Optimal Location of EV Parking Lot by MAOWHO technique in Distribution System

Abstract

This paper presents a new hybrid method for optimally locating and sizing the Electric vehicle charging stations and managing the electric vehicle charging system. The developed hybrid method is a joint action of Mexican Axolotl Optimization (MAO) and Wild Horse Optimizer (WHO) and called as MAOWHO method. The main use of this new method is for place and sizing of the electric vehicles parking lot and to increase the applications of Electric Vehicle Parking Lot (EVPL) for involvement in the reserve market. This hybrid method reduces the fluctuations in voltage and power losses due to the huge load demand on electric vehicles and uncertainty in renewable energy sources. In critical moments the flexibility and reliable for the electrical network can be improved by joining the electric vehicles (EV) and photovoltaic (PV) systems. The objective variables in this optimization problem are the location and capacity of the renewable energy sources (RES) and EV charging station. The MAOWHO technique is implemented using MATLAB /Simulink platform and its performance is compared with present methods. Its simulation results are compared with other methods like slime mould optimization (SMO), chaos game optimization (CGO), side-blotched lizard algorithm (SBLA) and this proposed approach gives a profit of 880 €.