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

TL;DR

This paper presents a stochastic optimization approach for planning electric truck charging stations, accounting for demand and grid uncertainties, to efficiently deploy infrastructure on major freight corridors.

Contribution

It introduces a two-stage stochastic mixed-integer programming model with a novel decomposition method for large-scale, real-world freight network planning under uncertainty.

Findings

The approach effectively solves large, complex instances within practical runtimes.

Explicit modeling of uncertainty significantly impacts optimal infrastructure deployment.

Optimal investments focus on major corridors initially, then expand to reinforce the network.

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.