Poster 33: Impact of Time-Efficient Vs. Energy-Efficient Route Selection of Ride-Hailing Automated Electric Vehicle Fleets on Charging Station Load Profile
(Room Palms Ballroom)
16 Jul 19
5:30 PM
-
7:00 PM
Tracks:
Energy and Environment
To optimally dispatch the ride hailing AEVs and make it feasible for real world implementation, the transportation models and power distribution models need to be co-simulated. This co-simulation bridges the gap between isolated transportation studies and isolated distribution system in the context of market penetration of EVs and AEVs. Addressing this problem, this paper proposes a co-simulation of transportation and electric grid modeling for AEVs. The transportation network and actual charging station locations for San Francisco will be modelled using BEAM. In this effort, a full-scale transportation simulation exercise will be carried out using BEAM. Then, for each given trip, multiple possible routes will be enumerated based on time required completion of the trip. Then energy required to traverse each of the multiple routes that are enumerated in the previous step will be calculated used a proprietary energy estimation model developed at the National Renewable Energy Laboratory (RouteE). Next, the routes with least energy consumption will be selected and the time required for the ride-hailing AEV to complete trips with those route selections will be calculated. This will help us compare and contrast the case of time-efficient and energy-efficient route selection, based on the required number of charging breaks taken by AEVs and charging power provided to AEVs in that 24-hour ride-hailing operation. The analysis for these case studies would enable the quantification of the impact of ride-hailing AEV fleet operation on charging power requirements of commercial fast charging stations. Furthermore, it would assist in analyzing number of trips completed for same number of OD pairs in both test cases. This would further help in optimizing ride-hailing dispatch for optimal power distribution system operation.