Affiliations: [a]
Department of Electrical and Electronics Engineering, GRT Institute of Engineering and Technology affiliated under Anna University of Chennai, India
| [b]
Department of Electrical and Electronics Engineering, Vels Institute of Science, Technology & Advanced Studies Chennai, India
| [c]
Department of Electrical and Electronics Engineering, St. Joseph’s Institute of Technology, Chennai, India
Correspondence:
[*]
Corresponding author. S. Swapna, Assistant Professor, Department of Electrical and Electronics Engineering, GRT Institute of Engineering and Technology affiliated under Anna University of Chennai, India. E-mail: meswapna1985@gmail.com.
Abstract: This paper proposes a hybrid optimization system depending on optimal location for electric vehicles parking lot (PL) and capacitors on distribution system to maintain voltage profile with electricity loss. The proposed system is the consolidation of Seagull optimization algorithm (SGO) and salp swarm algorithm (SSA). The migration and attacking behaviors of seagull is empowered through SSA method. By this manner, the proposed hybrid optimization scheme is known as SGOS2A method. Here, parking zone allocation with capacitor is considered to congestion management in conjunction through the compensation of reactive energy. So, one can optimally decide the size of automobile parking space, SGOS2A method is followed. Moreover, parking lot with capacitor allocation is considered to congestion control at the side of reactive power compensation. By this proper manipulate, the capacitors exact location, automobile parking space of electric vehicles on the grid, lessening of active with reactive power loss, voltage profile conversion is selected optimally. Besides, the proposed SGOS2A scheme is activated on MATLAB/Simulink site, then the efficiency is examined with different techniques. The mean, median and standard deviation of the proposed approach achieves 1.0593, 1.0915 and 0.1050.
Keywords: Electric vehicle, voltage and power loss, parking lot, seagull optimization algorithm, salp swarm algorithm
