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ORIGINAL RESEARCH ARTICLE
Efficient energy management in microgrid using Zebra Optimization Algorithm
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Department of Electrical Engineering, National Institute of Technology Andhra Pradesh, Tadepalligudem, Andhra Pradesh,
India
Submitted: 1 February 2025 | Accepted: 28 February 2025 | Published: 20 March 2025
(This article belongs to the Special Issue Renewable Energy Systems and Strategies in Smart Grids and Smart Cities Development)
© 2025 by the Author(s).. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Abstract
The inherent variability of power output from photovoltaic (PV) systems, wind energy resources, battery energy storage systems (BESS), and hydrogen (H₂) fuel cells presents a significant challenge in efficiently integrating these technologies into microgrids. This stochastic nature underscores the necessity of accounting for fluctuations in renewable energy resources (RERs) to optimize energy utilization within the microgrid. This paper proposes a resource-efficient energy management (REEM) framework for a microgrid interconnected with the main power system. By dynamically regulating PV generation, wind power output, BESS discharge, and hydrogen fuel cell operation in response to load variations, the proposed approach enhances energy utilization and grid stability. To address the complexities associated with REEM, this study employs the zebra optimization algorithm (ZOA), a highly efficient metaheuristic technique. The primary objectives of this optimization include cost minimization, voltage profile enhancement, and optimal sizing of RERs. Simulation results demonstrate that the strategic integration of PV units, wind turbines, grid-connected BESS, and hydrogen fuel cells significantly reduces operational costs while improving overall system performance. Comparative analysis further reveals that ZOA outperforms the moth-flame optimization algorithm and stochastic fractal search network in achieving the defined optimization objectives.
Keywords
Energy resources
Photovoltaic system
Wind energy
Zebra Optimization Algorithm
Voltage stability
Cost function
Funding
None.
Conflict of interest
The authors declare that they have no competing interest.
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