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: This paper presents a method for optimising the size of a standalone hybrid energy system that combines wind, PV, and biomass energy sources with battery storage. Hybrid renewable energy systems are required in off-the-grid communities. For such systems, optimal system sizing can be regarded as a constrained optimisation issue. This research presents an intelligent approach based on modern optimisation for designing a hybrid renewable energy system optimally, utilising the manta ray foraging technique to minimise the overall annualised system cost and satisfy load demand. To confirm the effectiveness of the proposed method, the results are compared with those from the ABC algorithm. The results have demonstrated that the MRFO algorithm exhibits fast convergence properties, delivers highquality results, and maintains 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