Pre-Day Scheduling of Charging Processes in Mobility-on-Demand Systems Considering Electricity Price and Vehicle Utilization Forecasts

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, e.g., 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 macroscopic model to study the tradeoffs between electricity, battery wear and level-of-service costs. In scenarios with varying fleet size and different numbers of

charging units, we compare the performance of several reactive and scheduling policies in a simulation framework based on a macroscopic model. 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.