  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.
