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Minimum hydrogen consumption based control strategy of fuel cell/ PV/battery/supercapacitor hybrid system using recent approach based parasitism-predation algorithm
Faculty
Engineering
Year:
2021
Type of Publication:
ZU Hosted
Pages:
Authors:
Ahmed Fathy Mohamed Ali Ali
Staff Zu Site
Abstract In Staff Site
Journal:
Energy Elsevier
Volume:
Keywords :
Minimum hydrogen consumption based control strategy
Abstract:
In hybrid renewable energy sources containing different storage devices like fuel cells, batteries, and supercapacitors, minimizing the hydrogen consumption is the main target for economic aspects and operation enhancement. External energy maximization strategy (EEMS) is the most popular energy management strategy used with hybrid renewable energy sources. However, gradient-based method is employed in EEMS which has low convergence, moreover it doesn’t guarantee the optimum solution. Therefore, this paper proposes for first time an energy management strategy based on recent metaheuristic optimizer of parasitism-predation algorithm employed in hybrid source comprises photovoltaic/ fuel cell/battery/supercapacitor for supplying aircraft in emergency state during landing. The main target is hydrogen consumption minimization, this helps in enhancing the power durability to the aircraft in case of curtailment of the main power source. The selection of parasitism-predation algorithm (PPA) is due to requirement of less parameters defined by the user and its high convergence ability. The proposed strategy is compared to other conventional and programmed approaches of state machine control, water cycle algorithm, dynamic differential annealed optimization, spotted hyena optimizer, EEMS, marine predator algorithm, and mayfly optimization algorithm. The obtained results confirmed the superiority of the proposed method achieving efficiency of 95.34% and minimum hydrogen consumption of 15.7559 gm.
Author Related Publications
Ahmed Fathy Mohamed Ali Ali, "Optimization of a PV fed water pumping system without storage based on teaching-learning-based optimization algorithm and artificial neural network", ELSEVIER, 2016
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Ahmed Fathy Mohamed Ali Ali, "A comparison of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions", Elsevier Ltd., 2017
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Ahmed Fathy Mohamed Ali Ali, "Grey Wolf Optimizer for Optimal Sizing and Siting of Energy Storage System in Electric Distribution Network", Taylor & Francis, 2017
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Ahmed Fathy Mohamed Ali Ali, "Parameter estimation of photovoltaic system using imperialist competitive algorithm", Elsevier Ltd., 2017
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Ahmed Fathy Mohamed Ali Ali, "A novel optimal parameters identification of triple-junction solar cell based on a recently meta-heuristic water cycle algorithm", Elsevier Ltd., 2017
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Attia Abdelaziz Hussien Ali, "Artificial ecosystem-based optimiser to electrically characterise PV generating systems under various operating conditions reinforced by experimental validations", Wiley, 2021
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