An adaptive approach for distribution system reconfiguration and charging management of plug-in electric vehicles (PEV) is presented in this study. A stochastic model predictive control is applied to stochastically, adaptively, and dynamically reconfigure the system, manage the incidental charging pattern of PEVs, and deal with the variable and uncertain power of renewable energy sources. The objective function of problem is minimizing daily operation cost of system. Herein, the geography of area is considered and the behavior of PEVs’ drivers (based on their income level) is modeled with respect to the value of incentive and their hourly distance from each charging station. It is shown that behavioral model of drivers is able to affect the optimal results of problem. The simulation results demonstrate the competence of the proposed approach for cost reduction and making the problem outputs robust with respect to prediction errors.

中文翻译：

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PEV充电管理和可再生能源渗透的配电系统重新配置的稳健方法

这项研究提出了一种自适应方法，用于插电式电动汽车（PEV）的配电系统重新配置和充电管理。随机模型预测控制用于随机，自适应和动态地重新配置系统，管理PEV的附带充电模式，并处理可再生能源的可变功率和不确定功率。问题的目标功能是最大程度地降低系统的日常运行成本。在此，考虑了地区的地理位置，并根据激励价值及其与每个充电站的每小时距离，对电动汽车的驾驶员的行为（基于其收入水平）进行了建模。结果表明，驾驶员行为模型能够影响问题的最优结果。