论文标题
数据驱动的分配在需求和供应不确定性的自动驾驶需求系统中的强大电动汽车平衡
Data-Driven Distributionally Robust Electric Vehicle Balancing for Autonomous Mobility-on-Demand Systems under Demand and Supply Uncertainties
论文作者
论文摘要
电动汽车（EV）由于其经济和社会利益而迅速采用。自动启动按需（AMOD）系统也包含了这一趋势。但是，电动汽车的较长充电时间和高充电频率构成了有效管理EV AMOD系统的挑战。 EV AMOD系统的复杂动态充电和机动性过程使设计车辆平衡算法时的需求和供应不确定性很大。在这项工作中，我们设计了一个数据驱动的分布强大的优化（DRO）方法，以平衡移动性服务和充电过程的电动汽车。优化目标是最大程度地减少乘客出行需求不确定性和电动汽车供应不确定性下最糟糕的预期成本。然后，我们提出了一种新型的分布不确定性集构造算法，该算法保证所产生的参数包含在带有给定概率的所需置信区域中。为了解决所提出的DRO AMOD EV平衡问题，我们得出了同等的计算凸优化问题。根据出租车系统的现实世界电动汽车数据，我们表明，使用解决方案，平均总平衡成本降低了14.49％，与不考虑不确定性的解决方案相比，平均行动性公平和收费公平分别提高了15.78％和34.51％。
Electric vehicles (EVs) are being rapidly adopted due to their economic and societal benefits. Autonomous mobility-on-demand (AMoD) systems also embrace this trend. However, the long charging time and high recharging frequency of EVs pose challenges to efficiently managing EV AMoD systems. The complicated dynamic charging and mobility process of EV AMoD systems makes the demand and supply uncertainties significant when designing vehicle balancing algorithms. In this work, we design a data-driven distributionally robust optimization (DRO) approach to balance EVs for both the mobility service and the charging process. The optimization goal is to minimize the worst-case expected cost under both passenger mobility demand uncertainties and EV supply uncertainties. We then propose a novel distributional uncertainty sets construction algorithm that guarantees the produced parameters are contained in desired confidence regions with a given probability. To solve the proposed DRO AMoD EV balancing problem, we derive an equivalent computationally tractable convex optimization problem. Based on real-world EV data of a taxi system, we show that with our solution the average total balancing cost is reduced by 14.49%, and the average mobility fairness and charging fairness are improved by 15.78% and 34.51%, respectively, compared to solutions that do not consider uncertainties.
