In current scenario, people tend to move towards outskirts and like to settle in places that are close to nature. But, due to urban lifestyle and to fulfill the basic needs, demand of electricity remains the same as in urban areas. This demand of electricity can be only fulfilled by using hybrid renewable energy resources, which is easily available in outskirts. Renewable energy resources are unreliable and more expensive. Researchers are working to make, it more reliable and economic in terms of utilization. This article proposes a metaheuristic grasshopper optimization algorithm (GOA) for the optimal sizing of hybrid PV/wind/battery energy system located in remote areas. The proposed algorithm finds the optimal sizing and configuration of remote village load demand that includes house electricity and agriculture. The optimization problem is solved by minimization of total system cost at a desirable level of loss of power supply’s reliability index (LPSRI). The results of GOA are compared with particle swarm optimization (PSO), genetic algorithm (GA) and hybrid optimization of multiple energy resources (HOMER) software. In addition, results are also validated by modeling and simulation of the hybrid energy system and its configurations at different weather conditions-based results. Hybrid PV/wind/battery is found as an optimal system at remote areas and sizing are[Formula: see text] with cost of energy (COE) (0.3473$/kWh) and loss of power supplies reliability index (LPSRI) (0%). It is clear from the results that GOA based methods are more efficient for selection of optimal energy system configuration as compared to others algorithms.

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This paper unveils the simulation and optimum design of five models based on photovoltaic (PV), diesel generator (DG), and battery energy systems for rural area farmhouse load demand (agriculture and household). The choice for designing an optimal energy system is constructed on minimum technical considerations like loss of load probability (LOLP), economic (cost of electricity [COE] with net present cost [NPC]), environmental (CO2 aspects) using a novel flamingo swarm intelligence algorithm (FSIA). The optimization results are compared by particle swarm intelligence algorithm, improved particle swarm intelligence algorithm, genetic algorithms, and hybrid optimization of multiple energy resources (HOMER‐Pro). FSIA shows PV/DG/battery energy system to be an optimal system as regards NPC (Rs. 8 056 734), COE (12.14 Rs./kWh), CO2 (13 784 kg/y), and LOLP (0.00000501 per year). The saving of the fuel cost and CO2 emission from PV/DG/battery to DG only system is 66.10% and 61.26%, respectively.

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