This article proposes a new convex optimization strategy for coordinating electric vehicle charging, which accounts for battery voltage rise and the associated limits on maximum charging power. Optimization strategies for coordinating electric vehicle charging commonly neglect the increase in battery voltage, which occurs as the battery is charged. However, battery voltage rise is an important consideration since it imposes limits on the maximum charging power. This is particularly relevant for dc fast charging, where the maximum charging power may be severely limited, even at the moderate state of charge levels. First, a reduced-order battery circuit model is developed, which retains the nonlinear relationship between the state of charge and maximum charging power. Using this model, limits on the battery output voltage and battery charging power are formulated as the second-order cone constraints. These constraints are integrated with a linearized power flow model for three-phase unbalanced distribution networks. This provides a new multiperiod optimization strategy for electric vehicle smart charging. The resulting optimization is a second-order cone program and, thus, can be solved in polynomial time by standard solvers. A receding horizon implementation allows the charging schedule to be updated online, without requiring prior information about when vehicles will arrive.

中文翻译：

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电池电压约束下电动汽车站智能充电的圆锥优化

本文提出了一种用于协调电动汽车充电的新凸优化策略，该策略考虑了电池电压上升和最大充电功率的相关限制。用于协调电动汽车充电的优化策略通常会忽略电池充电时电池电压的增加。然而，电池电压上升是一个重要的考虑因素，因为它限制了最大充电功率。这对于直流快速充电尤其重要，即使在中等充电水平下，最大充电功率也可能受到严重限制。首先，开发了一个降阶电池电路模型，它保留了充电状态和最大充电功率之间的非线性关系。使用这个模型，电池输出电压和电池充电功率的限制被公式化为二阶锥约束。这些约束与三相不平衡配电网络的线性化潮流模型相结合。这为电动汽车智能充电提供了一种新的多周期优化策略。由此产生的优化是一个二阶锥程序，因此可以通过标准求解器在多项式时间内求解。后退地平线实施允许在线更新充电时间表，而无需有关车辆何时到达的事先信息。这为电动汽车智能充电提供了一种新的多周期优化策略。由此产生的优化是一个二阶锥程序，因此可以通过标准求解器在多项式时间内求解。后退地平线实施允许在线更新充电时间表，而无需事先了解车辆何时到达。这为电动汽车智能充电提供了一种新的多周期优化策略。由此产生的优化是一个二阶锥程序，因此可以通过标准求解器在多项式时间内求解。后退地平线实施允许在线更新充电时间表，而无需事先了解车辆何时到达。