Optimal sizing of standalone rural microgrid for sustainable electrification with renewable energy resources

Renewable energy is widely used in the cost-effective electrification of rural communities. This article discusses a better electricity option for the residents of Uttarakhand's (India) rural areas. The proposed technology is utilized to create a standalone microgrid that reliably fulfills the area's energy demands at a low cost of energy. The suggested system focuses on the hybrid energy model's optimal sizing and sensitivity analysis. The integrated model uses locally accessible energy resources such as solar photovoltaic, micro-hydropower, biogas, battery, biomass, and wind energy systems to meet the area's overall electrical energy requirement. In the simulation, the proposed energy framework considers the intermittent nature of renewable resources. The proposed microgrid model is optimized by utilizing differential evolution (DE) to reduce the overall cost of energy and sizing of the system. The effectiveness of DE is tested for optimization problems, and its results are compared against particle swarm optimization and genetic algorithms. Sensitivity analysis of the cost of energy and the total net present cost demonstrates the impact of modifying the input parameters. The simulation findings reveal that DE opti-mizes the system compared to its contemporaries, PSO and GA. According to the findings, the optimal config-uration has a total net present cost of $ 7,12,532.00, and a cost of energy is 0.14$/kWh. The suggested model for the electrification of the rural community is quite effective in optimizing the system's cost and energy, and sizing.

Automated summary

This article discusses a better electricity option for rural areas in Uttarakhand, India, using renewable energy to create a standalone microgrid that reliably meets the area's energy demands at a low cost. The proposed model optimizes the hybrid energy system's sizing and sensitivity through the use of locally accessible renewable resources. The simulation findings demonstrate that the differential evolution optimization approach is more effective than other methods, resulting in a cost-effective and efficient system.