Abstract
This paper uses non-cooperative game to solve the optimization scheduling problem of charging and discharging of large-scale electric vehicles supporting V2G (Vehicle to Grid) in microgrid. Firstly, the new energy microgrid price calculation model and the charging and discharging process model of electric vehicles are constructed. Then, the objective function of the minimum charging cost of electric vehicles is proposed. After that, according to the characteristic that each electric vehicle aims to minimize its own charging cost in the charging process, the scheduling process is modeled as a non-cooperative game model between electric vehicles, there exists a unique Nash equilibrium of the game model. The Nash equilibrium solution method based on broadcast program is designed in this paper. Finally, through simulation, it can be seen that each electric vehicle constantly adjusts its charging strategy to minimize the charging cost during the game. The charging and discharging strategy of electric vehicle population reaches a stable Nash equilibrium, and the optimization goal can be got. The algorithm proposed in this paper can also reduce the total imported electricity and comprehensive operating costs of microgrid, which take into account the interests of both electric vehicles and microgrid.
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References
Marzband, M., Javadi, M., Pourmousavi, S.A., Lightbody, G.: An advanced retail electricity market for active distribution systems and home microgrid interoperability based on game theory. Electr. Power Syst. Res. 157, 187–199 (2018)
Saboori, H., Jadid, S., Savaghebi, M.: Optimal management of mobile battery energy storage as a self-driving, self-powered and movable charging station to promote electric vehicle adoption. Energies 14(3), 736 (2021)
Dequan, H.U., Guo, C., Qinbo, Y.U., Yang, X.: Bi-level optimization strategy of electric vehicle charging based on electricity price guide. Electr. Power Constr. 39(1), 48–53 (2018)
Suganya, S., Raja, S.C., Srinivasan, D., Venkatesh, P.: Smart utilization of renewable energy sources in a microgrid system integrated with plug-in hybrid electric vehicles. Int. J. Energy Res. 42(3), 1210–1224 (2017)
Zhang, W., Wang, J.: Research on V2G control of smart microgrid. In: Proceedings of the 2020 International Conference on Computer Engineering and Intelligent Control (ICCEIC), pp. 216–219 (2020)
Chukwu, U.C.: The impact of load patterns on power loss: A case of V2G in the distribution network. In: Proceedings of the 2020 Clemson University Power Systems Conference (PSC), pp. 1–4 (2020)
Tookanlou, M.B., Kani, S.A.P., Marzband, M.: An optimal day-ahead scheduling framework for E-mobility ecosystem operation with drivers’ preferences. IEEE Trans. Power Syst. 36(6), 5245–5257 (2021)
Amamra, S.A., Marco, J.: Vehicle-to-grid aggregator to support power grid and reduce electric vehicle charging cost. IEEE Access 7, 178528–178538 (2019)
Du, Y., Li, F.: Intelligent multi-microgrid energy management based on deep neural network and model-free reinforcement learning. IEEE Trans. Smart Grid 11, 1066–1076 (2019)
Li, H., Wan, Z., He, H.: Constrained EV charging scheduling based on safe deep reinforcement learning. IEEE Trans. Smart Grid 11, 2427–2439 (2019)
Cheng, L., Yu, T., Zhang, X., Yin, L.: Machine learning for energy and electric power systems: State of the art and prospects. Dianli Xitong Zidonghua/Autom. Electr. Power Syst. 43(1), 15–31 (2019)
Ma, Z.: Decentralized valley-fill charging control of large-population plug-in electric vehicles. In: Proceedings of the Control & Decision Conference. IEEE (2012)
Li, Y., et al.: Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing. Appl. Energy 232, 54–68 (2018)
Ma, Z., Callaway, D., Hiskens, I.: Decentralized charging control for large populations of plug-in electric vehicles. In: Proceedings of the IEEE International Conference on Control Applications. IEEE (2011)
Ma, Z., Callaway, D.S., Hiskens, I.A.: Decentralized charging control of large populations of plug-in electric vehicles. IEEE Trans. Control Syst. Technol. 21(1), 67–78 (2012)
Acknowledgement
Research work in this paper is supported by the National Natural Science Foundation of China (Grant No. 71871160) and Shanghai Science and Technology Innovation Action Plan (No. 19DZ1206800).
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Zeng, R., Zhang, H., Lu, J., Han, T., Guo, H. (2022). Real-Time Optimal Scheduling of Large-Scale Electric Vehicles Based on Non-cooperative Game. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_4
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