{"references": ["1.\tNational Research Council. (2010). Transitions to Alternative Transportation Technologies\u00e2\u00ac\" Plug-in Hybrid Electric Vehicles. National Academies Press.", "2.\thttp://www.transitchicago.com/about/facts.aspx", "3.\thttp://www.byd.com/ElectricBus.html.", "4.\thttp://www.byd.com/e6.html.", "5.\thttp://www.byd.com/auto/S6DM.htm", "6.\tGu, Y., & Xie, L. (2010, September). Look-ahead coordination of wind energy and electric vehicles: A market-based approach. In North American Power Symposium 2010 (pp. 1-8). IEEE.", "7.\tLuo, Z., Song, Y., Hu, Z., Xu, Z., Yang, X., & Zhan, K. (2011, July). Forecasting charging load of plug-in electric vehicles in China. In 2011 IEEE Power and Energy Society General Meeting (pp. 1-8). IEEE.", "8.\tYunus, K. J., Reza, M., Zelaya-De La Parra, H., & Srivastava, K. (2012, January). Impacts of stochastic residential plug-in electric vehicle charging on distribution grid. In 2012 IEEE PES Innovative Smart Grid Technologies (ISGT) (pp. 1-8). IEEE.", "9.\tSundstrom, O., & Binding, C. (2011). Flexible charging optimization for electric vehicles considering distribution grid constraints. IEEE Transactions on Smart grid, 3(1), 26-37.", "10.\thttp://www.aemo.com.au/en/Electricity/NEM-Data/Price-and-Demand-Data-Sets/Current-Dispatch-Interval-Price-and-Demand-Graph-NSW"]}

A stochastic model in MATLAB is created on this paper to discover the impact of charging demand from plug-in hybrid electric-powered vehicles (PHEVs). Two styles of PHEVs are defined on this version: public transportation automobiles and private automobiles. For each sort of vehicle, a different charging time plan, charging velocity, and battery length are taken under consideration. When the penetration level of PHEVs keeps to upward thrust to 30% in 2030, the simulation findings show that there might be two load peaks (at noon and inside the night). As a result, an optimization device is used to transport load peaks. Real-time pricing and wind power output information are used in this optimization approach. The energy allocated to each automobile might be managed with the assistance of a smart grid. As a result, this optimization can be capable of attain the intention of relocating load peaks to valley areas with low real-time prices and strong wind output.