Abstract
Based on the time-sharing price and the technology of interaction between power grid and electric vehicle, the V2G model of electric vehicle is established. Taking the minimum user cost and load fluctuation as the objective function, the charging and discharging model of electric vehicles, wind power generation, photovoltaic power generation and thermal power generation systems are constructed. When solving the optimal solution of the model, genetic algorithm is used. After the optimal solution is obtained, the optimization model can make use of the random residual power, reduce the abandoned air volume and light volume, and achieve the purpose of restraining the load fluctuation and reducing the user cost.
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References
Shi, J., Yuan, T., Khan, S.A., Qin, W., Han, X., Duan, G.: Unit commitment strategy considering cooperated dispatch of electric vehicles based on scheduling capacity and wind power generation. High Voltage Eng. 44(10), 3433–3440 (2018). https://doi.org/10.13336/j.1003-6520.hve.20180925035
Li, Z., Zhao, S., Liu, Y.: Control strategy and application of distributed electric vehicle energy storage. Power Syst. Technol. 40(02), 442–450 (2016). (in Chinese)
Zhisheng, Z., Lingyun, W., Guo, L., Wei, Z.: Multi-objective coordinated scheduling of electric vehicles and renewable generation based on improved chemical reaction optimization algorithm. Power Syst. Technol. 38(03), 633–637 (2014). (in Chinese)
Jun, Y., Feng, Y.: An orderly charging and discharging strategy for residential electric vehicles based on peak-valley electricity tariffs. Power Syst. Prot. Control 46(15), 127–134 (2018). (in Chinese)
Qiao, W.: Electric vehicle charging load prediction based on Monte Carlo simulation. Huadian Technol. 40(06), 19–22, 77 (2018). (in Chinese)
Qiushuo, L., Xiangning, X., Jing, G., Lin, L.: Research on scheme for ordered charging of electric vehicles. Power Syst. Technol. 36(12), 32–38 (2012). (in Chinese)
Xiaoming, S., Wang Wei, S.S., Jiang Jiuchun, X., Lijie, H.X.: Coordinated charging strategy for electric vehicles based on time-of-use price. Autom. Electr. Power Syst. 37(01), 191–195 (2013). (in Chinese)
Run, H., Xin, Z., Zheng, Y., Jie, G.: The controlled charging dispatch strategy by considering of the uncertainty of electric vehicles. Modem Electr. Power 29(03), 57–63 (2012). (in Chinese)
Zhang, H., Hu, Z., Song, Y., Xu, Z., Jia, L.: A prediction method for electric vehicle charging load considering spatial and temporal distribution. Autom. Electr. Power Syst. 38(01), 13–20 (2014). (in Chinese)
Li, Z., Sun, H., Guo, Q., Wang, Y.: Study on wind-EV complementation on the transmission grid side considering carbon emission. Proc. CSEE 32(10), 7, 41–48 (2012). (in Chinese)
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Chen, X., Fan, Y. (2021). Coordinated Scheduling Optimization of V2G Technology and Renewable Energy for Electric Vehicles. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2020. Advances in Intelligent Systems and Computing, vol 1303. Springer, Singapore. https://doi.org/10.1007/978-981-33-4572-0_168
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DOI: https://doi.org/10.1007/978-981-33-4572-0_168
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