Optimal Charging Strategy for Spatially Distributed Electric Vehicles in Power System by Remote Analyser

  • R. VenkataswamyEmail author
  • Teena M. Joseph
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 80)


The burden on the consumer for the price of fuel for classic vehicles is the root cause for the emergence of the fast growing trend in the power driven vehicles or electric vehicles. Less acceptance of electric vehicles by the customers and the hesitancy to replace traditional fuel powered vehicles by considering the economic factor is a major concern that existing in the current scenario. Therefore, for the proper balancing of the load with respect to the power available among different neighbouring charging stations in a given area, a load scheduling algorithm is used. The optimal route planner for the electric vehicles reaching the charging station is identified and then the power carried by each feeder is calculated by cumulative power of all the charging stations. The identification of the possible route is performed by the spatial network analysis which will be executing at remote analyzer. The location, state of charge, and other details of the electric vehicle through telemetry is used to find the best charging station for the particular vehicle in view of the cost, distance and the time. The performance of the technique is evaluated with and without optimization by considering the logical constraints; and the results are presented.


Google maps Load scheduling Optimization Optimal route planner 



The authors would like to acknowledge and express the deepest gratitude to management of Faculty of Engineering, Christ (Deemed to be University) for providing freedom to work in the laboratory.

Supplementary material


  1. 1.
    Abousleiman, R., Rawashdeh, O.: Electric vehicle modeling and energy-efficient routing using particle swarm optimisation. In: Research Article Published by Department of Electrical and Computer Engineering, Oakland University, IET, vol. 10, pp. 65–72 (2015)CrossRefGoogle Scholar
  2. 2.
    Rahman, S., Yeasmin, N., Ahmmed, M.U., Kaiser, M.S.: Adaptive route selection support system based on road traffic information. In: 2nd International Conference on Electrical Engineering and Information and Communication Technology (lCEEICT), Dhaka, Bangladesh, pp. 1–6 (2015)Google Scholar
  3. 3.
    Chen, J., Yang, J., Lib, M., Zhoub, Q., Jiang, B.: An optimal charging strategy for electrical vehicles based on the electricity price with temporal and spatial characteristics. In: IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, USA, pp. 1–5 (2016)Google Scholar
  4. 4.
    Boi, S., Zhongpei, S., Dajun, W., Li, Y.: Research on optimal control of electric vehicle charging in residential area. In: Proceedings of the 35th Chinese Control Conference, Chengdu, China, pp. 8617–8621 (2016)Google Scholar
  5. 5.
    Jozi, F., Mazlumi, K., Hosseini, H.: Charging and discharging coordination of electric vehicles in a parking lot considering the limitation of power exchange with the distribution system. In: IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), Tehran, Iran, pp. 937–941 (2017)Google Scholar
  6. 6.
    Georgiev, M., Stanev, R., Krusteva, A.: Flexible load control in electric power systems with distributed energy resources and electric vehicle charging. In: IEEE International Power Electronics and Motion Control Conference (PEMC), Varna, Bulgaria, pp. 1034–1040 (2016)Google Scholar
  7. 7.
    Liu, R., Zong, X., Mu, X.: Electric vehicle charging control system based on the characteristics of charging power. In: Chinese Automation Congress (CAC), Jinan, China, pp. 3860–3840 (2018)Google Scholar
  8. 8.
    Zhang, J., Zhou, H., Li, H., Liu, H., Li, B., Yan, C.L.: Multi-objective planning of charging stations considering vehicle arrival hot map. In: IEEE Conference on Energy Internet and Energy System Integration (EI2), Beijing, China, pp. 1–6 (2017)Google Scholar
  9. 9.
    Wang, Z., Paranjape, R.: Optimal scheduling algorithm for charging electric vehicle in a residential sector under demand response. In: IEEE Electrical Power and Energy Conference (EPEC), London, ON, Canada, pp. 45–49 (2015)Google Scholar
  10. 10.
    Jiang, R., Zhang, Li, J., Zhang, J., Huang, Q.: A coordinated charging strategy for electric vehicles based on multi-objective optimization. In: 2nd International Conference on Power and Renewable Energy, Chengdu, China, pp. 823–827 (2017)Google Scholar
  11. 11.
    Ceng, R.: Optimal charging/discharging control for electric vehicles considering power system constraints and operation costs. IEEE Trans. Power Syst. 31, 1854–1860 (2016)CrossRefGoogle Scholar
  12. 12.
    Huo, Y., Bouffard, F., Jos, G.: An energy management approach for electric vehicle fast charging station. In: IEEE Electrical Power and Energy Conference (EPEC), Saskatoon, SK, Canada, pp. 1–6 (2017)Google Scholar
  13. 13.
    Mistry, R.D., Eluyemi, F.T., Masaud, T.M.: Impact of aggregated EVs charging station on the optimal scheduling of battery storage system in islanded microgrid. In: North American Power Symposium (NAPS), Morgantown, WV, USA, pp. 1–5 (2017)Google Scholar
  14. 14.
    Hafez, O., Bhattacharya, K.: Integrating EV charging stations as smart loads for demand response provisions in distribution systems. IEEE Trans. Smart Grid 9, 1096–1106 (2018)CrossRefGoogle Scholar
  15. 15.
    Venkataswamy R.: Open IoE, Last accessed 26 Oct 2018

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electrical & Electronics Engineering, Faculty of EngineeringChrist (Deemed to be University)BangaloreIndia

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