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

2025-05-20

AI Summary

This study addresses the planning of a statewide hydrogen transportation system in Texas from 2025 to 2050. Method: We develop the first comprehensive, multi-period, multi-modal (truck, rail, pipeline) mixed-integer linear programming (MILP) optimization model for Texas, incorporating three methodological innovations: (i) phased infrastructure deployment under delayed-investment constraints; (ii) dynamic lifecycle modeling of transport assets; and (iii) a two-tier adaptive hub-and-spoke architecture based on geospatial clustering. Contribution/Results: By 2050, pipelines are projected to carry 94.8% of hydrogen volume, reducing total logistics costs by 23% compared to an all-truck scenario. However, a one-year delay along critical construction paths reduces pipeline coverage by over 60%, underscoring the importance of temporal coordination. The framework provides a scalable, decision-support tool for large-scale regional hydrogen logistics planning.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Transportation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Designing a Texas-specific hydrogen transport model for 2025-2050
• Addressing infrastructure phasing and multimodal logistics challenges
• Optimizing low-carbon hydrogen delivery under economic constraints

Innovation

• Multi-period mixed integer optimization model
• Phased deployment with delayed investment constraints
• Clustering-based hub structure for hydrogen delivery