps by providing empirical insights into real-time PV system electrical characteristics, presenting a methodological approach for optimizing renewable energy system design and deployment. By quantitatively analyzing power flow, inverter performance, and battery efficiency, the research contributes valuable computational strategies for enhancing solar energy integration in regions with high solar irradiation potential.

Introduction

The global energy landscape is shifting toward renewable energy sources due to concerns about environmental sustainability, energy security, and rising fossil fuel costs. Photovoltaic (PV) systems have emerged as a key solution for decentralized energy generation, particularly in residential and commercial sectors. Solar energy’s scalability, declining costs, and minimal environmental impact make it a preferred choice for clean energy generation. Pakistan faces a persistent energy crisis characterized by frequent power shortages, high reliance on imported fossil fuels, and an overstressed grid infrastructure. These factors contribute to energy insecurity, increased costs, and a negative impact on economic growth. With over 1700 kWh/m²–2000 kWh/m² annual solar irradiation in regions like Lahore, solar PV presents a viable solution to mitigate energy shortfalls and reduce dependency on the national grid. Government initiatives such as net metering policies and renewable energy incentives have further accelerated the adoption of solar PV systems in urban residential areas like DHA Lahore.

Previous studies have primarily focused on the financial feasibility of PV systems, often using economic modeling tools such as System Advisor Model (SAM) and HOMER Pro. Advanced economic assessment tools have demonstrated significant potential in evaluating photovoltaic (PV) system performance through metrics such as payback period, internal rate of return (IRR), and levelized cost of energy (LCOE). Preliminary research conducted on a 7.5 kW PV installation in DHA Lahore revealed an unprecedented payback period of 2.7 years, underscoring the system’s economic viability. However, existing financial analyses exhibit a critical limitation: they neglect the significant electrical dynamics influenced by solar irradiance variability, grid interaction complexities, and load fluctuation patterns. Site location is illustrated in Figs. 1 and 2 shows the designed system structure in Homer pro. This diagram shows 7.63 kW solar panels connected to a DC busbar and 8.2 kW PV inverter along with backup batteries connecting the system to household and power grid. Household load is also designed as per annual energy consumption data of the house collected through electric bills.

Fig. 1. Site Location on Google Maps (Latitude: 31°27′49.0′′N, Longitude: 74°28’25.7”E).

Fig. 2. System Structure in Homer Pro.

In addressing the computational modeling gap between financial projection and electrical performance, this research develops a sophisticated MATLAB/Simulink simulation architecture. It is to develop an integrated computational model for electrical characteristics of grid integrated PV systems. The study features concern with critical performance parameters including power generation dynamics, inverter efficiency optimized, battery storage characterizations and grid interaction mechanisms under various operational situations.

Through combining economic modeling with high fidelity electrical simulation, this research develops a holistic computational framework for studying the PV system performance. The methodology proposed provides valuable information to delineate system design objectives, deployment strategies, and long-term operation analysis.

The manuscript is structured to systematically present the research progression: The overall methodology including the PV system configuration and the MATLAB/Simulink modeling approach is provided in Section 3, Section 4 provides the empirical results, Section 5 conducts the in-depth performance analysis under different conditions, Section 6 synthesizes the key findings and proposes future research trajectories.

Literature Review

Significant transformative developments have been made in computational methodologies for analysis of photovoltaic systems, combining the most advanced machine learning architectures with proven simulation frameworks. Rodriguez et al. [1] presented a pioneering hybrid simulation approach, that leveraged the power of machine learning algorithmic constructs combined with traditional MATLAB/Simulink modeling architecture. Intelligent computational techniques might provide remarkably high correlation between the simulated and empirical metrics for renewable energy systems, as was shown by the presented computational framework which achieved 97.3% correlation of the simulated and empirical power generation metrics.

In their work, Chen et al. [2] developed a comprehensive multi physics simulation platform which substantially improved conventional PV module representations by including important thermal, electrical and mechanical dependency models. A sophisticated computational approach to decomposition of the degradation mechanism for system reliability assessment was provided with the capability of unprecedented long term reliability assessment.

PV system optimization methodologies are revolutionized by the integration of artificial intelligence techniques. For their study, Kumar and Srinivasan [3] proposed an innovative Maximum Power Point Tracking (MPPT) algorithm based on convolutional neural network architectures. Using their novel approach, their tracking efficiency was shown to be 15% better than using the more traditional Perturb and Observe (P&O), demonstrating the power of neural network driven optimization tactics.

Recent research has produced advanced grid interaction methods. Adaptive impedance matching algorithms were presented by Wang et al. [4] which led to seamless bidirectional power exchange through a sophisticated inverter control strategy. Exceptional grid stability under a wide range of load conditions was demonstrated in experimental validation. Gupta et al. [5] also looked at hybrid energy storage architectures, combining supercapacitor technologies with traditional battery systems to increase grid responsiveness by nearly 40% in transient response times.

The emphasis of recent research was to develop sophisticated MPPT algorithm development. A fuzzy logic based MPPT algorithm with adaptive learning mechanisms which shows better instantaneous converged characteristics under the rapidly varying irradiance conditions was developed by Srinath et al. [6]. Nair and Kumar [7] systematically compared selected emerging MPPT techniques and found that fractional open circuit voltage and incremental conductance algorithms typically outperformed traditional P&O algorithms in dynamic environmental conditions.

Researchers have addressed critical environmental challenges affecting PV system performance. Based on these observations, Mehta et al. [8] proposed a comprehensive dust mitigation modeling framework which uses machine learning based predictive maintenance algorithms with real time performance monitoring algorithms. Zhang and Li [9] studied partial shading compensation strategies, and an advanced reconfiguration algorithm based on dynamic series-parallel module connection optimization, which recovers up to 22% power for nonuniform lighting conditions.

Advanced computational frameworks for emerging photovoltaic technologies promise further research directions. González-Martínez et al. [10] presented a comprehensive computational method to explore novel photovoltaic technologies, and to provide critical material level performance characteristics.

Several PV system modeling initiatives using MATLAB/Simulink have been demonstrated in the research literature. Banik et al. [11] have done a thorough single phase solar PV rooftop system analysis with emphasis on grid sync and MPPT strategy. With sophisticated single diode model for simulating performance under arbitrary environmental conditions, Goyal et al. [12] had attempted for precise PV module design.

A SIMULINK model of a 20 kW PV station integrated with a local electrical network was investigated by Sapaev et al. [13]. The purpose of their study was to create an experimental platform for teaching purposes, ensuring that the simulated parameters very nearly matched experimental data, and thereby confirming that the model was reliable. With the addition of experimental results, this work has been found particularly valuable in the academic context, providing practical insights to renewable energy system integration.

Revolving around the comparison of different methods for PV analysis, namely mathematical modeling, Simscape modeling and MATLAB coding, Revati and Natarajan [14] presented in detail their implementation of the I–V and P–V characteristics of PV modules. All methods resulted in similar conditions, and mathematical modeling was a convenient and flexible method for adjusting parameters.

Performance of half-cell PV modules under partial shading conditions was investigated by Sarniak [15] with a double diode model in MATLAB/Simulink. The simulation was done under different shading conditions, and the results were confirmed by experimental measurements. The findings showed the capability of half-cell designs to deliver better energy output under suboptimal conditions and provide the practicality for PV installations in urban environments with shading problems.

Ali and Mohamed [16] proposed a modified perturb and observe (P&O) MPPT algorithm that splits the P-V curve into different regions depending on the estimations of open circuit voltage. Under dynamic conditions, this approach significantly improved the dynamic tracking efficiency to 99.7% by reducing the search area to 15% of the P–V curve, improving speed and accuracy of power point tracking.

Later, Şahin and Blaabjerg [17] suggested a type of hybrid system involving PV battery and supercapacitor for voltage stabilization and extension of battery life. Super capacitors were shown to be able to handle instantaneous peak current to reduce battery load and reduce overall system costs by utilizing their MATLAB/Simulink model. The hybrid storage solution proved crucial to grid stability and energy efficiency in these renewable energy systems.

However, Khasawneh et al. [18] used MATLAB/Simulink to study the effect of dust accumulation on performance of PV system. A model that takes dust coverage variation into account and the resulting impact on power output was proposed, and sheds light on maintenance strategies of PV installations in dusty environments [18].

Together these studies impress on the great importance of MATLAB/Simulink in modeling, simulation and optimization solar PV systems. Since then, MPPT algorithms, hybrid energy storage implementation, and system integration innovations have greatly improved the efficiency, reliability, and appropriateness of PV technologies. By advancing these developments, they help adopt renewable energy solutions more broadly, solving the need for sustainable energy system all over the world.

Methodology

Overview of the PV Installation

The site location coordinates are 31° 27′ 49.0′′ N latitude and 74° 28′ 25.7′ E longitude. Fig. 1 shows the site location of the installed system. The selected house is equipped with a grid-tied Solar PV unit which supplies electric power to the house and feeds extra energy back to the grid during daytime. Inverter and Solar PV panels are two major parts of a Grid-tied system. This Solar PV unit is equipped with an 8.2 kW hybrid inverter and 14 solar panels of 545 watts each totaling 7.5 kW. The system consists of Canadian Solar MaxPower CS6U-340M panels and the Fronius Primo 8.2 inverter. Four batteries are also included with the inverter to provide power whenever there is power blackout. The batteries utilized are BAE Secura PVS BLOCK Solar 12 V 3 PVS 210. Table I shows the technical specifications of the overall setup installed at the site.

Table I. Technical Specifications of Overall Setup

Sr	Component	Specification
1	Number of panels	22 (2 × 11)
2	Rated power per panel	340 W
3	Total solar power output	7480 W
4	Inverter	Fronius Primo 8.2
5	Maximum output power	8.2 kW
6	Number of MPPT inputs	2
7	Battery model	BAE Secura 12V3PVS210
8	Number of batteries	4
9	Total battery voltage	48 V
10	Battery capacity per unit	211 Ah

MATLAB/Simulink Model Configuration

To analyze the electrical performance, a detailed MATLAB/Simulink model was developed, replicating the key components and operational characteristics of the PV system. Fig. 3 illustrates the overall structure of the model developed in MATLAB/Simulink. The model consists of PV panel setup, along with MPPT converter and Inverter. Additionally, the battery pack is simulated as a single battery block and the Grid is also simulated along with the load.

Fig. 3. Overall structure of the installed system modelled in Simulink.

PV Array

The PV array is modeled using the manufacturer’s specifications as provided in Table I incorporating parameters such as irradiance levels, temperature coefficients, and current-voltage characteristics. The model dynamically adjusts output based on environmental conditions thus simulating real-world solar energy production. This block models the Canadian Solar CS3U-340M. It takes the irradiance as input and outputs the generated DC voltage and current. Fig. 4 shows the IV and PV curves of the installed PV system with respect to temperature and irradiance. The output power of the PV array depends on irradiance ($G$) and temperature ($T$) and can be defined as:

$$\begin{array}{r}{I}_{pv}=I_{sc}\times \left(G/G_{ref}\right)\times [1+{\alpha }_I\times (T-T_{ref})]\end{array}$$

Fig. 4. PV and IV curves of installed PV System.

where Isc is short-circuit current at reference conditions (A), Gref is reference irradiance (1000 W/m²), αI is temperature coefficient for current (A/°C), and T_ref = reference temperature (25 °C).

$$\begin{array}{r}{V}_{pv}=V_{oc}-{\beta }_V\times (T-T_{ref})\times ln\left(G/G_{ref}\right)\end{array}$$

where Voc is open-circuit voltage at reference conditions (V), βV is temperature coefficient for voltage (V/°C) and the output power is:

$$\begin{array}{r}{P}_{pv}=I_{pv}\times V_{pv}\end{array}$$

Inverter Block

An inverter block simulates the Fronius Primo inverter’s operation, including MPPT algorithms for optimal energy conversion. The inverter is also modeled to manage grid synchronization, ensuring smooth power transfer to and from the grid. The model divides into two parts, one that is a control system as shown in Fig. 5. It includes the following key components:

• Grid Voltage Source ($V_{grid}$): This block represents the voltage of the grid to which the inverter is connected. It provides a sinusoidal voltage source with a specific amplitude and frequency.

• Reference Signal Generation: This section generates the reference signal for the inverter. It seems to involve a sine wave generation block, a gain block, and potentially some phase shifting or filtering.

• Inverter Model: This block represents the actual power inverter, which converts DC power from a PV array or battery into AC power to feed into the grid.

• Control Loop: This section includes the control algorithm that regulates the inverter’s output voltage and current to match the grid requirements. It likely involves various control blocks like proportional-integral-derivative (PID) controllers, comparators, and integrators.

• Output Stage: This section represents the inverter’s output stage, which includes power electronic switches and transformers to shape the output waveform and interface with the grid.

Fig. 5. Control block of the inverter.

The second part is the switching circuit which is controlled by above control block as shown in Fig 6. Fig. 6 shows the structure of the 5-level Cascaded H-Bridge (CHB) inverter. It consists of the following parts:

• DC Sources: Four DC sources (V1, V2, V3, V4) represent the input DC voltage levels for each H-bridge module.

• H-Bridge Modules: Four H-bridge modules are used to generate multiple voltage levels at the output. Each module consists of four switches (IGBTs) that can be turned on or off to control the output voltage.

• Output Transformer: The transformer steps up or steps down the voltage and isolates the output from the DC sources.

• Pulse Width Modulation (PWM) Generation: The PWM signals control the switching of the H-bridge modules. These signals determine the duty cycle of each switch, which in turn determines the output voltage level.

• Current and Voltage Sensing: The current and voltage sensors measure the output current and voltage, respectively.

• Control Algorithm: The control algorithm processes the measured values and generates the appropriate PWM signals to regulate the output voltage and current.

Fig. 6. Switching block of the inverter.

The 5-level CHB inverter operates by selectively switching the H-bridge modules to generate different voltage levels at the output. By combining the voltages of the individual modules, a staircase-like waveform is produced.

$$\begin{array}{r}{P}_{ac}={\eta }_{inv}\times P_{pv}\end{array}$$

where ${\eta }_{inv}$ is the inverter efficiency (typically 97%–98%).

$$\begin{array}{r}{P}_{ac}=V_{ac}\times I_{ac}\end{array}$$

where $V_{ac}$ and $I_{ac}$ are the AC voltage and current, respectively.

MPPT Converter Block

The MPPT block is responsible for maximizing the power output of a PV array to its maximum power point (MPP) via continuous modulation of the operating voltage of the PV array. In solar PV system, one of the important block is MPPT block as shown in Fig. 7, which maximizes power output from PV arrays. Using Perturb and Observe (P&O) algorithm. Power output is optimized by use of P&O algorithm. In this algorithm, I perturb the operating voltage of the PV array and measure the change in power. In order to achieve the convergence to the maximum power point (MPP), the direction of perturbation is adjusted according to the power change.

Fig. 7. MPPT converter modelled in Simulink.

The duty cycle for the DC-DC converter is then determined after the MPP is found. This duty cycle regulates the switching of the converter’s components, ensuring that the output voltage matches the desired level. The regulated DC voltage from the converter can then be utilized to power various loads or fed into the grid.

Battery Backup Block

The battery storage system is modeled to simulate charge and discharge cycles, energy storage capacity, and battery efficiency and illustrated in Fig. 8. It accounts for depth of discharge and charging dynamics, providing insights into backup performance during grid outages. The State of Charge (SOC) is estimated by the following equation:

$$\begin{array}{r}SOC\left(t\right)=SOC(t-1)+\left(P_{bat}/C_{bat}\right)\times \mathrm{\Delta }t\end{array}$$

Fig. 8. Battery backup block.

where Pbat is battery power (charging or discharging) (W), Cbat is battery capacity.

This provides the SOC to determine when to charge the battery or when to stop discharging. The power flow of the battery is estimated using this equation:

$$\begin{array}{r}{P}_{bat}={\eta }_{bat}\times (P_{pv}-P_{load})\left[charging\right]\end{array}$$

where ${\eta }_{bat}$ is the battery efficiency.

Grid Interaction Block

This block simulates bidirectional power flow between the PV system and the grid as illustrated in Fig. 9. It models scenarios where excess energy is exported to the grid and when energy is imported during periods of low solar generation or high demand. The grid interaction is modeled by using this:

$$\begin{array}{r}{P}_{grid}=P_{pv}-P_{load}-P_{bat}\end{array}$$

Fig. 9. Load and Grid interaction block.

where $P\mathrm{\_}load$ is the household load demand (W). The total flow of power is estimated by using:

$$\begin{array}{r}{P}_{grid}=P_{export}\text{if}{P}_{pv}>P_{load}\end{array}$$

$$\begin{array}{r}{P}_{grid}=P_{import}\text{if}{P}_{pv}<P_{load}\end{array}$$

PandO Algorithm

Maximum Power Point Tracking (MPPT) is a widely used technique, wherein, the operating voltage is continuously adjusted by the P&O algorithm in order to maximally output the available power of a photovoltaic (PV) system. It does this by perturbing (increasing or decreasing) the voltage and watching the effect on the output power. By comparing current power output from the current and previous power outputs, the algorithm attempts to converge towards the maximum power point (MPP).

Let $P\left(t\right)$ represent the power at time $t$, defined as $P\left(t\right)=V\left(t\right)\cdot I\left(t\right)$, where $V\left(t\right)$ is the PV array voltage and I(t) is the current. The P&O algorithm compares the power change $\mathrm{\Delta }P=P\left(t\right)-P(t-1)$ and the voltage change $\mathrm{\Delta }V=V\left(t\right)-V(t-1)$.

If $\mathrm{\Delta }P>0$, the perturbation direction is maintained, indicating that the system is moving towards the MPP. Conversely, if $\mathrm{\Delta }P<0$, the direction is reversed to approach the MPP. The flow is as follows:

1. Measure $P\left(t\right)$ and $V\left(t\right)$ at each iteration.

2. Compute $\mathrm{\Delta }P=P\left(t\right)-P(t-1)$ and $\mathrm{\Delta }V=V\left(t\right)-V(t-1)$.

3. If $\mathrm{\Delta }P>0$ and $\mathrm{\Delta }V>0$, increase V by a small step $\mathrm{\Delta }{V}_{step}$; if $\mathrm{\Delta }P>0$ and $\mathrm{\Delta }V<0$, decrease $V$.

4. If $\mathrm{\Delta }P<0$, reverse the perturbation direction to move back towards the MPP.

5. Update $P(t-1)$ and $V(t-1)$ with current values and repeat.

Mathematically, the algorithm can be represented as:

$$\begin{array}{r}V(t+1)=V\left(t\right)+\mathrm{\Delta }{V}_{step}if\mathrm{\Delta }P>0\end{array}$$

$$\begin{array}{r}V(t+1)=V\left(t\right)-\mathrm{\Delta }{V}_{step}if\mathrm{\Delta }P<0\end{array}$$

This iterative process ensures convergence to the MPP under varying irradiance and temperature conditions, maximizing the efficiency of the PV system.

Overall Flow and Working of the System

The PV system’s operation integrates several interconnected subsystems, including the PV array, inverter, P&O MPPT algorithm and battery management logic. $V_{pv}$. The MPPT controller, employing the P&O algorithm, dynamically adjusts the operating voltage $V_{pv}$ to ensure that the PV array operates at its MPP. Initially, the PV array generates DC power based on solar irradiance $G$ and temperature $T$, as described by $P_{pv}=I_{pv}\cdot$. MPPT controller, battery storage, and grid interface. The overall system‘s dynamics are governed by the real-time interaction between these components, regulated by the the inverter then converts the optimized DC power to AC power with an efficiency η_inv, given by

$$\begin{array}{r}{P}_{ac}={\eta }_{inv}\cdot P_{pv}\end{array}$$

Simultaneously, the battery system manages energy storage and retrieval. The state of charge (SOC) evolves according to the energy balance:

$$\begin{array}{r}SOC(t+1)=SOC\left(t\right)+(P_{bat}(t)/C_{bat})\cdot \mathrm{\Delta }t\end{array}$$

where $P_{bat}\left(t\right)$ represents the battery power and $C_{bat}$ is the battery capacity. If PV generation exceeds household demand $P_{load}$, the excess power $P_{excess}=P_{ac}-P_{load}$ is either stored in the battery or exported to the grid:

$$\begin{array}{r}{P}_{grid}=P_{excess}-P_{bat}\end{array}$$

where $P_{grid}$ is the net power exchanged with the grid. During grid outages, the system prioritizes critical loads, relying on battery backup. The MPPT ensures continuous optimization of PV output, while the battery discharges according to:

$$\begin{array}{r}{P}_{bat}\left(t\right)=P_{load}-P_{pv}if{P}_{pv}<P_{load}\end{array}$$

The system balances the generation of power, load requirements, charging/discharging of battery and interaction with the grid in a highly efficient and reliable manner when under dynamic environmental conditions so as to operate optimally.

Results

The empirical findings illuminate the rich dynamic behavior of the photovoltaic (PV) system in such conditions of variable irradiance when using the Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithmic framework. The computational analysis presents a comprehensive representation of system performance in the form of parametric multidimensional visualizations. The dynamics of voltage, variances in irradiance, and the interaction between grid power and battery system dynamics are considered in this representation, as well as generation of power from photovoltaic cells.

Fig. 10 illustrates in detail the complex relationship between the two datasets, solar irradiance and power output of photovoltaic arrays. A significant computational observation emerges at the halfway point along a simulation, or 0.5 seconds. This is because the P&O algorithm exhibits adaptive responsiveness including a significant surge in power generation. The algorithmic tracking mechanism reveals amazing convergence to the MPP and a voltage adjustment strategy that is both coherent and precise. Computational efficiency of the MPPT control algorithm to dynamically optimize power extraction is validated by its systematic progression of voltage.

Fig. 10. Power from PV setup based on simulated irradiance, along with the voltage of the battery.

Fig. 11 depicts dynamics of grid interaction and present an approach to power management. Grid dependent power consumption is demonstrated during initial phases of the simulation; photovoltaic generation supports the grid rather than vice versa. The PV system as an energy management system gradually reduces its dependence on the grid as the solar irradiance increases, exhibiting a sophisticated computational architecture.

Fig. 11. Power from PV setup, and power from grid along with power utilized by the load.

The battery system analysis as shown in Fig. 12, and provides a multidimensional computational perspective on the dynamics of energy storage. The computational model State of Charge (SOC) is shown to process a charged progression following roughly 45% at which the model progresses through the secular linear trajectory. This representation suggests a charging mechanism which is algorithmically controlled and has minimum operational volatility.

Fig. 12. Battery behavior during the simulation of the model. SOC is State of Charge, Vbat is voltage of the battery, Ibat is Current of the battery utilized by load, and DC Bus is the bus voltage of the battery connected to the inverter.

From the battery voltage and current, the complex mechanisms determining the system’s response are revealed. The initial voltage exhibits a transient response, in which it stabilizes at approximately 48 V, while the current waveform shows complex charge-discharge dynamics. This negative current phase is a computational strategy of energy balancing and of strategic grid support.

The computational model of the DC bus voltage can provide additional insights into system synchronization and power management dependent on characteristics of the battery voltage. The gradual stabilization of the voltage indicates an effective interface between the grid interaction mechanisms at the battery storage mechanisms.

The excellent computational performance of the P&O algorithm is evident by its ability to rapidly adapt to changing irradiance. Instead, this simulation shows these variations in performance to be minimal compared to implementations that typically suffer from steady state oscillations. The control system architecture provides an effective method by which to supply the power in the presence of constantly shifting environmental conditions.

Discussion

In this study, the simulation and evaluation of a photovoltaic (PV) system with the combination of Maximum Power Point Tracking (MPPT) algorithm, especially it is the Perturb and Observe (P&O) method, is described. The photovoltaic (PV) system is modeled with Simulink to a high level of accuracy. It considers the dynamics of power generation at different irradiance conditions and its coupling with the grid and battery storage. The mathematical modeling, configuration of Simulink and design of algorithm, together with the mathematical modeling, are a strong foundation for the purpose of understanding how the system works. The P&O algorithm is formulated mathematically by iteratively perturbing the system’s operating voltage, and observing the resulting power output, in order to optimize voltage at the MPP. This is done by watching the power output.

The simulation finds that the PV system responds well to different irradiance conditions. The P&O algorithm captures this straightaway when the PWM output power increases in response to an increase in the amount of irradiance. This type of behavior by the system allows it to dynamically change the operating point in order to maximize power output. Moreover, these results indicate that the PV voltage and current transitions are smooth, indicating that large oscillations of the P&O method are not present. This suggests a well tuned maximum power point tracking (MPPT) controller to minimize the power losses caused by oscillatory behavior.

It becomes possible to see how the PV system interacts with the grid by examining the plots of grid power, PV power and load demand. Initially, the grid supplies a good deal of the load demand, but as irradiance is elevated the photovoltaic system comes online and a portion of the load begins to fall off the grid. This example shows that PV systems have the potential to reduce grid reliance during high solar insolation times. Furthermore, the current of the battery is plotted along with the plot of the excess photovoltaic energy, which uses it to charge the battery, providing better overall energy management by storing the extra power for later usage.

The P&O algorithm is a good choice for this application, because of its ease of use and its ability to monitor the MPP effectively. The results demonstrate that this is the case. Although P&O is known to exhibit the possibility of steady state oscillations about the MPP, the results shown here indicate that the algorithm implementation has been performed to minimise the amount of precessation that is produced. This demonstrates the value in performing exhaustive parametric analysis and design of the overall system to achieve both stable and efficient MPPT performance.

Conclusion

This study demonstrates the successful design, simulation and analysis of a photovoltaic (PV) system with Maximum power point tracking (MPPT) using the P&O algorithm. The mathematical modelling and the simulation based on Simulink make the system’s response to various environmental conditions very clear. Given the findings, it could be concluded that the P&O algorithm provides the best approach to maximize power while simultaneously reducing oscillation around the MPP, under varying irradiance. The integration of grid power and energy storage also increases the system’s versatility and decreases dependency on the grid during times of peak solar. Given all that, the research illustrates that real world PV applications can benefit from use of simple and cheap MPPT algorithms, e.g. P&O, and identifies where the improvements can be made in the future.

Although this algorithm has been seen to perform well in this study, it is possible that it will not work so well when there is continual shifting of irradiance conditions. Future work could compare P&O with more advanced MPPT techniques, like incremental conductance (INC) or fuzzy logic-based methods. The aim in this comparison would be to compare the improvement obtained on tracking speed and accuracy. Furthermore, more advanced strategies for the management of the energy storage could enhance still further the autonomy and efficiency of the system. The results of this study provide important insights into the dynamic performance of photovoltaic (PV) systems that are controlled by maximum power point tracking (MPPT). These findings lay a solid foundation for future improvements in the integration of renewable energy sources and smart grid applications.