  The increasing number of electric vehicles (EVs) has led to the need for
installing public electric vehicle charging stations (EVCS) to facilitate ease
of use and to support users who do not have the option of residential charging.
The public electric vehicle charging infrastructures (EVCIs) must be equipped
with a good number of EVCSs, with fast charging capability, to accommodate the
EV traffic demand, which would otherwise lead to congestion at the charging
stations. The location of these fast-charging infrastructures significantly
impacts the distribution system (DS). We propose the optimal placement of
fast-charging EVCIs at different locations in the distribution system, using
multi-objective particle swarm optimization (MOPSO), so that the power loss and
voltage deviations are kept at a minimum. Time-series analysis of the DS and EV
load variations are performed using MATLAB and OpenDSS. We further analyze the
cost benefits of the EVCIs under real-time pricing conditions and employ an
autoregressive integrated moving average (ARIMA) model to predict the dynamic
price. The simulated test system without any EVCI has a power loss of 164.36 kW
and squared voltage deviations of 0.0235 p.u. Using the proposed method, the
results obtained validate the optimal location of 5 EVCIs (each having 20 EVCSs
with a 50kWh charger rating) resulting in a minimum power loss of 201.40 kW and
squared voltage deviations of 0.0182 p.u. in the system. Significant cost
benefits for the EVCIs are also achieved, and an R-squared value of dynamic
price predictions of 0.9999 is obtained. This would allow the charging station
operator to make promotional offers for maximizing utilization and increasing
profits.
