###### Abstract  
Several families in India live in remote places with no access to grid-connected power supply due to their remoteness. The study area chosen from the Indian state of Odisha does not have an electrical power supply due to its distant location. As a result, this study analyzed the electrification process using Renewable Energy (RE) resources available in the locality. However, these RE resources are limited by their dependency on weather conditions and time. So, a robust battery storage system is needed for a continuous power supply. Hence, the Nickel Iron (Ni-Fe), Lithium-Ion (Li-Ion) and Lead Acid (LA) battery technologies have been analyzed to identify a battery technology that is both technologically and economically viable. Using the available RE resources in the study area, such as photovoltaic and biomass energy resources, as well as the various battery technologies, three configurations have been modelled, such as Photovoltaic Panels (PVP)/Biomass Generator(BIOMG)/BATTERY(Ni-Fe), PV/BIOMG/BATTERY(Li-Ion) and PVP/BMG/BATTERY(Li). These three configurations have been examined using nine prominent metaheuristic algorithms, in which the PVP/BIOMG/BATTERY(Ni-Fe) configuration provided the optimal Life Cycle Cost value of 367,586 USD. Among the all metaheuristic algorithms, the dynamic differential annealed optimization was given the best Life Cycle Cost values for all of the three configurations. optimization techniques; different batteries; off-grid microgrid; integrated renewable energy system +
Footnote †: journal: Applied Soft Matter  
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be used for irrigation, drinking water supply and hydropower generation. Wind energy has many applications, including power generation, water pumps and windmills. Some commercial and residential applications of battery energy storage systems are explained as follows. From a commercial point of view, battery energy storage systems are useful in the following applications such as peak shaving, emergency backup, microgrids, load shifting, grid services and renewable energy resource integration. From a residential standpoint, battery energy storage systems are useful in the following applications: storing excess energy produced by the solar PV panels, emergency backup and off-grid applications. Numerous studies on the sizing of microgrids have been published in reputed journals. These approaches can be classified into three broad categories, which are categorized as follows: (i) software tools such as IHOGA, RESTcreen, HOMER and HOGA, [2] (ii) deterministic methodologies such as linear programming, numerical, iterative, graphical design, analytical and probabilistic methods [3] and (iii) metaheuristic algorithms such as Grasshopper Optimization Algorithm (GOA), Moth Flame Optimization (MFO), Genetic Algorithm (GA) and Dragonfly Algorithm (DA). Nevertheless, while software tools are simple to use the relevant components inside them cannot be picked. Furthermore, its algorithms and calculations are hidden from users. When it comes to estimating the size of a microgrid, software tools are constrained by certain assumptions. Deterministic approaches, on the other hand, outperform the software tools but the solutions are trapped at the local optima due to its complex design. They are unable to discover the global best optimal values under such conditions. It should be run several times with random beginning conditions to prevent the local optima trapping. Hence, the solution is unlikely to be the global best optimum value. Obtaining the global best optimal solution may necessitate multiple efforts. Finally, metaheuristic algorithms were chosen as the most promising methods, and they are widely used for optimization problems [4]. Last decade, metaheuristic algorithms were built to manage microgrid sizing issues. Surprisingly, certain algorithms, such as Differential Evolutionary Algorithm (DE), Particle Swarm Optimization (PSO) [5] and GA, are well-known among most software engineers as well as other researchers from other disciplines.