## 3 Simulation Results and Discussion  
The Fuzzy and Jellyfish Optimization-controlled solar PV system was created and simulated in MATLAB. The overall Simulink model of the Fuzzy and Jellyfish Optimization-controlled solar PV battery system is shown in Figure 14.  
The gain tuning of the PI controller convergence graph with Jellyfish Optimization, PSO, and GA is shown in Figure 15.  
Figure 13: Ocean current force and group motion types are randomly determined by a random function [20]. The Jellyfish Optimization Algorithm reaches global fitness value after 17 iterations, but PSO reaches it after 25 iterations and GA after 35 iterations. The value obtained from the Jellyfish Optimization Algorithm is 0.022, but the global point for PSO is 0.025, and for GA, it is 0.035. Figure 16 shows the solar PV battery system's load voltage response using Jellyfish Optimization, PSO, and GA.  
Figure 14: Fuzzy and Jellyfish Optimization-controlled solar PV battery system. Figure 15: Convergence graph for JSAA, PSO, and GA algorithm. In Figure 16 the dotted line shows the unit step value. The load voltage response of a GA-optimized PI controller has more overrun and more oscillation. Jellyfish Optimization Algorithm-optimized PI controller has less overrun and faster settling than PSO- and GA-optimized PI controllers. Based on test results, the PI controller optimized by the Hawks Optimization Algorithm provides superior results. The solar PV battery system was tested for different operating systems, such as constant irradiance, varying irradiance, and varying load conditions. Figures 17-19 show the results for constant irradiance conditions (1000 W/m\({}^{2}\)). Figure 16: The load voltage response of the solar PV battery system with PSO, GA, and JSAA. Figure 17: Solar PV power, current, and voltage for constant irradiance. The voltage of the PV panel is 17.4 volts, the PV panel's power regeneration is 99.5 W, and the PV panel current is 5.71 A. Theoretically, the maximum power is 100 W at 1000 W/m\({}^{2}\). Still, fuzzy logic MPPT extracts 99.5 W, and the maximum power ratio is 99.5%. The fuzzy logic MPPT extracts the optimum power from a PV panel. The battery voltage, power, and current are 13 Volts, \(-\)45.3 W, and \(-\)3.48 A, respectively. The battery is in charging mode in this condition. The load current and voltage are 2.08 A and 24 volts, whereas the load power is 50 W. The overall system efficiency is 95.3%. The performance under constant irradiance conditions is shown in Table 2.  
Figure 19: Load power, voltage, and current for constant irradiance. Figure 18: Current, battery power, and voltage for constant irradiance. Every 0.2 s, the irradiance of the PV panel changes from 1000 to 600 to 400 W/m\({}^{2}\). Figures 20-22 show the results of the system with varying irradiance. The fuzzy logic MPPT extracts the maximum power PV panel around 99.5 W at 1000 W/m\({}^{2}\), 59.9 W at 600 W/m\({}^{2}\), and 39.9 W at 400 W/m\({}^{2}\). The maximum power ratio is around 98 to 99.5% using fuzzy MPPT. The battery is in charging mode at 1000 and 600 W/m\({}^{2}\), and the battery is in discharge mode at 400 W/m\({}^{2}\) to meet the load demand. The system's overall efficiency improved from 94 to 95.3%. The performance under varying irradiance conditions has been shown in Figure 15.0  
The system's load varied every 0.2 s from 50 to 100 W and then 100 to 150 W. Figures 23-25 show the results of the system. Figure 15.1 Solar PV power, current, and voltage for varying load conditions. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Irradiance** & \multicolumn{3}{c}{**1000 W/m\({}^{2}\)**} & \multicolumn{3}{c}{**600 W/m\({}^{2}\)**} & \multicolumn{3}{c}{**400 W/m\({}^{2}\)**} \\ \hline
**Parameter** & **Voltage** & **Current** & **Power** & **Voltage** & **Current** & **Power** & **Voltage** & **Current** & **(A)** & **Power** \\  & **(V)** & **(A)** & **(  
The fuzzy logic MPPT extracts the maximum power PV panel around 99.5 W, and the maximum power ratio is around 99.5% using fuzzy MPPT.