On-Line Event-Driven Scheduling for Electric Vehicle Charging via Park-and-Charge
On-Line Event-Driven Scheduling for Electric Vehicle Charging via Park-and-Charge
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DOI：
10.1109/rtss.2016.016
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发表时间：
2016-11
期刊：
影响因子：
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通讯作者：
Fanxin Kong;Qiao Xiang;L. Kong;Xue Liu
中科院分区：
文献类型：
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作者：
Fanxin Kong;Qiao Xiang;L. Kong;Xue Liu
大型充电站成为支持电动汽车快速普及不可或缺的基础设施。它们的运作模式引起了学术界和工业界的高度关注。最近推出了一种有前途的模式，称为停车充电。这种新模式允许客户将电动汽车停放在停车场，并在停车时间内为车辆充电。 V-Charge 项目和通用汽车的 E-Motor 工厂等一些小规模实验已经证明了其潜力。将这种模式部署到大规模站点的一个关键推动因素是有效且高效的充电负荷调度方法。大多数现有工作由于只关注充电服务而仅限于时间驱动的调度策略。将他们的解决方案应用于停车充电模式会危害充电资源的统一化或导致频繁的充电模式切换。这种不适用性促使我们探索在停车收费系统中利用事件驱动的调度策略的可行性和好处。此外，为了更好地表征这种模式下的充电负载，我们建议采用计量模型，系统根据所服务的充电需求按比例获得价值。具体来说，本文的目的是对适用于这种计量模型的事件驱动算法进行理论和实验分析。我们利用竞争分析和资源增强分别证明最早截止日期优先和最高价值优先算法的非恒定和恒定性能界限。此外，我们提供了更强有力的理论结果，即整类工作保护调度算法的性能界限。通过广泛的模拟，我们验证了所提出的理论结果，并通过对模拟结果的深入分析进一步提供了有趣的发现。
Large-scale charging stations become indispensable infrastructure to support the rapid proliferation of electric vehicles. Their operation modes have drawn great attention from both academia and industry. One promising mode called park-and-charge has been recently introduced. This new mode allows customers to park their electric vehicles at a parking lot, where the vehicles are charged during the parking time. Several small-scale experiments, such as the V-Charge project and General Motors' E-Motor plant, have demonstrated its potential. A key enabler for deploying this mode to large-scale stations is effective and efficient charging load scheduling methods. Most existing works confine to the time-driven scheduling policy due to their sole focus on the charging service. Applying their solutions to the park-and-charge mode would jeopardize the unitization of charging resource or cause frequent charging mode switching. This inapplicability motivates us to explore the feasibility and benefits of exploiting the event-driven scheduling policy in park-and-charge systems. Further, to better characterize charging load in this mode, we propose to adopt a metered model, by which a system gains value in proportion to the served charging demand. To be specific, the objective of this paper is to carry out both theoretical and experimental analysis for event-driven algorithms adapted to this metered model. We leverage both the competitive analysis and resource augmentation to demonstrate the non-constant and constant performance bounds for the earliest-deadline-first and highest-value-first algorithms respectively. Moreover, we provide a stronger theoretical result, i.e., the performance bound for the whole class of work-conserving scheduling algorithms. Through extensive simulations, we validate the proposed theoretical results and further provide interesting findings from the in-depth analysis of the simulation results.