Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Rishav Sen, Amutheezan Sivagnanam, Aron Laszka, Ayan Mukhopadhyay, Abhishek Dubey

TL;DR

This work addresses the challenge of efficiently operating a mixed fleet of electric and diesel public buses under dynamic electricity pricing while honoring seating and route constraints. It develops a mixed-integer linear programming model that jointly optimizes charging schedules and trip assignments, and it introduces a hierarchical decomposition to scale to realistic fleet sizes. The approach, evaluated on CARTA data, yields consistent operating-cost savings (average around 6.25% versus actual schedules) and significant CO$_2$ reductions, with additional gains from increasing EV shares. The combination of a hierarchical MILP, warm-starting heuristic, and real-world data demonstrates a practical pathway for transit agencies to transition toward sustainable, cost-effective service.

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.

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

TL;DR

This work addresses the challenge of efficiently operating a mixed fleet of electric and diesel public buses under dynamic electricity pricing while honoring seating and route constraints. It develops a mixed-integer linear programming model that jointly optimizes charging schedules and trip assignments, and it introduces a hierarchical decomposition to scale to realistic fleet sizes. The approach, evaluated on CARTA data, yields consistent operating-cost savings (average around 6.25% versus actual schedules) and significant CO reductions, with additional gains from increasing EV shares. The combination of a hierarchical MILP, warm-starting heuristic, and real-world data demonstrates a practical pathway for transit agencies to transition toward sustainable, cost-effective service.

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.
Paper Structure (18 sections, 20 equations, 7 figures, 4 algorithms)

This paper contains 18 sections, 20 equations, 7 figures, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related Research
  3. Problem Model
  4. Solution Space
  5. Objective
  6. Hierarchical Approach
  7. Level 1
  8. Level 2
  9. Maintaining Feasibility
  10. Warm start using initial solution
  11. Experiments
  12. Data Preparation
  13. Baselines
  14. Multiple Days Analysis
  15. Impact of Increasing Electric Vehicles (EVs)
  16. ...and 3 more sections

Figures (7)

  • Figure 1: Comparison of all methods across Feb 2024 for actual block assignments used by CARTA. With a maximum fleet size of 4 electric and 31 diesel buses, which can vary daily. The electric buses have a battery capacity of 310 kWh, and 2 chargers with a maximum charging rate of 80 kWh. We compare the operational cost for the Hierarchical and the baseline models. Hierarchical provides the best operational cost for all days
  • Figure 2: Effect of changing the number of electric buses for 2024-02-16, which has 47 blocks. The number of diesel buses are decreased proportionately to maintain a total available fleet size of 27
  • Figure 3: Effect of changing the electric bus battery capacity on the operational cost
  • Figure 4: Effect of changing the maximum charging rate of the chargers on the operational cost
  • Figure 5: Effect of changing the diesel cost per gallon on the diesel bus usage (in terms of gallons used). The operational cost increases due to the increase in diesel cost.
  • ...and 2 more figures