论文标题
通过灵活的服务操作进行电动汽车费用计划
Electric vehicle charge scheduling with flexible service operations
论文作者
论文摘要
部署大型电动汽车的运营商通常会面临一个具有挑战性的费用调度问题。具体而言,时间友好的充电操作限制了在服务操作期间充电的盈利能力,以使操作员在中央仓库下车充电车辆。在这里,高投资成本和电网容量限制可用的收费基础设施,因此运营商需要安排充电运营以保持车队运营。在这种情况下,灵活的服务操作,即允许延迟或加速车辆出发,可能会增加充电器利用率。除此之外,共同安排充电和服务操作有望通过更好地利用使用时间关税和精心制作的充电时间表来节省运营成本,旨在最大程度地减少电池磨损。在此背景下,我们研究了由此产生的联合充电和服务操作调度问题问题,以解决电池降解,非线性充电和使用时间关税的问题。我们提出了一种精确的分支机构算法,利用自定义分支规则和原始的启发式,以在分支和绑定阶段保持效率。此外,我们为我们的定价问题开发了一种精确的标签算法,构成了资源约束的最短路径问题,该问题考虑了可变的能源价格和非线性充电操作。我们在一项全面的数值研究中基准了算法,并表明它可以以低于一个小时的计算时间来解决现实规模的问题实例,从而在实践中实现其应用。此外,我们分析了共同调度充电和服务操作的好处。我们发现,我们的集成方法降低了多达57%所需的充电基础设施量,除了可节省高达5%的运营成本。
Operators who deploy large fleets of electric vehicles often face a challenging charge scheduling problem. Specifically, time-ineffective recharging operations limit the profitability of charging during service operations such that operators recharge vehicles off-duty at a central depot. Here, high investment cost and grid capacity limit available charging infrastructure such that operators need to schedule charging operations to keep the fleet operational. In this context, flexible service operations, i.e. allowing to delay or expedite vehicle departures, can potentially increase charger utilization. Beyond this, jointly scheduling charging and service operations promises operational cost savings through better utilization of Time-of-Use energy tariffs and carefully crafted charging schedules designed to minimize battery wear. Against this background, we study the resulting joint charging and service operations scheduling problem accounting for battery degradation, non-linear charging, and Time-of-Use energy tariffs. We propose an exact Branch & Price algorithm, leveraging a custom branching rule and a primal heuristic to remain efficient during the Branch & Bound phase. Moreover, we develop an exact labeling algorithm for our pricing problem, constituting a resource-constrained shortest path problem that considers variable energy prices and non-linear charging operations. We benchmark our algorithm in a comprehensive numerical study and show that it can solve problem instances of realistic size with computational times below one hour, thus enabling its application in practice. Additionally, we analyze the benefit of jointly scheduling charging and service operations. We find that our integrated approach lowers the amount of charging infrastructure required by up to 57% besides enabling operational cost savings of up to 5%.