Trading Off Energy Storage and Payload -- An Analytical Model for Freight Train Configuration

TL;DR

This paper presents an analytical model to optimize the number of energy storage tender cars in freight trains, balancing costs and operational constraints to support decarbonization efforts in rail transport.

Contribution

It introduces a convex optimization framework with a closed-form solution for determining optimal energy tender car configurations in freight trains, considering market and technology factors.

Findings

Optimal configurations vary across different freight markets.

Lighter, time-sensitive shipments use more battery tender cars.

Heavier commodities typically require fewer tender cars.

Abstract

To support planning of alternative fuel technology (e.g., battery-electric locomotives) deployment for decarbonizing non-electrified freight rail, we develop a convex optimization formulation with a closed-form solution to determine the optimal number of energy storage tender cars in a train. The formulation shares a similar structure to an Economic Order Quantity (EOQ) model. For given market characteristics, cost forecasts, and technology parameters, our model captures the trade-offs between inventory carrying costs associated with trip times (including delays due to charging/refueling) and ordering costs associated with train dispatch and operation (energy, amortized equipment, and labor costs). To illustrate the framework, we find the optimal number of battery-electric energy tender cars in 22,501 freight markets (origin-destination pairs and commodities) for U.S. Class I railroads. The results display heterogeneity in optimal configurations with lighter, yet more time-sensitive shipments (e.g., intermodal) utilizing more battery tender cars. For heavier commodities (e.g., coal) with lower holding costs, single battery tender car configurations are generally optimal. The results also show that the optimal train configurations are sensitive to delays associated with recharging or swapping tender cars.