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Electric vehicle charging scheduling under local renewable energy and stochastic grid power price

机译:当地可再生能源和随机电网电价下的电动汽车充电调度

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In the paper, we consider delay-optimal charging scheduling of the electric vehicles (EVs) at a renewable energy aided charging station with multiple charge points. The uncertainty of the EV arrival, the intermittence of the renewable energy, and the variation of the grid power price are taken into account. In each period, the station determines the number of charging EVs during this period. Meanwhile, it also chooses the amount of renewables used for charging (the rest amount of energy will be purchased from the grid). The goal is to minimize the mean waiting time of EVs under the long-term constraint on the cost. We formulate a stochastic optimization problem, in which the charging EV number sequence and the allocated renewable energy sequence compose the two-dimensional optimization variable vector sequence, to investigate this scheduling problem. We derive the formal solution of the problem. Specifically, we prove that the optimal variable vector can be successively obtained: the optimal number of charging EVs is the solution of a reduced stochastic optimization problem and the greedy renewable energy allocation is optimal for given number of charging EVs. Finally, based on theoretical analysis, we propose two strategies for the problem and we investigate the proposed strategies numerically.
机译:在本文中,我们考虑了在具有多个充电点的可再生能源辅助充电站对电动汽车(EV)进行延迟优化的充电调度。考虑了电动汽车到站的不确定性,可再生能源的间歇性以及电网电价的变化。在每个时段中,​​站点确定该时段内的充电电动汽车数量。同时,它还选择用于充电的可再生能源的数量(其余的能源将从电网购买)。目标是在成本的长期约束下,最大限度地减少电动汽车的平均等待时间。我们提出了一个随机优化问题,其中充电电动汽车编号序列和分配的可再生能源序列组成二维优化变量矢量序列,以研究此调度问题。我们得出问题的正式解决方案。具体而言,我们证明了可以连续获得最优变量向量:充电电动汽车的最佳数量是减少的随机优化问题的解决方案,对于给定数量的充电电动汽车,贪婪的可再生能源分配是最优的。最后,在理论分析的基础上,我们针对该问题提出了两种策略,并对所提出的策略进行了数值研究。

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