Electric Vehicles (EVs) are gradually replacing conventional vehicles as they are environmentally friendly and cause less pollution problems. Unregulated charging has severe impacts on the distribution grid and may incur EV owners higher charging costs. Therefore, controlled charging infrastructures to supply the charging needs of large numbers of EVs are of vital importance. In this article, an optimal control scenario is presented to formulate the charge scheduling problem of EVs in a solar charging station (CS). Two different objective functions are considered. The first objective function holds for minimizing the total charging cost of EVs. In this case, the benefits of Vehicle‐to‐Grid (V2G) are investigated by comparing the charging costs of EVs with and without this capability. The total EV charging costs and grid benefits are also investigated in the second objective function which holds for minimizing the extracted power from the grid. A modified version of Dynamic Programming is used to solve the large state‐space model defined for the optimal control problem with extremely shorter computation time and minimal loss of optimality. Extensive simulations are done in two representative summer and winter climates to determine the role of solar energy in the CS performance. The results show that in the cost minimization algorithms, significant savings for EV owners and a smooth load shape for the grid are achieved. For the minimized power from the grid algorithm, a total near Photovoltaic (PV)‐curve charging power is obtained to exploit the PV power as much as possible to minimize the impacts on the grid.

中文翻译：

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使用动态规划的太阳能充电站中不同最佳充电方案的性能

电动汽车（EV）环保且减少污染问题，因此正逐步取代传统汽车。不受监管的充电会对配电网造成严重影响，并可能导致电动汽车所有者收取更高的充电成本。因此，满足大量电动汽车充电需求的可控充电基础设施至关重要。在本文中，提出了一种最优控制方案，以制定太阳能充电站（CS）中电动汽车的充电调度问题。考虑了两个不同的目标函数。第一个目标函数是保持电动汽车的总充电成本最小化。在这种情况下，通过比较具有和不具有此功能的电动汽车的充电成本，研究了车辆到电网（V2G）的优势。在第二目标函数中还研究了总的电动汽车充电成本和电网收益，该目标函数可最大程度地减少从电网中提取的功率。动态编程的修改版用于解决为最优控制问题定义的大型状态空间模型，该模型具有极短的计算时间和最小的最优性损失。在两个代表性的夏季和冬季气候中进行了广泛的模拟，以确定太阳能在CS性能中的作用。结果表明，在成本最小化算法中，可为电动汽车所有者节省大量资金，并为电网带来平滑的负载形状。对于电网算法中的最小功率，将获得总的近光伏（PV）曲线充电功率，以尽可能多地利用PV功率，以最大程度地降低对电网的影响。