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Impartial near-optimal control and sizing for battery hybrid energy system balance via grey wolf optimizers: Lead acid and lithium-ion technologies
Faculty
Engineering
Year:
2022
Type of Publication:
ZU Hosted
Pages:
Authors:
Hytham Saad Mohamed Ramadan
Staff Zu Site
Abstract In Staff Site
Journal:
IET Renewable Power Generation Wiley-IET Renewable Power Generation
Volume:
Keywords :
Impartial near-optimal control , sizing , battery hybrid
Abstract:
The balance of renewable-energy-based power systems has witnessed significant importance particularly with their rapid integration within these systems. The optimal sizing and control of energy storage systems (ESS) in hybrid power systems (HPSs) based on renewable energy becomes of particular interest. In this research, the HPS under study comprises PV, wind, and energy storage system. Two battery technologies, lead acid (LA) and lithium-Ion (LI)—are conducted to reach a near-optimal solution via metaheuristic optimization algorithms in HPS. This paper aims at reaching the equilibrium of the generation consumption for HPS through applying a novel technique, grey wolf optimization (GWO) through the optimal battery sizing of the HPS. The optimization is used for reaching the due balance between the production of power and that absorbed by the load, by minimizing the difference between the final and initial state of charge. Based on numerical simulations, the two different battery technologies are considered in the sizing of the ESS using GWO approach. From the simulation results, the proposed GWO leads to more enhanced performance with LI rather than LA by 3.1% with reduced number of parallel/series cells (Np/Ns) of 240/3450 and 270/3500. Accordingly, the GWO provides an adequate dynamic controlled performance.
Author Related Publications
Hytham Saad Mohamed Ramadan, "Efficient and Sustainable Reconfiguration of Distribution Networks via Metaheuristic Optimization", IEEE, 2022
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Hytham Saad Mohamed Ramadan, "Efficient experimental energy management operating for FC/battery/SC vehicles via hybrid Artificial Neural Networks-Passivity Based Control", ELSEVIER, 2021
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Hytham Saad Mohamed Ramadan, "Hydrogen storage technologies for stationary and mobile applications: Review, analysis and perspectives", ELSEVIER, 2021
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Hytham Saad Mohamed Ramadan, "Efficient metaheuristic utopia-based multi-objective solutions of optimal battery-mix storage for microgrids", ELSEVIER, 2021
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Hytham Saad Mohamed Ramadan, "Optimal reconfiguration for vulnerable radial smart grids under uncertain operating conditions", ELSEVIER, 2021
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Department Related Publications
Raef Seam Sayed Ahmed, "Model predictive control algorithm for fault ride-through of stand-alone microgrid inverter", Elsevier Ltd., 2021
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Enas Ahmed Mohamed Abdelhay, "Recent Maximum Power Point Tracking Methods for Wind Energy Conversion System", Elsevier, 2024
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Raef Seam Sayed Ahmed, "Optimal design and analysis of DC–DC converter with maximum power controller for stand-alone PV system", Elsevier Ltd., 2021
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Raef Seam Sayed Ahmed, "Parameters identification and optimization of photovoltaic panels under real conditions using Lambert W-function", Elsevier Ltd., 2021
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Mohammed Abdelhamied Abdelnaeem , "Artificial ecosystem-based optimiser to electrically characterise PV generating systems under various operating conditions reinforced by experimental validations", Wiley, 2021
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