Abstract The increasing penetration of plug-in electric vehicles in recent years asks for specific solutions concerning the charging policies to be used in parking lots equipped with charging stations. In fact, simple policies based on uncoordinated charge of vehicles lead, in general, to high peak power demand, which may cause high costs to the car park owner. In this paper, the problem of minimizing the daily peak power of a charging station is addressed. Three sources of uncertainty affect the incoming vehicles: the arrival time, the departure time and the demanded energy to be charged. To assess the quality of the charging service under such uncertainties, a suitable customer satisfaction policy is employed. Depending on the information available on the uncertain variables, two algorithms based on a receding horizon approach are designed. Such algorithms require the solution of linear programs and provide the charging power for each plugged-in vehicle. Numerical simulations are provided to assess performance and computational burden of the algorithms, showing the effectiveness and feasibility of the proposed techniques.

中文翻译：

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存在不确定性时电动汽车充电站峰值功率最小化的后退方法

摘要 近年来，插电式电动汽车的普及率不断提高，需要针对配备充电站的停车场的充电政策提出具体的解决方案。事实上，基于车辆不协调充电的简单政策通常会导致高峰电力需求，这可能会给停车场所有者带来高昂的成本。在本文中，解决了最小化充电站日峰值功率的问题。三个不确定性来源会影响进站车辆：到达时间、出发时间和需要充电的能量。为了在这种不确定性下评估充电服务的质量，采用了合适的客户满意度政策。根据有关不确定变量的可用信息，设计了两种基于后退范围方法的算法。这种算法需要线性程序的解决方案，并为每辆插入式车辆提供充电功率。提供了数值模拟来评估算法的性能和计算负担，显示了所提出技术的有效性和可行性。