Optimal Sizing of Hybrid Renewable Energy System using Manta Ray Foraging Technique
Priyanka Brahamne1, M. P. S. Chawla2, H. K Verma3
1Priyanka Brahamne, Department of Electrical Engineering, SGSITS, Indore (M.P), India.
2Assoc. Prof. M. P. S. Chawla, Department of Electrical Engineering, SGSITS, Indore (M.P), India.
3Dr. H. K Verma, Department of Electrical Engineering, SGSITS, Indore (M.P), India.
Manuscript received on 18 January 2023 | Revised Manuscript received on 20 January 2023 | Manuscript Accepted on 15 February 2023 | Manuscript published on 28 February 2023 | PP: 8-16 | Volume-11 Issue-3, February 2023 | Retrieval Number: 100.1/ijese.C25450211323 | DOI: 10.35940/ijese.C2545.0211323
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, a method for optimizing the size of a standalone hybrid that consists of a wind, PV, and biomass energy system with battery storage is discussed. Hybrid renewable energy systems are required in off-the-grid communities. For such systems, the optimal system sizing can be regarded as one of the constrained optimization issues. This research presents an intelligent approach based on modern optimization for designing the hybrid renewable energy system optimally using the manta ray foraging technique, minimizing overall annualized system cost and satisfying load demand. In order to confirm the effectiveness of the proposed method, results are compared against findings from the ABC algorithm. The results have proven that the MRFO algorithm has fast convergence properties, the ability to deliver high-quality results, and the capacity to manage a smooth power flow under the same ideal conditions.
Keywords: Renewable Energy, System, MRFO, Battery Storage, ABC Algorithm, Optimization
Scope of the Article: Renewable Energy Technology