Charging demand for electric vehicle based on stochastic analysis of trip chain

Charging demand for electric vehicle based on stochastic analysis of trip chain

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With its popularisation, electric vehicle (EV) has been part of the important load of smart grid. The analysis on charging demands of EVs is the basis of the study of influences on power grid and charging infrastructures planning. Charging demand calculation is based on the precise analysis on the driving law of users. This study proposes a method based on the stochastic simulation of trip chain to solve the existing problem. First, the concept and characteristic variables (trip start time, driving time, parking duration, driving distance and trip purpose) of trip chain are introduced. Second, the probability distribution models of these variables are established considering their correlative relationships. Third, using Monte Carlo method, the complete trip chains are simulated, and then the spatial–temporal distributions of charging demands are calculated. Finally, based on the National household trip survey data, and according to the proposed method, charging demands in different areas are studied. The results show that the proposed method can accurately simulate the driving law of users and reflect spatial–temporal distribution characteristics of charging demands, and different charging scenarios will lead to different forms of charging demand distribution, which will exert different impacts on power system.


    1. 1)
      • 1. Situ, L.: ‘Electric vehicle development: the past, present & future’. Proc. of Int. Conf. on Power Electronics Systems and Applications, Hong Kong, China, May 2009, pp. 2022.
    2. 2)
    3. 3)
      • 3. Shireen, W., Patel, S.: ‘Plug-in hybrid electric vehicles in the smart grid environment’. 2010 IEEE PES Transmission and Distribution Conf. and Exposition, New Orleans, USA, April 2010, pp. 1922.
    4. 4)
    5. 5)
    6. 6)
      • 6. Yi, F., Li, F.: ‘An exploration of a probabilistic model for electric vehicles residential demand profile modeling’. 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, July 2012, pp. 2226.
    7. 7)
      • 7. Yang, B., Wang, L., Liao, C.: ‘Research on power-charging demand of large-scale electric vehicles and its impacting factors’, Trans. China Electrotech. Soc., 2013, 28, (2), pp. 2227.
    8. 8)
      • 8. Zhang, H., Hu, Z., Song, Y.: ‘A prediction method for electric vehicle charging load considering spatial and temporal distribution’, Autom. Electr. Power Syst., 2014, 38, (1), pp. 1320.
    9. 9)
    10. 10)
      • 10. Tehrani, N.H., Shrestha, G.B., Wang, P.: ‘Probabilistic estimation of system-wide electric vehicle charging demand’. Proc. of Probabilistic Methods Applied to Power System Conf., Istanbul, Turkey, 2012, pp. 251256.
    11. 11)
    12. 12)
    13. 13)
      • 13. Liu, Z., Li, X.: ‘Review of trip-chain-based travel activity study of residents’. 2010 Int. Conf. on Logistics Systems and Intelligent Management, Harbin, China, January 2010, pp. 910.
    14. 14)
      • 14. U.S. Department of Transportation, Federal Highway Administration: ‘National household travel survey’. 2009, Available at:
    15. 15)
    16. 16)
      • 16. Dempster, A.P., Laird, N.M., Rubin, D.B.: ‘Maximum likelihood from incomplete data via the EM algorithm’, J. R. Statist. Soc. B, 1977, 39, (6), pp. 138.
    17. 17)
    18. 18)
      • 18. Mauri, G., Valsecchi, A.: ‘Fast charging stations for electric vehicle: the impact on the MV distribution grids of the MILAN metropolitan area’. 2012 IEEE Int. Energy Conf. and Exhibition, Florence, Italy, September 2012, pp. 912.
    19. 19)
      • 19. Akhavan-Rezai, E., Shaaban, M.F., El-Saadany, E.F., et al: ‘Uncoordinated charging impacts of electric vehicles on electric distribution grids: normal and fast charging comparison’. 2012 IEEE Power and Energy Society General Meeting, San Diego, USA, July 2012, pp. 2226.
    20. 20)
      • 20. Amprion: ‘Demand in control area’. Available at:

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