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.


  1. Froger et al., “The electric vehicle routing problem with partial charge, nonlinear charging function, and capacitated charging stations”, Annual Workshop EURO Working Group Veh. Rout. Logistics Optimiz., 2017.
  2. Yong et al., “A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects”, Renew. Sustain. Energy Rev., vol. 49, pp. 365-385, 2015.
  3. Deilami et al., “Real-time coordination of plug-in electric vehicle charging in smart grids to minimize power losses and improve voltage profile”, IEEE Trans. Smart Grid, vol. 2, no. pp. 456-467, 2011.
  4. Wang et al., “Traffic-constrained multiobjective planning of electric-vehicle charging stations”, IEEE Trans. Power Deliv., vol. 28, no. 4 pp. 2363-2372, 2013.
  5. Liu et al., “Vehicle-to-grid control for supplementary frequency regulation considering charging demands”, IEEE Trans. Power Syst., vol. 30, pp. 3110-3119, 2014.
  6. Torreglosa et al., “Decentralized energy management strategy based on predictive controllers for a medium voltage direct current photovoltaic electric vehicle charging station”, Energy Conv. Manage., vol. 108, pp. 1-13, 2016.
  7. Poullikkas, “Sustainable options for electric vehicle technologies”, Renew. Sustain. Energy Rev., vol. 41, pp. 1277-1287, 2015.
  8. Wang et al., “Coordinated planning strategy for electric vehicle charging stations and coupled traffic-electric networks”, IEEE Trans. Power Syst., vol. 34, no. 1, pp. 268-279, 2018.
  9. Mozafar, M. Moradi and M. Amini, “A simultaneous approach for optimal allocation of renewable energy sources and electric vehicle charging stations in smart grids based on improved GA-PSO algorithm”, Sustain. Cities Soc., vol. 32, pp. 627-637, 2017.
  10. Awasthi et al., “Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm”, Energy, vol. 133, pp. 70-78, 2017.
  11. Sen and H. Zhao, “Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective”, Appl. Energy, vol. 158, pp. 390-402, 2015.
  12. Hussein, “Capacity fade estimation in electric vehicle li-ion batteries using artificial neural networks”, IEEE Trans. Ind. Appl., vol. 51, no. 3, pp. 2321-2330, 2014.
  13. A Badri, K. Hoseinpour, “Stochastic multiperiod decision making framework of an electricity retailer considering aggregated optimal charging and discharging of electric vehicles”, Oper. Autom. Power Eng., vol. 3, no. 1, pp. 34-46, 2015.
  14. Rashidizadeh-Kermani et al., “Optimal decision-making framework of an electric vehicle aggregator in future and pool markets”, J. Oper. Autom. Power Eng., vol. 6, no. 2, pp. 157-168, 2018.
  15. Aghajani and I. Heydari, “Energy management in microgrids containing electric vehicles and renewable‎ energy sources considering demand response”, J. Oper. Autom. Power Eng., vol. 9, no.1, pp. 34-48, 2021.
  16. Sadhukhan, M. Ahmad and S. Sivasubramani, “Optimal allocation of EV charging stations in a radial distribution network using probabilistic load modeling”, IEEE Trans. Intel. Transp. Syst., 2021.
  17. Alhazmi, H. Mostafa and M. Salama, “Optimal allocation for electric vehicle charging stations using Trip Success Ratio”, Int. J. Electr. Power Energy Syst. vol. 91, pp. 101-116, 2017.
  18. Liu et al., “Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming”, Int. Trans. Electr. Energy Syst., vol. 30, no. 6, pp.1-20, 2020.
  19. Aghapour et al., “Probabilistic planning of electric vehicles charging stations in an integrated electricity-transport system”, Electr. Power Syst. Res. vol. 189, p. 106698, 2020.
  20. Shojaabadi et al., “Optimal planning of plug‐in hybrid electric vehicle charging station in distribution network considering demand response programs and uncertainties”, IET Gener., Transm. Distrib., vol. 10, no. 13, pp. 3330-3340, 2016.
  21. Jimenez and N. Garcia, “Power flow modeling and analysis of voltage source converter-based plug-in electric vehicles”, Proc. IEEE Power Energy Soc. Gen. Meet., 2011.
  22. Farkas, G. Szűcs and L. Prikler, “Grid impacts of twin EV fast charging stations placed alongside a motorway”, Proc. 4th Int. Youth Conf. Energy, 2013.