Publications Scientifiques

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Author: search.filters.author.Fares, Dalila

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    Performance evaluation of metaheuristic techniques for optimal sizing of a stand-alone hybrid PV/wind/battery system
    (Elsevier, 2021) Fares, Dalila; Fathi, Mohamed; Mekhilef, Saad
    This study presents a performance evaluation of ten metaheuristics optimization techniques that are applied to solve the sizing problem for a stand-alone hybrid renewable energy system including a photovoltaic module, wind turbine, and a battery (PV/WT/Battery). The algorithms include genetic algorithm (GA), cuckoo search (CS), simulated annealing (SA), harmony search (HS), Jaya algorithm, firefly optimization algorithm (FA), flower pollination algorithm (FPA), moth flame optimization (MFO), brainstorm optimization in objective space (BSO-OS), and the simplified squirrel search algorithm (S-SSA). The optimization process aims to minimize the total net present cost (TNPC) of the system while maintaining the acceptable deficiency of power supply probability (DPSP). The levelized energy cost and the relative excess power generated criteria are also considered. The studied algorithms have been simulated for four DPSP values (0%, 0.3%, 1%, and 5%), each for 50 independent runs. Based on the simulation results, FPA and SA demonstrated high robustness and accuracy with zero standard deviation and a 0% increase in the TNPC values compared to the optimal solutions. The FAO showed the best performance in terms of execution time with an average of 6.32 s, followed by BSO-OS (6.36 s) and SA (7.84 s). The SA has the best compromise between robustness, accuracy, and rapidity, and is found to be the best option to solve the sizing problem. The FPA is the most advantageous in case the execution time is not crucial for the optimization. Our findings will be a good reference for researchers to select the best technique for the sizing problem
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    A novel global MPPT technique based on squirrel search algorithm for PV module under partial shading conditions
    (Elsevier, 2021) Fares, Dalila; Fathi, Mohamed; Shams, Immad; Mekhilef, Saad
    The partial shading condition (PSC) makes it challenging for the PV system to harvest maximum power via maximum power point tracking (MPPT). Various MPPT algorithms based on bio-inspired optimization methods were proposed in the literature. The mechanism employed by these algorithms varies from one to another, making them perform differently when tracking the GMPP. This paper introduces a novel MPPT technique based on the improved squirrel search algorithm (ISSA). The performance of the proposed ISSA improved the tracking time by 50% in comparison with the conventional SSA algorithm. Similarly, the proposed method has also been compared with popular Genetic algorithm (GA), and particle swarm optimization (PSO). The results proved the ability of the proposed algorithm in tracking the GMPP with faster convergence and fewer power oscillations in comparison. The feasibility and effectiveness of the proposed ISSA based MPPT have been validated experimentally, and the results clearly demonstrate its capability in tracking the GMPP with an average efficiency of 99.48% and average tracking time of 0.66 s.
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    Comparison of Two Hybrid Global Maximum Power Point Algorithms for Photovoltaic Module under Both Uniform and Partial Shading Condition
    (IEEE, 2020) Fares, Dalila; Fathi, Mohamed; Mekhilef, Saad
    Power vs. Voltage (P-V) characteristics of a photovoltaic module (PV) show multiple peaks under partial shading conditions (PSCs). Most conventional maximum power point tracking (MPPT) techniques can accurately locate the single point under uniform conditions but fail under PSCs. Intelligent algorithms can locate the global point (GMPP) among the local ones (LP) but incur more computational cost. Combining both types as hybrid GMPPT provides more effective performance under different environmental conditions. This paper aims to analyze and compare the performance of two hybrid GMPP techniques under both uniform conditions and partial shading. In the proposed approach, the genetic algorithm (GA) and particle swarm optimization (PSO) are integrated with the perturb and observe algorithm (P&O). The simulation results in Matlab/Simulink confirm that both hybrid algorithms can track the GMPP. Furthermore, they show the ability to differentiate between different environment changes occurrences