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Cost-effective hybrid renewable energy strategies for rural Electrification: Optimization-based evaluation of grid-connected and Islanded microgrid systems

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  • Wang, Shuaijie
  • Guan, Xin
  • Liu, Shu

Abstract

Although many rural areas in India are electrified, a significant gap remains between the demand for electricity and its supply, driven by rapid economic expansion and higher living standards. To address this, integrating renewable energy sources through microgrid systems is crucial. This study explores the effectiveness of optimization algorithms in designing cost-efficient, sustainable energy solutions for rural electrification. Specifically, four algorithms (Tree Physiology Optimization (TPO), Invasive Weed Optimization (IWO), Biogeography-Based Optimization (BBO), and Seagull Optimization Algorithm (SOA)) are used to design a hybrid renewable energy system (HRES) that minimizes Levelized Cost of Energy (LCOE) and Net Present Cost (NPC), while meeting fluctuating energy demands in rural areas. The results show that the SOA approach outperforms the others, achieving the lowest LCOE and NPC in grid-connected and islanded configurations. A techno-economic study of a photovoltaic, wind turbine, digestion plant, storage, and converter system reveals an NPC of 1,355,437$ for the grid-connected scenario and 1,821,323$ for the islanded scenario. The LCOE for these configurations is 0.076 $/kWh and 0.139 $/kWh, respectively. This research demonstrates that the proposed HRES offers a financially viable and sustainable solution for rural electrification, providing economic advantages and serving as a model for similar regions facing energy challenges.

Suggested Citation

  • Wang, Shuaijie & Guan, Xin & Liu, Shu, 2025. "Cost-effective hybrid renewable energy strategies for rural Electrification: Optimization-based evaluation of grid-connected and Islanded microgrid systems," Renewable Energy, Elsevier, vol. 249(C).
    • Handle: RePEc:eee:renene:v:249:y:2025:i:c:s096014812500936x
    DOI: 10.1016/j.renene.2025.123274
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