Input data of PV system
A collection of input data was collected and structured to precisely simulate and assess the PV system’s performance. These data contain all the key parameters that characterize system components, ambient conditions, and site-specific features. The primary input values for the analysis are presented in the (Table 2).
A PV system constructed in MATLAB/SIMULINK version: R2023a, running on AMD Ryzen 7 7435HS, 64-bit operating system, 8 GB RAM laptop. with varying irradiance and temperature levels, and control MPP via P&O MPPT technique with different Controllers. Used to power the PEM electrolyzer, which produces hydrogen. This section is structured into three main parts as follows:
Scenario-based assessment of PV and PEM systems
In the findings section, I evaluated the solar energy system and proton exchange membrane (PEM) electrolyzer under various operating circumstances. This dual method enabled me to assess each system’s capabilities, efficacy, and limitations in the face of technological and environmental changes. The primary situations I focused on are outlined below:
-
(1)
PV production under varying conditions.
-
(2)
PEM electrolyzer result under variable conditions.
-
(3)
PI controller behavior with three optimizers (DCSO-PSO-GWO).
-
(4)
FOPI controller behavior with three optimizers (DCSO-PSO-GWO).
-
(5)
Comparison of the Fuzzy logic controller with the previous two controllers.
-
(6)
Every scenario provided useful information regarding the behavior of these technologies, both individually and in combination, especially under real-world conditions. In the following parts, I will go over each situation in great depth, focusing on the approach, findings, and observations.
PV production under varying conditions
Constant irradiation and temperature
In this section, we examine the PV system’s voltage and power outputs under 1000 W/m2 radiation and a temperature of 25 °C, as illustrated in Fig. 8(a) and (b).
(a) PV output Voltage under 1000 W/m2 and 25 C, (b) PV output Power under 1000 W/m2 and 25 C.
These curves provide a baseline for performance comparisons, emphasizing the system’s responsiveness in steady-state environmental conditions.
Irradiance fluctuation and constant temperature
In this part, we look at the PV system’s voltage and power outputs at different irradiance levels [1000 W/m2, 800 W/m2, 650 W/m2, 500 W/m2] and a temperature of 25 °C, as shown in Fig. 9(a, b).
(a) PV output Voltage under variable irradiance and 25 °C, (b) PV output Power under variable irradiance and 25 °C.
It was observed from Fig. 9(a, b) that reduced radiation levels resulted in lower PV output power and voltage, which in turn caused a decline in the overall efficiency of the solar system.
Constant irradiance and variable temperature
In this section, we analyze the PV system’s voltage and power outputs at a fixed irradiation of 1000 W/m2 and a varied temperature of [5 °C, 25 °C, 55 °C, 80 °C, 100 °C] as shown in Fig. 10(a), (b).
(a) PV output Voltage under constant irradiance and variable temperature (°C), (b) PV output Power under constant irradiance and variable temperature (°C).
From Fig. 10(a, b), it was observed that as the temperature increased, both the PV output power and voltage decreased, leading to a reduction in the solar system’s efficiency.
Variable irradiance and temperature
In this part, we examine the voltage and power outputs of the PV system at a variable irradiation of [1000 W/m2, 800 W/m2, 650 W/m2, 500 W/m2] and a variable temperature of [5 °C, 25 °C, 55 °C, 80 °C, 100 °C], as shown in Fig. 11(a, b).
(a) PV output Voltage under variable irradiance(W/m2) and temperature (°C), (b) PV output Power under variable irradiance(W/m2) and temperature (°C).
Figure 11(a, b) shows that when the two prior examples are combined, changing both the radiation and the temperature, the PV output power and output voltage fall, resulting in a decrease in the solar system’s efficiency.
PEM electrolyzer results
Constant pressure and temperature
In this part, we analyzed the electrolyzer behavior under fixed pressure 1 atm and fixed temperature 25 °C and discovered that the quantity of hydrogen flow rate is equivalent to 22.32 L/min, and the electrolyzer efficiency is 67.45%, as shown in the following figures.
Polarization curve of PEM electrolyzer.
PEM electrolyzer input power versus current.
PEM electrolyzer hydrogen output flow rate versus current.
PEM electrolyzer hydrogen output flow rate versus input power.
PEM electrolyzer efficiency versus input power.
Figures 12, 13, 14, 15 and 16 clearly show a linear relationship between voltage and PEMEZ stack current under constant conditions. The slope of this curve can alter when the primary operational factors (pressure and temperature) are changed, as demonstrated in the next three sections. Figure 13 depicts the semi-linear relationship between the electrical power supplied to the PEMEZ and the current going across its membrane. Figures 14 and 15 show the semi-linear relationship between hydrogen generation rate and PEMEZ input power. Figure 16 depicts the fluctuation of the PEMEZ stack efficiency with the input power, as efficiency decreases with increasing power due to rising losses against the higher passing current across the membrane (Fig. 17).
PEMEZ results under variable temperature and constant pressure
In this part, we will analyze the behavior of PEMEZ fed from a solar system with a buck converter under variable temperature [35, 55, 65 °C] and a constant pressure equal to 1 atm, as shown in Fig. 12.
Polarization curve of PEMEZ (V-I) under variable temperature and constant pressure.
The amount of hydrogen created in each of the four preceding cases was analyzed and compared, as indicated in the (Table 3).
This portion illustrates how an increase in temperature generates a decrease in the slope of the polarization curve and shows that when the temperature increases, the amount of hydrogen flow rate increases.
Comparative analysis
In this section, the solar system’s findings and how the P&O MPPT technique was regulated using the FLC and PI, FOPI controllers were investigated. As indicated below, various algorithms were utilized to optimize PI and FOPI parameters.
PI controller behavior with three optimizations
In this part, the three methods were tested to ensure optimal tuning for the PI controller parameter. To provide a fair comparison of these algorithms, as shown in Tables 4 and 5, we ran all of them with the same population size, number of iterations, and boundary conditions. DCSO branch marking results when combined with other techniques.
Duty cycle performance using PI controller tuned by DCSO.
The convergence curve for PI tuning parameters using DCSO.
Power output of the PV cell with DCSO.
Convergence curve for PI tuning parameters using PSO.
Power output of PV cell with PSO.
The convergence curve for PI tuning parameters using GWO.
Power output of the PV cell with GWO.
From a close look at previous,, Figs. 18, 19, 20, 21, 22, 23 and 24; Table 5, it is clear that the best obtained results were for DCSO, PSO, and GWO respectively based on the fitness scale, where the best fitness was configured as the minimum value of the summation of square error between the actual output voltage from PV and reference voltage from P&O MPPT, Despite this, the optimal fitness values were near, and there was variability in terms of the time spent on the process of optimization.
FOPI controller behavior with three optimizations
This section evaluated the three methods to ensure optimal tuning of the FOPI controller parameters. To facilitate a fair comparison of these algorithms, as presented in Table 5, all procedures were executed with the same population size, number of iterations, and boundary conditions.
The convergence curve for FOPI tuning parameters using DCSO.
Power output of the PV system with DCSO.
The convergence curve for FOPI tuning parameters using PSO.
Power output of the PV system with PSO.
The convergence curve for FOPI tuning parameters using GWO.
Power output of the PV system with GWO.
It’s clear from the previous Figs. 25, 26, 27, 28, 29 and 30; Table 6 that the DCSO method produced results that were comparable when combined with other techniques. A closer examination of the table reveals that the best results, based on the fitness scale, were achieved by DCSO, PSO, and GWO, respectively. The fitness was defined as the minimum value of the summation of squared errors between the actual output voltage from the PV system and the reference voltage from the P&O MPPT. Although the optimal fitness values were similar, there were differences in the time required for the optimization process.
Three controllers: FLC, PIC, and FOPI behaviors
Power output of the PV system with FLC.
Table 7 The result of the fuzzy logic controller in comparison with other PI and FOPI controllers.
Figure 31 depicts the time-domain response of PV output power when the FLC is utilized. The major goal of this image is to show how the controller achieves its final working value. As illustrated, the FLC successfully drives the system to a power output of 6296 W. Although the response is smooth and free of huge, unexpected leaps, it takes longer to achieve the final value than DCSO-tuned controllers. This figure provides visible confirmation of the FLC’s ability in maintaining a continuous power supply to the PEM electrolyzer, despite its reduced efficiency in extracting the maximum possible power.
A comparison was made between the Fuzzy Logic Controller (FLC), the PI controller optimized using several algorithms (GWO, PSO, and DCSO), and the Fractional-Order PI (FOPI) controller utilizing the same optimization suite. As shown in Fig. 31, the FLC’s tracking capabilities was significantly limited, as it failed to accurately achieve or hold the Maximum Power Point (MPP) in the evaluated conditions. This performance disparity is related to the FLC’s fixed membership functions, which may not adjust quickly to fast irradiance changes. In contrast, the optimized PI and FOPI controllers displayed improved tracking accuracy, effectively attaining the MPP with low error, which validates the use of metaheuristic algorithms for controller tuning in PV systems. MPPT control is a technique for tracking the maximum power point under the impact of radiation 1000 W/m2 and temperature 25°C while feeding a PEM electrolyzer. Based on the results in Table 7, a thorough comparison of the three controllers indicates unique performance trade-offs. The PI-DCSO controller obtained the greatest peak power extraction of 6987 W, successfully maximizing the PV system’s capacity. This supremacy in power tracking is due to differential creative search Optimization (DCSO), which precisely optimized the Kp and Ki gains to match the operating point with the MPP. However, in terms of temporal reaction, the FOPI-DCSO had the quickest settling time (0.144 s), which was much faster than the normal PI-DCSO (0.432 s).The fractional-order operators (λ and µ) give more degrees of freedom, allowing the controller to efficiently suppress transients, resulting in this improvement. On the other hand, the FLC delivered a balanced performance but struggled to match the DCSO-tuned controllers’ steady-state accuracy, resulting in the lowest power output (6296 W). The high accuracy reported here, particularly with the PI-DCSO, outperforms the findings in Ref.4, confirming that combining advanced metaheuristic optimization with robust modeling significantly reduces the ‘chattering’ effect and improves the overall efficiency of the PV-PEM hydrogen production system. In further work, we propose to examine the performance of P&O MPPT with adaptive FLC and a hybrid fuzzy _PI controller. The current results indicate higher accuracy due to the utilization of advanced modeling approaches in comparison to the results presented in the Ref.4.
Comparison of three DC/DC converters
This section compares the three types of DC-DC converters: buck, boost, and buck-boost converters, have component values as shown in Table 8, in terms of studying their effect on PV output power and PV efficiency, as well as their effect on electrolyzer efficiency and the amount of produced hydrogen. Every converter topology’s steady-state analysis determines the design of the reactive components (L and C). The main design parameters are the inductor current ripple (∆IL) and the output voltage ripple (∆Vout)39.
Determine the best proportional-integral (Kp, Ki) gains for minimizing the system error. By reducing the selected fitness function (Integral Absolute Error – IAE), the improved PI controller may dynamically modify the duty cycle (D) to guarantee a rapid and steady response, maintaining the output voltage at the intended setpoint despite variations in solar irradiation.
PV output Power under 1000 W/m2 and 25 °C using different converters.
Different converter output voltages under 1000 W/m2 and 25 °C.
PEM electrolyzer hydrogen flow rate versus input power with different converters.
PEM electrolyzer polarization curve under different converters.
The comparative study shown in Figs. 32, 33, 34 and 35, as well as Table 9, demonstrates a key trade-off between hydrogen generation rate and PEM electrolyzer efficiency. While the Boost converter produced the maximum hydrogen flow rate (109.9 L/min), it did so at a considerable loss of electrolyzer efficiency (38.6%), most likely because to the high voltage stress, which increases internal ohmic losses. In contrast, the Buck converter provided the best performance for this integrated system, maintaining the maximum electrolyzer efficiency of 70.48% and a PV system efficiency of 34.97%. This suggests that the Buck topology offers a better impedance match between the PV source and the PEM stack. As a result, the Buck converter is recommended as the best interface for sustainable hydrogen generation, establishing a balance between energy harvesting and the lifespan of the electrolysis unit.
System performance limitations
The examination of Table 9 reveals that the PEM electrolyzer is the primary component limiting overall system performance. The PV array and DCSO-controller have good energy harvesting efficiency (~ 35% and 99.8% tracking, respectively), however the electrolyzer’s efficiency reduces to 38.6% with faulty voltage matching (Boost scenario). This demonstrates that the interface matching between the converter and the electrolyzer is the main barrier for hydrogen generation efficiency.
