## 9 Results and Discussion  
In general, using the existing RE resources, rural distant communities can be electrified. However, the fundamental disadvantage of RE resources is their inherent nature, as well as their energy production depends on the metrological conditions in the chosen location. As a result of the RE resources' time and metrological dependence, the power supply to the study area is not continuous. As a result, while RE resources were used to electrify the study area, one of the key problems with these resources is that they cannot provide a continuous power supply. Hence, a reliable battery storage system is required to provide a continuous power supply. So, three different types of battery technologies have been proposed in this study to provide a continuous power supply in order to select a battery technology that is technologically and economically feasible for the study area. Finally, we have been modelled three different types of configurations employing available RE resources and battery technologies, such as PVP/BIOMG/BATTERY(Ni-Fe), PVP/BIOMG/BATTERY(Li-Ion) and PVP/BIOMG/BATTERY(LA) to Identify a configuration that is technologically and economically feasible to electrify the study area. PV panels and batteries are the variable parameters in all of the three configurations and their quantities can be determined using optimization algorithms based on the required load demand and constraints. However, the biomass generator is regarded as a fixed energy resource, with a rated power of 10 kW. Hence, no optimization algorithm is required to estimate its  
Figure 7: Modes of operation for the off-grid microgrid. size because it is already fixed as a 10 kW. The converter size is also considered as a fixed parameter because it is already predesigned to meet the peak load demands of the study area. Because the study area's peak load demand is 35.72 kW, the converter power rating is set to 40 kW. For safety reasons, the converter power rating is set to be 10% higher than the peak load demand. As a result, there is no need to use optimization algorithms to optimize the converter size. The three above-mentioned configurations have been optimized using well-known algorithms such as Differential Evolutionary Algorithm (DE) [10], Genetic Algorithm (GA) [11], Moth Flame Optimization (MFO) [12], Dragonfly Algorithm (DA) [13], Particle Swarm Optimization (PSO) [14], Ant Lion Optimization (ALO) [15], Grey Wolf Optimization (GWO) [16], Grasshopper Optimization Algorithm (GOA) [17] and Dynamic Differential Annealed Optimization Algorithm [18]. These algorithms that were run with their default control parameter values with the population and iterations are considered to be 100. The technical and cost values used in the study are listed in Tables 2 and 3. \begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline Project lifetime & 25 years & Erection cost of BIOMG [2] & 450.5 USD \\ \hline Nominal interest rate [2] & 13\% & Rated Power of BIOMG [6] & 10 kW \\ \hline Inflation rate [7] & 5\% & BIOMG Capital cost [6] & 9010 USD \\ \hline Manufacturer of PV Panel [19] & Vikram solar & AO\&M cost of BIOMG [2] & \$271 \\ \hline Model No. of PV Panel [19] & Somera 385 & Quantity of biomass & 19 tons/year \\ \hline Rated power of PV Panel [19] & 385 Wp & Cost of biomass [2] & 15 USD/ton \\ \hline Lifetime of PV Panel [8] & 25 years & Efficiency of BIOMG [2] & 20\% \\ \hline Capital cost of PV Panel [8] & 128 USD & Rated power of BDC-CC & 40 kW \\ \hline AO\&M cost of PV Panel [2] & 3.2 USD & Lifetime of converter [2] & 10 years \\ \hline Mechanical structure cost of PV Panel [20] & 41 USD & C\&R of converter [2] & 4320 USD \\ \hline Life time of mechanical structure of PV & 25 years & AO\&M cost of converter [2] & 108 USD \\ \hline  & & & \\ \hline  & & & \\ \hline  & & & \\ \end{tabular}
\end{table} 
Table 2: The study’s technical and financial values.