Plug-in electric vehicle (PEV) charging station service capability is physically limited by the charger availability and local transformer capacity. However, the station operation performance has become an increasingly important factor for enhancing charging service accessibility. In this work, we propose an innovative station-level optimization framework to operate charging station with optimal pricing policy and charge scheduling. This model incorporates human behaviors to explicitly and effectively capture drivers’ charging decision process. We propose a menu of price-differentiated charging services, which differ in both the per-unit prices and the energy delivery schedule. Involving human in the loop, the operation model also exploits the capability to alleviate the overstay issue that occurs when a PEV’s charging session has completed. We then propose a muli-block convex transformation methodology to reformulate the resulted non-convex problem via Fenchel-Young Inequality; the Block Coordinate Descent algorithm is applied to solve the overall problem and the efficiency enables real-time implementation. As a result, our simulation demonstrates that the proposed control polices can realize benefits in three aspects: (i) net profits gains, (ii) overstay reduction, and (iii) quality-of-service increase.

中文翻译：

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诱导人类行为以最大限度地提高 PEV 充电站的运行性能

插电式电动汽车 (PEV) 充电站服务能力受到充电器可用性和当地变压器容量的物理限制。然而，车站运营绩效已成为提高充电服务可及性的一个越来越重要的因素。在这项工作中，我们提出了一个创新的站级优化框架，以优化定价政策和充电调度来运营充电站。该模型结合了人类行为来明确有效地捕捉驾驶员的充电决策过程。我们提出了一个价格差异化的充电服务菜单，它们的单位价格和能源交付时间表都不同。在循环中涉及人，操作模型还利用了缓解问题的能力逾期逗留当 PEV 的充电会话完成时发生的问题。然后，我们提出了一种多块凸变换方法，通过 Fenchel-Young 不等式重新表述由此产生的非凸问题；采用块坐标下降算法解决整体问题，效率高，实现实时化。因此，我们的模拟表明，所提出的控制策略可以在三个方面实现收益：(i) 净利润收益，(ii) 减少逾期逗留，以及 (iii) 提高服务质量。