The relevance and presence of Electric Vehicles (EVs) are increasing all over the world since they seem an effective way to fight pollution and greenhouse gas emissions, especially in urban areas. One of the main issues related to EVs is the necessity of modifying the existing infrastructure to allow the installation of new charging stations (CSs). In this scenario, one of the most important problems is the definition of smart policies for the sequencing and scheduling of the vehicle charging process. The presence of intermittent energy sources and variable execution times represent just a few of the specific features concerning vehicle charging systems. Even though optimization problems regarding energy systems are usually considered within a discrete time setting, in this paper a discrete event approach is proposed. The fundamental reason for this choice is the necessity of limiting the number of the decision variables, which grows beyond reasonable values when a short time discretization step is chosen. The considered optimization problem regards the charging of a series of vehicles by a CS connected with a renewable energy source, a storage element, and the main grid. The objective function to be minimized results from the weighted sum of the (net) cost for purchasing energy from the external grid, the weighted tardiness of the services provided to the customers, and a cost related to the occupancy of the socket during the charging. The approach is tested on a real case study. The limited computational burden allows also the implementation in real-case applications.

中文翻译：

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多插座汽车充电站离散事件优化

电动汽车 (EV) 的相关性和存在性在世界各地都在增加，因为它们似乎是对抗污染和温室气体排放的有效方法，尤其是在城市地区。与电动汽车相关的主要问题之一是修改现有基础设施以允许安装新的充电站 (CS) 的必要性。在这种情况下，最重要的问题之一是为车辆充电过程的排序和调度定义智能策略。间歇性能源的存在和可变的执行时间仅代表了与车辆充电系统相关的几个特定特征。尽管关于能源系统的优化问题通常在离散时间设置内考虑，但本文提出了一种离散事件方法。The fundamental reason for this choice is the necessity of limiting the number of the decision variables, which grows beyond reasonable values when a short time discretization step is chosen. 所考虑的优化问题涉及通过与可再生能源、存储元件和主电网相连的 CS 对一系列车辆进行充电。要最小化的目标函数由从外部电网购买能源的（净）成本、向客户提供的服务的加权延迟以及充电期间与插座占用相关的成本的加权总和得出。该方法在一个真实的案例研究中进行了测试。有限的计算负担也允许在实际应用中实现。which grows beyond reasonable values when a short time discretization step is chosen. 所考虑的优化问题涉及通过与可再生能源、存储元件和主电网相连的 CS 对一系列车辆进行充电。要最小化的目标函数由从外部电网购买能源的（净）成本、向客户提供的服务的加权延迟以及充电期间与插座占用相关的成本的加权总和得出。该方法在一个真实的案例研究中进行了测试。有限的计算负担也允许在实际案例应用中实现。which grows beyond reasonable values when a short time discretization step is chosen. 所考虑的优化问题涉及通过与可再生能源、存储元件和主电网相连的 CS 对一系列车辆进行充电。要最小化的目标函数由从外部电网购买能源的（净）成本、向客户提供的服务的加权延迟以及充电期间与插座占用相关的成本的加权总和得出。该方法在一个真实的案例研究中进行了测试。有限的计算负担也允许在实际案例应用中实现。一个存储元件和主网格。要最小化的目标函数由从外部电网购买能源的（净）成本、向客户提供的服务的加权延迟以及充电期间与插座占用相关的成本的加权总和得出。该方法在一个真实的案例研究中进行了测试。有限的计算负担也允许在实际案例应用中实现。一个存储元件和主网格。要最小化的目标函数由从外部电网购买能源的（净）成本、向客户提供的服务的加权延迟以及充电期间与插座占用相关的成本的加权总和得出。该方法在一个真实的案例研究中进行了测试。有限的计算负担也允许在实际案例应用中实现。