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Electricity access is a critical factor for social and economic development. Microgrid becomes cost‐effective means to provide access to electricity for the rural community. Rural electrification demands high investment due to its low energy demand and population. Planning of rural electrification is a technical and financial challenge for policymakers. This paper proposes the grid‐connected electrification of the rural community of Uttarakhand (India). An integrated model formed with the solar photovoltaic system, wind energy, and battery. This research aims to provide the optimal cost‐effective solution for the electrification of the rural community. The suggested model is optimized using Differential Algorithm to determine the optimal system configuration, cost of energy, and net present cost. The effectiveness of the proposed DE algorithm is compared with genetic algorithm and particle swarm optimization. The results confirm that the proposed renewable energy‐based microgrid has a total production cost of $5 185 367.04, with a per‐unit cost of energy is $0.20/kWh. The proposed microgrid is a more suitable option for electrification in rural communities with less dependence on the grid and conventional sources. [ABSTRACT FROM AUTHOR]

In this paper, a novel liquefied air energy storage (LAES) system driven by wind energy and natural gas, integrated with a two‐stage supercritical carbon dioxide cycle is proposed and investigated. Three different sensible thermal energy storage (TES) systems are considered in this study to store the heat produced in the compressions stage and use it later to improve the performance. The proposed system is analyzed in terms of energy, exergy, exergo‐economic, and environmental impacts. A detailed economical and technical investigation is carried out to determine system hotspots, exergy destruction rates, and the performance of the system and subsystems. Finally, by considering ten design variables, the nondominated sorting genetic algorithm‐II (NSGA‐II) is employed to evaluate the optimal design solutions for double objective functions, including cost per unit of exergy and overall system exergy efficiency. Results indicate that for a 50 MW of discharging power, 80.75 MW of charging electricity and 1.22 kg/s of fuel are required. Additionally, the product cost per unit of exergy and the levelized costs are calculated as 32.02 $/GJ and 0.133 $/kWh, respectively. The results of the optimization study also show that for the optimal Pareto solution, the energy efficiency would be 52.2% while the exergy efficiency is 45.26%. [ABSTRACT FROM AUTHOR]