Multi-Objective Optimization of Hybrid Power System using Magnetotactic Bacteria Moment Migration Optimization Algorithm
K. Ranjith Kumar1, M. Surya Kalavathi2
1K. Ranjith Kumar, Assistant Professor, Electrical and Electronics Engineering Department, Vaagdevi College of Engineering, Warangal, India.
2M. Surya Kalavathi, Professor, Electrical and Electronics Engineering Department, Jawaharlal Nehru Technological University, Hyderabad, India.
Manuscript received on 11 August 2019. | Revised Manuscript received on 15 August 2019. | Manuscript published on 30 September 2019. | PP: 5133-5139 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5746098319/2019©BEIESP | DOI: 10.35940/ijrte.C5746.098319
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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: Alternative energy sources are more attractive now a day because of the vanishing of conventional sources in the next few decades, energy crisis and environmental effects. Solar PV and wind are the two emerging sources among the existing renewable energy sources. However intermittent nature and system cost are the two main limitations of renewable sources. Therefore hybridization of photovoltaic, wind with battery can overcome this drawback and improves the reliability as well as efficiency to some extent. The objectives considered in this work are to minimize the hybrid system cost and maximize the energy generation from PV and Wind with a new constraint of Grid Dependency Ratio (GDR). A new multi-objective optimization method is suggested in this work to achieve the aforementioned objectives. The proposed algorithm shows the quick convergence, improved accuracy and minimum processing time compared to the Particle Swarm Optimization algorithm.
Keywords: Photovoltaic, Wind, Battery, Hybrid Power System, Multi-Objective Optimization, Magnetotactic Bacteria and Particle Swarm Optimization.
Scope of the Article: Discrete Optimization