Optimal Electric Vehicle Charging Control Strategy Powered by Grid-Linked Hybrid PV-Wind-Battery Renewable Energy System
Adel Elgammal1, Tagore Ramlal2

1Adel Elgammal*, the University of Trinidad and Tobago UTT, Utilities Engineering, Point Lisas Campus, Trinidad and Tobago.
2Tagore Ramlal, the University of Trinidad and Tobago UTT, Utilities Engineering, Point Lisas Campus, Trinidad and Tobago.
Manuscript received on August 07, 2020. | Revised Manuscript received on August 15, 2020. | Manuscript published on August 30, 2020. | PP: 414-422 | Volume-9 Issue-6, August 2020. | Retrieval Number: F1475089620/2020©BEIESP | DOI: 10.35940/ijeat.F1475.089620
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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: An advanced model is proposed for grid connectivity of an interconnected network consisting of a charging station for electric automobiles. To automate the discharge procedure of charging/ the battery energy storing system, a wind network, the photovoltaic system, and the battery energy storing system is developed to efficiently increase the consumption degree of solar and wind energy sources and create renewable inner-city capacity. On the basis of DC bus architecture, the power design was planned such that buffered storage systems and renewable energy resources can be incorporated. The proposed optimal control algorithm uses the Swarm Optimization Algorithm consists of Multi-Objective Particle, developed for electric vehicles charging or discharge behaviors to minimize the overall actual energy loss and increase the integration of EVs with power networks due to the efficiency and economy of network activity, taking into account the economic issue and the satisfaction of consumers, the voltage limits and the parking availability pattern. To test the proposed EV charging strategy, simulation studies based on efficiency, and assessed major energy fluxes within the device. Energy management approaches have also been developed to optimize the power requirements and charging times of various electric vehicles. Results suggest that proposed model will substantially reduce the power grid’s operational costs while meeting the charging criteria of the customer. Improved performance on global search capabilities is also checked, as is the desired outcome of enhanced particle swarm optimization algorithm. The findings show that the new approach is in a position to prepare EV charging times optimally, taking into account electronic knowledge and uncertainty. 
Keywords: Electric vehicles, Photovoltaic systems, Energy storage, Adaptive charging control algorithm, charging station, energy management system, and multi-objective particle swarm optimization.