Probabilistic Optimal Allocation of Electric Vehicle Charging Stations Considering the Uncertain Loads by Using the Monte Carlo Simulation Method

Document Type : Research paper


Electrical Engineering Department, Engineering Faculty, Razi University, Kermanshah, Iran


Nowadays, the use of electric vehicles (EVs), in the form of distributed generation, as an appropriate solution is considered to replace combustion vehicles by reducing fuel consumption and supplying needed power. In this regard, the incorporation of EVs charging stations (EVCSs) in the power network can affect the distribution networks in different ways. On the other hand, the location of EVCS in distribution networks changes operational parameters includes electrical losses, and voltage deviations. Also, the probabilistic and uncertain behaviour of the loads and their daily changes can play a significant role on power distribution networks. To this end, in this paper, first, the modelling of the EVCSs affected by the behaviour of the EVs’ owner in a power distribution network is discussed. Then, the optimal location and size of EVCSs to reduce their negative effects on the network, including network losses (active and reactive) and voltage deviations are addressed in the presence of uncertain loads. The probabilistic model is investigated based on using the Monte Carlo simulation (MCS) method. The simulation results in MATLAB software environment show a 10% increase in active and reactive power losses in most hours of the day, due to increased power flow, when EVCSs are located in the optimal placement. The power losses at 24:00-7:00. when the EVs load is very low, are reduced due to decreased power flow across the lines. The results also show that if the EVCSs are not optimally located, the voltage deviation will increase by an average of 30% over a day, while by optimal placement of EVCSs, the voltage deviation increases to a maximum of 8% of the nominal value.


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Volume 11, Issue 4
December 2023
Pages 277-284
  • Receive Date: 26 February 2022
  • Revise Date: 12 March 2022
  • Accept Date: 06 June 2022
  • First Publish Date: 13 December 2022