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Optimization of shared autonomous electric vehicles operations with charge scheduling and vehicle-to-grid

机译：通过充电调度和车辆到电网优化共享自动驾驶汽车的运行

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摘要

Shared autonomous electric vehicles, also known as autonomous mobility on demand systems, are expected to become commercially available by the next decade. In this work we propose a methodology for the optimization of their charging with vehicle-to-grid in parallel with optimized routing and relocation. The methodology presented is based on previous work expanded to include charge optimization. The proposed model optimizes transport service and charging at two different time scales by running two model-predictive control optimization algorithms in parallel. Charging is optimized over longer time scales to minimize both approximate waiting times and electricity costs. Routing and relocation are optimized at shorter time scales to minimize waiting times, with the results of the long-time-scale optimization as charging constraints. This approach allows efficient optimization of both aspects of system operation. The problem is solved as a mixed-integer linear program. A case study using transport and electricity price data for Tokyo is used to test the model. Results show that the system can substantially reduce charging costs without significantly affecting waiting times, with cost reduction dependent on electricity price variability. Vehicle-to-grid is shown to be unsuitable for current electricity and battery prices, however offering substantial savings with price profiles with higher variability.
机译：共享的自动驾驶电动汽车，也称为自动驾驶随需应变系统，预计将在未来十年内商用。在这项工作中，我们提出了一种方法，用于优化车辆到电网的充电，同时优化路线和重新安置。提出的方法基于先前的工作，扩展到包括电荷优化。所提出的模型通过并行运行两种模型预测控制优化算法，在两个不同的时间尺度上优化运输服务和收费。在更长的时间范围内优化了充电，以最大程度地减少等待时间和电费。路由和重定位在更短的时间范围内进行了优化，以最大程度地减少等待时间，而长期时间范围内的优化结果则成为了收费约束。这种方法可以有效地优化系统操作的两个方面。该问题通过混合整数线性程序解决。使用东京运输和电价数据的案例研究对模型进行了测试。结果表明，该系统可以显着降低充电成本，而不会显着影响等待时间，而成本的降低取决于电价的可变性。车辆到电网已证明不适合当前的电价和电池价格，但是价格差异较大，可节省大量费用。

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