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Charging Scheduling of Electric Vehicles with Local Renewable Energy under Uncertain Electric Vehicle Arrival and Grid Power Price

机译:基于局部可再生能源的电动汽车充电调度   不确定电动汽车到货和电网电价下的问题

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摘要

In the paper, we consider delay-optimal charging scheduling of the electricvehicles (EVs) at a charging station with multiple charge points. The chargingstation is equipped with renewable energy generation devices and can also buyenergy from power grid. The uncertainty of the EV arrival, the intermittence ofthe renewable energy, and the variation of the grid power price are taken intoaccount and described as independent Markov processes. Meanwhile, the chargingenergy for each EV is random. The goal is to minimize the mean waiting time ofEVs under the long term constraint on the cost. We propose queue mapping toconvert the EV queue to the charge demand queue and prove the equivalencebetween the minimization of the two queues' average length. Then we focus onthe minimization for the average length of the charge demand queue under longterm cost constraint. We propose a framework of Markov decision process (MDP)to investigate this scheduling problem. The system state includes the chargedemand queue length, the charge demand arrival, the energy level in the storagebattery of the renewable energy, the renewable energy arrival, and the gridpower price. Additionally the number of charging demands and the allocatedenergy from the storage battery compose the two-dimensional policy. We derivetwo necessary conditions of the optimal policy. Moreover, we discuss thereduction of the two-dimensional policy to be the number of charging demandsonly. We give the sets of system states for which charging no demand andcharging as many demands as possible are optimal, respectively. Finally weinvestigate the proposed radical policy and conservative policy numerically.
机译:在本文中,我们考虑在具有多个充电点的充电站对电动汽车(EV)进行延迟优化的充电调度。充电站配备了可再生能源发电设备,还可以从电网购买能源。电动汽车到来的不确定性,可再生能源的间歇性以及电网电价的变化都被考虑在内,并被描述为独立的马尔可夫过程。同时,每个EV的充电能量是随机的。目标是在成本的长期约束下使电动汽车的平均等待时间最小化。我们提出了队列映射方法,将EV队列转换为充电需求队列,并证明了两个队列的平均长度最小化之间的等效性。然后,我们将重点放在在长期成本约束下最小化充电需求队列的平均长度。我们提出了一个马尔可夫决策过程(MDP)的框架来研究此调度问题。系统状态包括被充电配额队列长度,充电需求到达,可再生能源存储电池中的能量水平,可再生能源到达以及电网价格。另外,充电需求的数量和来自蓄电池的分配的能量构成了二维策略。我们得出了最优政策的两个必要条件。此外,我们将二维策略的推论讨论为仅收费需求数。我们给出了无状态充电和尽可能多的充电最优的系统状态集。最后,我们对拟定的激进政策和保守政策进行了数值研究。

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