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Techno-economic modeling and optimal sizing of autonomous hybrid microgrid renewable energy system for rural electrification sustainability using HOMER and grasshopper optimization algorithm

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  • Araoye, Timothy Oluwaseun
  • Ashigwuike, Evans Chinemezu
  • Mbunwe, Muncho Josephine
  • Bakinson, Oladipupo Idris
  • Ozue, ThankGod Izuchukwu

Abstract

This research paper focuses on techno-economic modeling and optimal sizing of autonomous hybrid microgrid systems. The optimal configuration of the suggested stand-alone system was performed by developing a size optimization model based on the metaheuristic novel Grasshopper Optimization algorithm (GOA) method to minimize the Total Net Present Cost (TNPC), unmet load, and Cost of Energy (COE) in the Nsukka Community which comprises 88 villages. The GOA and HOMER Pro Software are employed to compare results across four possible configurations of hybrid renewable power systems (HRES). The comparative analysis between GOA and HOMER shows that configuration-4 (biogas/Diesel), emerges as the optimal solution, with a 0 % unmet load at the COE of $0.01783 per kWh. The findings indicated that the GOA-based HRES, with a higher saturation of Biogas and photovoltaics (PV), proves to be more affordable in comparison to the HOMER-based solutions. The reduction in the COE and NPC of renewable energy for peak demands highlights the growing importance of biogas generators as an affordable local power supply to meet energy demands. This underscores the potential of the GOA in optimizing hybrid renewable energy systems for remote communities, producing an economically viable and sustainable energy solution.

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  • Araoye, Timothy Oluwaseun & Ashigwuike, Evans Chinemezu & Mbunwe, Muncho Josephine & Bakinson, Oladipupo Idris & Ozue, ThankGod Izuchukwu, 2024. "Techno-economic modeling and optimal sizing of autonomous hybrid microgrid renewable energy system for rural electrification sustainability using HOMER and grasshopper optimization algorithm," Renewable Energy, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:renene:v:229:y:2024:i:c:s0960148124007808
    DOI: 10.1016/j.renene.2024.120712
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