###### Abstract
They are adaptable techniques and offer better results than deterministic methods by preventing the local optima entanglement. These algorithms offer a range of properties that allow them to tackle any optimization problem and they solve optimization problems by imitating natural processes. On the other hand, the No-Free-Lunch theorem states that while a specific metaheuristic algorithm can produce optimal results for a specific objective function, the same algorithm produces lower performance in other objective functions. For such reasons, researchers working on microgrid sizing issues have looked at robust metaheuristic algorithms [6]. A review of the literature on off-grid microgrid size issues revealed that evaluating the techno-economic feasibility analysis with various battery technologies to deliver an uninterrupted electrical supply is quite limited. Moreover, researchers can determine a specific algorithm's robustness and convergence efficiency using various algorithms. However, such studies are limited. The limitations stated above should be addressed while analyzing perfect off-grid rural electrification from a techno-economic perspective. So, taking these limitations into account, some research has been undertaken to supply power to four un-electrified villages in the Indian state of Odisha. After conducting some research in the study area, it was discovered that the accessible RE resources in the study area are solar and biomass. Solar energy is the most volatile of the two RE resources due to its time-dependent nature and weather conditions. So, the power supply is not continuous. Hence, a robust battery system is essential to ensure a continuous electrical supply. Therefore, in this study, a detailed analysis with three v kinds of batteries, such as Ni-Fe, Li-Ion and LA, has been carried out. Three types of configurations are modelled using available RE resources and battery technologies to identify the most viable configuration for electrifying the study region, such as Photovoltaic Panels (PVP)/ Biomass Generator (BIOMG)//BATTERY(Ni-Fe), PVP/BIOMG/BATTERY(Li-Ion) and PVP/BIOMG/BATTERY(LA). In order to find out a feasible configuration to electrify the study area, nine proven metaheuristic algorithms have been used in the study, such as Differential Evolutionary Algorithm (DE) [5], Genetic Algorithm (GA) [6], Moth Flame Optimization (MFO) [7], Dragonfly Algorithm (DA) [8], Particle Swarm Optimization (PSO) [9], Ant Lion Optimization (ALO) [10], Grey Wolf Optimization (GWO) [11], Grasshopper Optimization Algorithm (GOA) [12] and Dynamic Differential Annealed Optimization Algorithm [13]. Finally, after identifying the optimal configuration, it is evaluated with various loss of power supply probability values. The novelty in this work can be summarized as:  
* A renewable energy-based plan for electrification of an un-electrified village in a remote location. * Identification of a suitable optimization algorithm amongst nine prominent ones in the literature for energy management. * Exploration of the most cost-effective and technically suitable battery storage technology for the given setup. * Identify the best configuration cost wise amongst three different possibilities.