Abstract Summary
Electrifying mobility-on-demand (MoD) fleets is an important step towards a more sustainable transportation system. With increasing fleet size, MoD operators will be able to participate in the energy exchange market and will have access to time-varying electricity prices. They can benefit from intelligent scheduling of charging processes considering forecasts of electricity prices and vehicle utilization. Considering a long time horizon of i.e. a day improves scheduling decisions, but electricity prices change in a short interval of 15 minutes; hence, an optimization-based approach needs to overcome challenges regarding computational time. For this reason, we develop a computationally very efficient model to study the trade-offs between electricity, battery wear and level-of-service costs. In scenarios with varying fleet sizes and different numbers of charging units, we compare the performance of several reactive and scheduling policies. Overall, the results of the study show that an MoD provider with 2000 vehicles could save several thousands of euros in daily operational costs by changing from a state of charge reactive charging strategy to one adapting to the price fluctuations of the electricity exchange market.
