Incentivized self-rebalancing fleet in electric vehicle sharing
Incentivized self-rebalancing fleet in electric vehicle sharing
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DOI：
10.1080/24725854.2021.1928340
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发表时间：
2021-05
影响因子：
2.6
通讯作者：
Yuguang Wu;Minmin Chen;Xin Wang
中科院分区：
文献类型：
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作者：
Yuguang Wu;Minmin Chen;Xin Wang
随着人们对高效、灵活的城市短途交通需求的不断增长，越来越多的汽车共享公司开始提供单向汽车共享服务。电动汽车共享系统在减少燃料消耗和碳排放方面更为有效。本文研究了时变随机需求和电价下电动汽车共享系统的动态车队管理问题。需求在每个时期都是弹性的，对宣布的价格作出反应。为了实现收益最大化，电动汽车车队动态优化出行定价和电动汽车调度决策。我们开发了一种新的值函数逼近与输入凸神经网络产生高质量的解决方案。通过对纽约市的一个案例研究，我们将其与标准的动态规划方法进行了比较，并对电动汽车车队与电网之间的相互作用产生了见解。
Abstract With the rising need for efficient and flexible short-distance urban transportation, more vehicle sharing companies are offering one-way car-sharing services. Electrified vehicle sharing systems are even more effective in terms of reducing fuel consumption and carbon emission. In this article, we investigate a dynamic fleet management problem for an Electric Vehicle (EV) sharing system that faces time-varying random demand and electricity price. Demand is elastic in each time period, reacting to the announced price. To maximize the revenue, the EV fleet optimizes trip pricing and EV dispatching decisions dynamically. We develop a new value function approximation with input convex neural networks to generate high-quality solutions. Through a New York City case study, we compare it with standard dynamic programming methods and develop insights regarding the interaction between the EV fleet and the power grid.