Abstract
With the help of mobile charging stations (MCSs), the charging service of electric vehicles (EVs) can be provided more easily with higher payoff and lower consumption, compared with the fixed charging stations (FCSs). Although many traditional approaches have been used to decide the pricing plans for FCSs, it can not be efficient to design the optimal pricing strategy for MCSs. In this paper, we propose a contract-based scheme to solve the problem of supplying power service to EV users. Firstly, considering quality of service (QoS) and mobility of MCS in the heterogeneous networks, we study and develop the utility function based on the relationship for MCS and EV users. Then, the charging problem for EV users is formulated as an optimization problem through the contract theory. Thirdly, we present the iterative algorithm to achieve the optimal solution. Our simulation results show the effectiveness of the proposed strategy.
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References
Bayram, I., George, M., Michael, D., et al.: Electrical power allocation in a network of fast charging stations. IEEE J. Sel. Areas Commun. 31(7), 1235–1246 (2015)
Yuan, W., Huang, J., Zhang, Y.: Competitive charging station pricing for plug-in electric vehicles. In: Proceedings of the IEEE International Conference on Smart Grid Communications, pp. 668–673 (2014)
Abdelsamad, S., Morsi, W., Sidhu, T.: Impact of wind-based distributed generation on electric energy in distribution systems embedded with electric vehicles. IEEE Trans. Sustain. Energ. 6(1), 79–87 (2015)
Huang, S., He, L., Gu, Y., et al.: Design of a mobile charging service for electric vehicles in an urban environment. IEEE Trans. Intell. Transp. Syst. 16(2), 787–798 (2015)
Liu, N., Chen, Q., liu, J., et al.: A heuristic operation strategy for commercial building microgrids containing EVs and PV System. IEEE Trans. Industr. Electron. 62(4), 2560–2570 (2014)
Karbasioun, M., Ioannis, L., Gennady, S., et al.: Optimal charging strategies for electrical vehicles under real time pricing. In: Proceedings of the IEEE International Conference on Smart Grid Communications, pp. 746–751 (2012)
Bayram, I., Ismail, M., Abdallah, M., et al.: A pricing-based load shifting framework for EV fast charging stations. In: Proceedings of the IEEE International Conference on Smart Grid Communications, pp. 680–685 (2014)
Bahrami, S., Parniani, M.: Game theoretic based charging strategy for plug-in hybrid electric vehicles. IEEE Trans. Smart Grid. 5(5), 2368–2375 (2014)
Fan, S., Wang, X., Yu, D.: Exact BER analysis for signal code modulation in wireless communication. In: Proceedings of the International Conference on Vehicular Technology, pp. 11–14 (2008)
Zhang, K., Mao, Y., Leng, S., et al.: Incentive-driven energy trading in the smart grid. IEEE Access 4, 1243–1257 (2016)
Jeong, S., Jang, Y., Kum, D.: Economic analysis of the dynamic charging electric vehicle. IEEE Trans. Power Electron. 30(11), 6368–6377 (2015)
Lee, J., Guo, J., Choi, J., et al.: Distributed energy trading in microgrids: a game theoretic model and its equilibrium analysis. IEEE Trans. Industr. Electron. 62(6), 1–10 (2015)
Xie, X., Lei, W., Ma, S., et al.: Cognitive and cooperative wireless communication networks. Press (2012). ISBN 978-7-115-27365-9
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Chen, H., Su, Z., Hui, Y., Hui, H., Fang, D. (2017). A Contract Based Approach for Electric Vehicles Charging in Heterogeneous Networks. In: Li, K., Xue, Y., Cui, S., Niu, Q., Yang, Z., Luk, P. (eds) Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 763. Springer, Singapore. https://doi.org/10.1007/978-981-10-6364-0_32
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DOI: https://doi.org/10.1007/978-981-10-6364-0_32
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