An advanced electrical vehicle charging station using adaptive hybrid particle swarm optimization intended for renewable energy system for simultaneous distributions
Affiliations: [a] Department of EEE, Erode Sengunthar Engineering College, Perundurai, Tamilnadu, India
| [b] Department of ECE, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu, India
| [c] Department of ECE, Ramco Institute of Technology, Rajapalayam, Tamilnadu, India
Correspondence:
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Corresponding author: Johny Renoald Albert, Department of EEE, Erode Sengunthar Engineering College, Perundurai, Tamilnadu, India. E-mail: jorenoeee@esec.ac.in.
Abstract: A proposed hybrid approaches are incorporated in Electric Vehicle (EV) fast charging station (FCS) using (RES). Hybrid approach is improved by Adaptive Hybrid Particle Swarm Optimization (AHPSO) named as AHWPSO, moreover the proposed work Grey Wolf Optimization (GWO) is assist with adaptive hybridize PSO algorithm. Therefore, an overall pricing cost should be reduced maximum Electric Vehicle Charging Station (EVCS) with minimal installation. This simulation work is verified an adaptive time varying weightage parameters to increase the AHWPSO particle diversity factor. Proposed algorithm is incorporated with improve the novelty, and compared the results are recent version of PSO used for EVCS. Its increase the charging ability, energy loss minimization, voltage deviation reduction, and cost minimization. A distribution micro-grid capacity and demand are tested. Similarly, low to peak period energy variations are controlled by proposed algorithm with reduced capacitor bank. Overall control algorithm code is executed buy MATLAB/Simulink platform, the performance of this work listed, and compare to the existing approaches with achievement of maximum efficiency.
Keywords: Electric vehicle, renewable energy sources, adaptive hybrid PSO, grey wolf optimization, grid
