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

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.