# Performance Analysis of a Stand-Alone PV/WT/Biomass/Bat System in Alrashda Village in Egypt
## 6 Results and Discussion
and mean of the target function, whereas the non-parametric measurements contain the median, relative error (RA), mean absolute error (MAE), standard deviation (SD), and efficiency. The efficiency here referred to the ratio of the lower value to the mean value of the goal function. For all four system scenarios, the results for statistical metrics for HBO, CFA, GWO, and STOA are shown in Table 10. On the basis of the results obtained, the proposed HBO in each case proved the best sensitivity and stability results compared to other optimization methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **HBO** & **CFA** & **GWO** & **STOA** \\ \hline \multicolumn{5}{c}{Case \(1\)} \\ \hline Max. & 0.0644471 & 0.0644714 & 0.0648796 & 0.0657149 \\ Min. & 0.0643767 & 0.0643778 & 0.0643857 & 0.0644027 \\ Mean & 0.0  
Figure 10, illustrate the sensitivity analysis of studying the impact of the variation of the decision parameters on the stand-alone system objective functions, (a) COE, (b) NPC, (c) LPSP, (d) EXP. Where \"0\" on the x-axis refers to the nominal values of the sensitivity factors. Figure 10, _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline  & **HBO** & **CFA** & **GWO** & **STOA** \\ \hline  & \multicolumn{3}{c}{Case 4} \\ \hline Max. & 0.151983478 & 0.15198479 & 0.1520216 & 0.151993102 \\ Min. & 0.151991724 & 0.152001799 & 0.154319327 & 0.153088601 \\ Mean & 0.151985107 & 0.151991986 & 0.15277566 & 0.152047201 \\ Median & 0.15198463 & 0.151991439 & 0.152656294 & 0.152014582 \\ SD & 0.000194530 & 0.000389174 & 0.057991385 & 0.015732186 \\ RE & 0.000535826 & 0.002367153 & 0.248002849 & 0.01779644 \\ MAE & 0.000001629 & 0.000007195 & 0.000754036 & 0.000054099 \\ RMSE & 0.000002522 & 0.000008162 & 0.000947705 & 0.000164869 \\ Efficiency & 99.99893 & 99.99527 & 99.50784 & 99.96452 \\ \hline \end{tabular}
\end{table} 
Table 10: _Cont._Figure 10: _Cont._  
Figure 10A,B illustrate the effect on the COE and the NPC. As it can be noted that, at lower values of the specified parameters, both COE and NPC drop when the number of each PV panels, biomass generators, and batteries decreased. While, at a higher parameter values, the COE and NPC raise with increasing the number of each PV panels, biomass generators, and batteries. For the number of wind turbines, it can be noted that both the COE and NPC are nearly constant with the variation of the wind turbines number. Figure 10C,D indicates that the chosen parameters has an effect on the system parameters, especially the number of the biomass generators. Table 11, illustrate the yearly expenses breakdown of the hybrid system units and in turns show the system's NPC. The reader can notice that, for all suggested system cases the battery storage system has the highest yearly sharing of the capital cost compared to other system units. While the Biomass system has the highest operating and maintenance cost compared with other generating units in the suggested hybrid power system. Figure 10: Sensitivity analysis of studying the influence of sizing parameters variation on the stand-alone system variables, (a) COE, (b) NPC, (c) LPSP, (d) EXP. (**A**) The influence of sizing change on the energy cost. (**B**) The influence of sizing change on the NPC. (**C**) The influence of sizing variation on the LPSP. (**D**) The influence of sizing variation on an excess of energy.