Due to the increasing penetration of electric vehicles (EVs) in the distribution grid, coordinated control of their charging is required to maintain a proper grid operation. Many EV charging strategies assume that the EV can charge at any rate up to a maximum value. Furthermore, many strategies use detailed predictions of uncertain data such as uncontrollable loads as input. However, in practice, charging can often be done only at a few discrete charging rates and obtaining detailed predictions of the uncertain data is difficult. Therefore, this paper presents an online EV scheduling approach based on discrete charging rates that does not require detailed predictions of this uncertain data. Instead, the approach requires only a prediction of a single value that characterizes an optimal offline EV schedule. Simulation results show that this approach is robust against prediction errors in this characterizing value and that this value can be easily predicted. Moreover, the results indicate that incorporating practical limitations such as discrete charging rates and uncertainty in uncontrollable loads can be done in an efficient and effective way.

由于电动汽车（EV）在配电网中的普及率不断提高，因此需要对其充电进行协调控制，以维持正常的电网运行。许多EV充电策略都假定EV可以以任何速率充电直至最大值。此外，许多策略都使用不确定数据（例如不可控制的负载）的详细预测作为输入。然而，实际上，通常只能以几个离散的充电速率进行充电，并且难以获得不确定数据的详细预测。因此，本文提出了一种基于离散充电率的在线EV调度方法，该方法不需要对该不确定数据进行详细预测。相反，该方法仅需要预测表征最佳离线EV时间表的单个值。仿真结果表明，该方法对于该特征值的预测误差具有鲁棒性，并且可以轻松地预测该值。而且，结果表明，可以以有效和有效的方式结合实际的局限性，例如离散的充电速率和不可控负载中的不确定性。