Designing a Multi-Period Model for Economic and Low-Carbon Hydrogen Transportation in Texas

TL;DR

This paper develops a Texas-specific multi-period optimization model for hydrogen transportation infrastructure, demonstrating how phased deployment and hub clustering can optimize costs and logistics from 2025 to 2050.

Contribution

It introduces a novel multi-period mixed integer optimization framework with phased deployment, fleet aging, and adaptive hub clustering for hydrogen logistics.

Findings

Pipeline deployment can support up to 94.8% of hydrogen flow by 2050.

Pipeline delays over 1 year significantly reduce coverage and increase reliance on road transport.

The model's modular design is adaptable to other regions and policy scenarios.

Abstract

The transition to hydrogen powered transportation requires regionally tailored yet scalable infrastructure planning. This study presents the first Texas specific, multi-period mixed integer optimization model for hydrogen transportation from 2025 to 2050, addressing challenges in infrastructure phasing, asset coordination, and multimodal logistics. The framework introduces three innovations: (1) phased deployment with delayed investment constraints, (2) dynamic modeling of fleet aging and replacement, and (3) a clustering-based hub structure enabling adaptive two-stage hydrogen delivery. Simulations show pipeline deployment supports up to 94.8% of hydrogen flow by 2050 under high demand, reducing transport costs by 23% compared to vehicle-based systems. However, one-year construction delays reduce pipeline coverage by over 60%, shifting reliance to costlier road transport. While the study focuses on Texas, its modular design and adaptable inputs apply to other regions. It provides a tool for policy makers and stakeholders to manage hydrogen transitions under logistical and economic constraints.