## 8 Conclusions  
The optimal design for electrifying the study area has been determined by modeling six different configurations that leveraged renewable energy resources and battery technologies. These configurations include PV/BMG/LA at 70% depth of discharge (DOD), PV/BMG/LA at 80% DOD, PV/BMG/Li-Ion at 50% DOD, PV/BMG/Li-Ion at 70% DOD, PV/BMG/Li-Ion at 80% DOD, and PV/BMG/Ni-Fe at 80% DOD. The analysis of these configurations is necessary to identify the most cost-effective and efficient solution for the study area. Nine metaheuristic algorithms, such as particle swarm optimization, grey wolf optimization, genetic algorithm, ant lion optimization, differential evolutionary algorithm, moth flame optimization, dragonfly algorithm, grasshopper optimization algorithm, and salp swarm algorithms, were assessed in a MATLAB environment under a scenario of low-efficiency appliances usage-based scenario. Our research showed that the salp swarm algorithm is an efficient and reliable one for finding the global best optimal values. Our study revealed that this algorithm is capable of providing effective solutions quickly and accurately. The Ni-Fe-battery-based IRES has been found to be an optimal configuration with a low-efficiency-appliance usage-based scenario, which was further evaluated with both medium and high-efficiency-appliance usage-based scenarios. The results indicate that the Ni-Fe-battery-based IRES is the optimal choice in all scenarios. The summary of these results is listed as follows:  
* The Ni-Fe-battery-based IRES with low-efficiency-appliance usage-based scenario obtained a life-cycle cost (LCC) of $951,257, which was 40% and 65% lower than the LA (@80% DOD) and lithium-ion (@50% DOD) battery-based IRESs LCCs, respectively; \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Configuration**} & **Quantity** & \multirow{2}{*}{**LPSP (0\%)**} & \multirow{2}{*}{**LPSP (1\%)**} & \multirow{2}{*}{**LPSP (2\%)**} & \multirow{2}{*}{**LPSP (3\%)**} & \multirow{2}{*}{**LPSP (4\%)**} & \multirow{2}{*}{**LPSP (5\%)**} \\  & **and Cost** & & & & & \\ \hline \multirow{2}{*}{PV/  BMG/  Ni-Fe  @DOD = 80\%} & N\({}_{\text{PV}}\) & 852 & **737** & 700 & 679 & 662 & 647 \\ \cline{2-8}  & N\({}_{\text{BAT}}\) & 496 & **452** & 438 & 424 & 413 & 400 \\ \cline{2-8}  & LCC (\$) & 530,603 & **476,846** & 459,639 & 446,445 & 435,953 & 424,829 \\ \cline{2-8}  & LPSH & 0 & **173** & 310 & 467 & 579 & 649 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of the optimal configuration at different LPSP values. Figure 14: Variability in LCC and related components at various LPSP values. * The Ni-Fe-battery-based IRES with medium-efficiency appliance usage-based scenario, i.e., with demand side management (DSM), yielded an LCC of $635,271, which was 33% lower than its LCC when using the low-efficiency appliance usage-based scenario;
* The Ni-Fe-battery-based IRES system with the integration of high-efficiency appliance usage-based scenario, i.e., with DSM, led to a significant decrease in the LCC by 44% and 17% when compared to the scenarios based on low and medium-efficiency appliance usage-based scenarios, respectively, to $530,603;
* This study determined that the Ni-Fe-battery-based IRES with DSM was the most cost-effective and efficient system for the study area. Future research should explore other renewable energy sources, control strategies, and innovative energy storage technologies in order to optimize the design of renewable energy-based systems. The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/su151310137/s1](https://www.mdpi.com/article/10.3390/su151310137/s1)]([https://www.mdpi.com/article/10.3390/su151310137/s1](https://www.mdpi.com/article/10.3390/su151310137/s1)). Conceptualization, P.P.K. and R.S.S.N. ; methodology, S.A.S. and M.F.I. ; software, P.P.K. and R.S.S.N. ; validation, S.M.M. and G.M.S. ; formal analysis, M.F.I. ; investigation, S.A.S., S.M.M. and A.R. ; resources, S.A.S., A.R. ; data curation, S.A.S., M.F.I., G.M.S.