Electric vehicles (EVs), as part of sustainable transport, are believed to be helpful to reduce global warming. In this article, we focus on the fast-charging station (FCS) deployment problem, which is one of the key issues of the EV ecosystem. Specifically, elastic demand is considered, i.e., charging demand will be suppressed because of either long driving distance to get charging or long waiting time at the station. A fixed-point equation is proposed to capture the nature of the EV users’ charging behavior. It considers both spatial and temporal penalties by establishing a connection between the resulting arrival rate and a combination of driving distance and waiting time. We formulate the FCS deployment problem as a nonlinear integer problem, which seeks to figure out the optimal locations to build the FCSs and the optimal number of charging piles of each selected FCS. A genetic-algorithm-based heuristic algorithm is adopted to tackle the problem. Simulation results prove the effectiveness of our proposed algorithm. The importance of a match between the power grid capacity and the amount of charging demand is revealed, both in terms of increasing profit and reducing outage probability.

中文翻译：

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考虑弹性需求的快速充电站部署

电动汽车 (EV) 作为可持续交通的一部分，被认为有助于减少全球变暖。在本文中，我们关注快速充电站 (FCS) 部署问题，这是电动汽车生态系统的关键问题之一。具体而言，考虑弹性需求，即充电需求将因行驶距离长或在车站等待时间长而受到抑制。提出了一个定点方程来捕捉电动汽车用户充电行为的性质。它通过在最终到达率与行驶距离和等待时间的组合之间建立联系来考虑空间和时间损失。我们将 FCS 部署问题表述为非线性整数问题，它试图找出构建 FCS 的最佳位置以及每个选定 FCS 的最佳充电桩数量。采用基于遗传算法的启发式算法来解决该问题。仿真结果证明了我们提出的算法的有效性。揭示了电网容量与充电需求量之间匹配的重要性，无论是在增加利润还是降低停电概率方面。