Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Charging station location planning for electric trucks under demand and grid uncertainty

Céline Pagnier, Tord Gunnar Holen, Thomas Haugen de Lange, Patrick Levin, Steffen J. S. Bakker, Peter Schütz

Abstract

Decarbonizing long-haul freight requires large-scale deployment of high-power charging infrastructure. This paper studies a multi-period charging station location problem that determines where and when to deploy charging capacity for battery-electric heavy-duty vehicles under uncertain future demand and local grid capacity availability. The problem is formulated as a two-stage stochastic mixed-integer program that maximizes covered electric freight flow. Feasible truck routes are generated a priori using a resource-constrained label-setting algorithm that enforces range limitations and driving-break regulations. To solve large-scale instances, an integer L-shaped decomposition method embedded in a branch-and-cut framework and accelerated by a deterministic warm start is implemented. Computational experiments are conducted on a nationwide Norwegian case study based on real candidate locations provided by a charging station operator. The approach solves instances intractable for a monolithic formulation and achieves near-optimal solutions within practical runtimes. For larger networks, the value of the stochastic solution is substantial, highlighting the importance of explicitly modeling uncertainty in long-term infrastructure planning. Optimal investments prioritize major freight corridors in early periods and subsequently reinforce and expand the network. Grid capacity constraints discourage large, concentrated stations and shift deployments toward more distributed layouts. Covered demand increases rapidly at low budget levels but exhibits diminishing returns as the network approaches saturation.

Charging station location planning for electric trucks under demand and grid uncertainty

Abstract

Decarbonizing long-haul freight requires large-scale deployment of high-power charging infrastructure. This paper studies a multi-period charging station location problem that determines where and when to deploy charging capacity for battery-electric heavy-duty vehicles under uncertain future demand and local grid capacity availability. The problem is formulated as a two-stage stochastic mixed-integer program that maximizes covered electric freight flow. Feasible truck routes are generated a priori using a resource-constrained label-setting algorithm that enforces range limitations and driving-break regulations. To solve large-scale instances, an integer L-shaped decomposition method embedded in a branch-and-cut framework and accelerated by a deterministic warm start is implemented. Computational experiments are conducted on a nationwide Norwegian case study based on real candidate locations provided by a charging station operator. The approach solves instances intractable for a monolithic formulation and achieves near-optimal solutions within practical runtimes. For larger networks, the value of the stochastic solution is substantial, highlighting the importance of explicitly modeling uncertainty in long-term infrastructure planning. Optimal investments prioritize major freight corridors in early periods and subsequently reinforce and expand the network. Grid capacity constraints discourage large, concentrated stations and shift deployments toward more distributed layouts. Covered demand increases rapidly at low budget levels but exhibits diminishing returns as the network approaches saturation.
Paper Structure (44 sections, 34 equations, 4 figures, 6 tables)

This paper contains 44 sections, 34 equations, 4 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Literature review
  3. Model formulation
  4. Problem definition
  5. Mathematical formulation
  6. Model variables
  7. Objective function
  8. Location constraints
  9. Spatial constraints
  10. Grid capacity constraints
  11. Flow constraints
  12. Budget constraints
  13. Pre-planned infrastructure
  14. Binary, integer and non-negativity constraints
  15. Solution method
  16. ...and 29 more sections

Figures (4)

  • Figure 1: Norwegian road network and OD nodes.
  • Figure 2: First and second stage decisions for instance 62N-300S (red lines indicate flows, circles indicate CS preparation and installed chargers).
  • Figure 3: Geographical distribution of first-stage decisions of station preparation. Each color/marker-size combination corresponds to one budget level.
  • Figure 4: Relative CO2 emissions to the full diesel benchmark across instances. The shaded areas represent the envelope of CO2 emissions across scenarios for the different instances.