Scheduling Battery-Electric Bus Charging under Stochasticity using a Receding-Horizon Approach


A significant challenge of adopting battery electric buses into fleets lies in scheduling the charging, which in turn is complicated by considerations such as timing constraints imposed by routes, long charging times, limited numbers of chargers, and utility cost structures. This work builds on previous network-flow-based charge scheduling approaches and includes both consumption and demand time-of-use costs while accounting for uncontrolled loads on the same meter. Additionally, a variable-
rate, non-linear partial charging model compatible with the mixed-integer linear program (MILP) is developed for increased charging fidelity. To respond to feedback in an uncertain environment, the resulting MILP is adapted to a hierarchical receding
horizon planner that utilizes a static plan for the day as a reference to follow while reacting to stochasticity on a regular basis. This receding horizon planner is analyzed with Monte-Carlo techniques alongside two other possible planning methods. It is found to provide up to 52% cost savings compared to a non-time-of-use aware method and significant robustness benefits compared to an optimal open-loop method.

See publication:
This publication pertains to:
Charging Stations
Publication Authors:
  • Justin Whitaker
  • Derek Redmond
  • Greg Droge
  • Jake Gunther
It appeared in:
Peer-reviewed technical journal
Integer linear program, optimization, optimal scheduling, green transportation, batteries, power demand