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Providing electricity access to remote rural areas plays a crucial role in the socio-economic development of the rural community. The use of renewable energy sources (RES) for electrification of remote rural areas has been increased in the past two decades. The integrated renewable energy system (IRES), which embeds two or more RES, is paid great attention to satisfy the energy needs of the rural areas. In the present study, the IRES model is developed using solar, micro-hydro, biomass, biogas, and wind energy sources to meet the electricity demand of the seven un-electrified clusters of hamlets of Limkheda taluka in Dahod district of Gujarat state in India. The proposed IRES model is optimized for the lowest total net present cost (TNPC) and cost of energy (COE) of the system using artificial bee colony (ABC), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) algorithms. The optimal operational strategy has been developed to minimize the operating cost of the IRES and the optimum sizing of the system. An optimal power scheduling method with a linear programming problem (LPP) approach has been carried out in the MATLAB environment. The proposed power scheduling method effectively reduces the TNPC and COE of the IRES. Finally, the results recorded in the paper confirm that the proposed configuration together with the TLBO method offers the lowest TNPC of INR 9.5951 million and TAC of INR 1.1274 million at the estimated COE of INR 5.68 per kWh, which is comparable with respect to standardized ABC and GWO.

Development of Integrated Renewable Energy System Based on Optimal Operational Strategy and Sizing for an Un-Electrified Remote Area