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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 %. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach that extracts the maximum power from photovoltaic (PV) system utilizing the Horse Herd Optimization (HHO) algorithm under different weather conditions, including fast changes in solar radiation (FCSR) and partial shading conditions (PSCs). The HHO algorithm is a technique for optimization that mimics the movement behavior exhibited by horses within a herd. The proposed MPPT controller has been tested and compared with other recognized metaheuristic algorithms, including the Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Flower Pollination (FP), Deterministic PSO (DPSO), and Cuckoo Search (CS). According to the simulation results, the proposed HHO-based MPPT method is found to outperform other considered metaheuristic methods in terms of the maximum power extraction, fast-tracking, and settling times under various weather conditions. Additionally, the suggested MPPT controller is robust and can continuously track the MPP under the FCSR. The performance of the proposed HHO controller is validated experimentally on a real PV system. It is demonstrated that the proposed HHO algorithm is robust and capable of outperforming the other counterpart metaheuristic algorithms as well as offering the highest MPPT efficiency of about 98.81 % with a rapid tracking time of 2.2 s. Furthermore, it has the lowest power oscillations of about 1.91 %, ensuring stable and consistent power output. [ABSTRACT FROM AUTHOR]