Fleets of electric vehicles will likely shift electricity demand, and the effect of upstream charging emissions will come from generation sources that are dispatched in response. This study proposes a multi-stage charging and discharging problem to translate low-cost energy transactions into vehicle dispatch decisions. A day-ahead charging optimization problem minimizes electricity purchases and marginal emissions damages, with energy transactions becoming targets in an optimization-based dispatch strategy for an on-demand shared autonomous electric vehicle (SAEV) fleet. The framework was tested for Austin, Texas, using an agent-based simulator. Fleets can schedule charging to lower daily power costs (averaging 15.5% or $0.79/day/SAEV) while reducing health damages from generation-related pollution (2.8% or $0.43/day/SAEV). Fleet managers can increase profits ($8 per SAEV per day) by adopting a multi-stage charging and discharging strategy that can serve more passengers per day than price-agnostic dispatch strategies.

电动汽车车队可能会转移电力需求，而上游充电排放的影响将来自响应调度的发电来源。本研究提出了一个多阶段充电和放电问题，以将低成本能源交易转化为车辆调度决策。日前充电优化问题最大限度地减少电力购买和边际排放损害，能源交易成为按需共享自动驾驶电动汽车 (SAEV) 车队基于优化的调度策略的目标。该框架已使用基于代理的模拟器在德克萨斯州奥斯汀市进行了测试。车队可以安排充电以降低每日电力成本（平均 15.5% 或 0.79 美元/天/SAEV），同时减少与发电相关的污染对健康造成的损害（2.8% 或 0.43 美元/天/SAEV）。