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access icon free Distributed charging management of multi-class electric vehicles with different charging priorities

This study proposes distributed energy management approach for charging multi-class electric vehicles (EVs) in community microgrids. The energy management problem is implemented in real-time and represented by a non-cooperative Stackelberg game for the power distribution inside the microgrid. In this game, a battery energy storage system is chosen as a leader and the EVs are designated as followers. The charging power distribution among EVs is tackled in the two cases of ‘plenty of power’ and ‘lack of power’. The challenging case of ‘lack of power’ occurs when the total charging power is insufficient to meet the need of each EV, such as when weather conditions are unfavourable. A priority factor is included in the EV utility functions to address charging priorities of different classes of EVs in practical scenarios. A consensus-based distributed algorithm is developed later to iteratively reach the Nash equilibrium, i.e. final charging power distribution, among EVs with different charging priorities. Both simulation and experimental results show that the charging power is properly distributed when the predefined charging priorities are followed, particularly in the case of a ‘lack of power’.

References

    1. 1)
      • 13. Yang, H., Xie, X., Vasilakos, A.V.: ‘Noncooperative and cooperative optimization of electric vehicle charging under demand uncertainty: a robust Stackelberg game’, IEEE Trans. Veh. Technol., 2016, 65, (3), pp. 10431058.
    2. 2)
      • 18. Piller, S., Perrin, M., Jossen, A.: ‘Method for state of charge determination and their applications’, J. Power Sources, 2001, 96, pp. 113120.
    3. 3)
      • 15. Eldjalil, C.D.A., Lyes, K.: ‘Optimal priority-queuing for EV charging-discharging service based on cloud computing’. 2017 IEEE Int. Conf. on Communications (ICC), Paris, France, 2017, pp. 16.
    4. 4)
      • 14. Li, J., Li, C., Xu, Y., et al: ‘Noncooperative game-based distributed charging control for plug-in electric vehicles in distribution networks’, IEEE Trans. Ind. Inf., 2018, 14, pp. 301310.
    5. 5)
      • 8. Garcia-Trivino, P., Torreglosa, J.P., Fernandez-Ramirez, L.M., et al: ‘Decentralized fuzzy logic control of microgrid for electric vehicle charging station’, IEEE J. Emerg. Sel. Top. Power Electron., 2018, 6, (2), pp. 726737.
    6. 6)
      • 10. Zhao, Y., He, X., Yao, Y., et al: ‘Plug-in electric vehicle charging management via a distributed neurodynamic algorithm’, Appl. Soft. Comput., 2019, 80, pp. 557566.
    7. 7)
      • 22. Kisacikoglu, M.C., Erden, F., Erdogan, N.: ‘Distributed control of PEV charging based on energy demand forecast’, IEEE Trans. Ind. Inf., 2018, 14, (1), pp. 332341.
    8. 8)
      • 2. Yoldaş, Y., Önen, A., Muyeen, S., et al: ‘Enhancing smart grid with microgrids: challenges and opportunities’, Renw. Sust. Energy Rev., 2017, 72, pp. 205214.
    9. 9)
      • 20. ‘Renewable resource data center, national renewable energy laboratory’, (http://www.nrel.gov/rredc/), accessed 27 March 2019.
    10. 10)
      • 6. Wu, Y., Ravey, A., Chrenko, D., et al: ‘Demand side energy management of EV charging stations by approximate dynamic programming’, Energy Convers. Manage., 2019, 196, pp. 878890.
    11. 11)
      • 1. Ravichandran, A., Sirouspour, S., Malysz, P., et al: ‘A chance-constraints-based control strategy for microgrids with energy storage and integrated electric vehicles’, IEEE Trans. Smart Grid, 2018, 9, (1), pp. 346359.
    12. 12)
      • 21. Rahbari-Asr, N., Chow, M.Y.: ‘Cooperative distributed demand management for community charging of PHEV/PEVs based on KKT conditions and consensus networks’, IEEE Trans. Ind. Inf., 2014, 10, (3), pp. 19071916.
    13. 13)
      • 11. Kikusato, H., Fujimoto, Y., Hanada, S., et al: ‘Electric vehicle charging management using auction mechanism for reducing PV curtailment in distribution systems’, IEEET Sustain. Energy, 2019, doi: 10.1109/TSTE.2019.2926998.
    14. 14)
      • 17. Zhang, J., Li, K.J., Wang, M., et al: ‘A bi-level program for the planning of an islanded microgrid including caes’, IEEE Trans. Ind. Appl., 2016, 52, (4), pp. 27682777.
    15. 15)
      • 23. Zhang, T., Chen, X., Yu, Z., et al: ‘A monte carlo simulation approach to evaluate service capacities of EV charging and battery swapping stations’, IEEE Trans. Ind. Inf., 2018, 14, (9), pp. 39143923.
    16. 16)
      • 3. Yao, L., Lim, W.H., Tsai, T.S.: ‘A real-time charging scheme for demand response in electric vehicle parking station’, IEEE Trans. Smart Grid, 2017, 8, (1), pp. 5262.
    17. 17)
      • 19. ‘Commercial load datasets’, (http://en.openei.org/datasets/files/961/pub/), accessed 27 March 2019.
    18. 18)
      • 12. Tushar, W., Saad, W., Poor, H.V., et al: ‘Economics of electric vehicle charging: a game theoretic approach’, IEEE Trans. Smart Grid, 2012, 3, (4), pp. 17671778.
    19. 19)
      • 25. Fletcher, R., Bomze, I.M., Demyanov, V.F., et al: ‘The sequential quadratic programming method’ in ‘Nonlinear optimization’ (Springer, Berlin, Germany, 2010).
    20. 20)
      • 16. Kumar, K.N., Sivaneasan, B., So, P.L.: ‘Impact of priority criteria on electric vehicle charge scheduling’, IEEE Trans. Transp. Electrif., 2015, 1, (3), pp. 200210.
    21. 21)
      • 24. Rahmani-Andebili, M., Mahmud, F.F.: ‘An adaptive approach for PEVs charging management and reconfiguration of electrical distribution system penetrated by renewables’, IEEE Trans. Ind. Inf., 2018, 14, (5), pp. 20012010.
    22. 22)
      • 5. Qi, J., Lai, C., Xu, B., et al: ‘Collaborative energy management optimization toward a green energy local area network’, IEEE Trans. Ind. Inf., 2018, 14, (12), pp. 54105418.
    23. 23)
      • 9. Liu, Y., Deng, R., Liang, H.: ‘A stochastic game approach for PEV charging station operation in smart grid’, IEEE Trans. Ind. Inf., 2018, 14, (3), pp. 969979.
    24. 24)
      • 7. Zhao, T., Li, Y., Pan, X., et al: ‘Real-time optimal energy and reserve management of electric vehicle fast charging station: hierarchical game approach’, IEEE Trans. Smart Grid, 2018, 9, (5), pp. 53575370.
    25. 25)
      • 4. Yan, Q., Zhang, B., Kezunovic, M.: ‘Optimized operational cost reduction for an EV charging station integrated with battery energy storage and PV generation’, IEEE Trans. Smart Grid, 2019, 10, (2), pp. 20962106.
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