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Designing an optimal hybrid microgrid system using a leader artificial rabbits optimization algorithm for domestic load in Guelmim city, Morocco

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  • Kharrich, Mohammed
  • Hassan, Mohamed H.
  • Kamel, Salah
  • Kim, Jonghoon

Abstract

This research endeavor strives to conceptualize a hybrid microgrid tailored for household consumption in Guelmim City, situated in the southern region of Morocco. In pursuit of this objective, a groundbreaking and enhanced algorithm, denoted as the Leader Artificial Rabbits Optimization Algorithm, is introduced. The efficacy of this innovative algorithm is initially validated by means of an exhaustive performance assessment involving a comprehensive comparison with the original version of the algorithm as well as other contemporary optimization methodologies. This comparative analysis encompasses the evaluation of the algorithms across 23 benchmark functions. The outcomes clearly exhibit that the developed algorithm outperforms both the original iteration and other optimization techniques in terms of both convergence speed and solution quality. Subsequent to this validation, the proposed algorithm is applied to the specific project at hand. The resulting outcomes are juxtaposed with those derived from other optimization strategies. This comparative evaluation serves to underscore the effectiveness of the suggested approach. The outcome of this comparison highlights a net present cost of $106,821, based on the microgrid configuration employing PV/wind/diesel/battery. Additionally, the cost of energy is estimated at approximately $0.11 per kWh, accompanied by a reliability rate of 5 % and an availability rate of 95 %.

Suggested Citation

  • Kharrich, Mohammed & Hassan, Mohamed H. & Kamel, Salah & Kim, Jonghoon, 2024. "Designing an optimal hybrid microgrid system using a leader artificial rabbits optimization algorithm for domestic load in Guelmim city, Morocco," Renewable Energy, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:renene:v:223:y:2024:i:c:s0960148124000764
    DOI: 10.1016/j.renene.2024.120011
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