Grid-Aware Charging and Operational Optimization for Mixed-Fleet Public Transit

TL;DR

This paper develops a MILP-based optimization model for managing mixed electric and diesel bus fleets, focusing on dynamic electricity pricing and operational constraints to reduce costs in urban transit.

Contribution

It introduces a hierarchical MILP approach for joint charging and trip assignment optimization in mixed fleets considering real-world pricing and constraints.

Findings

Significant cost savings demonstrated with real-world data

Effective handling of dynamic electricity prices

Hierarchical approach improves computational tractability

Abstract

The rapid growth of urban populations and the increasing need for sustainable transportation solutions have prompted a shift towards electric buses in public transit systems. However, the effective management of mixed fleets consisting of both electric and diesel buses poses significant operational challenges. One major challenge is coping with dynamic electricity pricing, where charging costs vary throughout the day. Transit agencies must optimize charging assignments in response to such dynamism while accounting for secondary considerations such as seating constraints. This paper presents a comprehensive mixed-integer linear programming (MILP) model to address these challenges by jointly optimizing charging schedules and trip assignments for mixed (electric and diesel bus) fleets while considering factors such as dynamic electricity pricing, vehicle capacity, and route constraints. We address the potential computational intractability of the MILP formulation, which can arise even with relatively small fleets, by employing a hierarchical approach tailored to the fleet composition. By using real-world data from the city of Chattanooga, Tennessee, USA, we show that our approach can result in significant savings in the operating costs of the mixed transit fleets.