Hydrogen Supply Chain Planning with Flexible Transmission and Storage Scheduling

TL;DR

This paper presents a comprehensive hydrogen supply chain planning model that incorporates flexible transmission and storage options, including trucks and pipelines, to optimize costs and production pathways in the U.S. Northeast.

Contribution

It introduces a novel integrated model that captures the flexibility of hydrogen transmission and storage resources, improving decision-making for cost-effective hydrogen supply chain development.

Findings

Flexible scheduling of trucks and pipelines enhances cost competitiveness.

Electrolyzer-based hydrogen production becomes more viable with mobile storage.

The model balances accuracy and computational efficiency effectively.

Abstract

Hydrogen is becoming an increasingly appealing energy carrier, as the costs of renewable energy generation and water electrolysis continue to decline. Developing modelling and decision tools for the H$_{2}$ supply chain that fully capture the flexibility of various resources is essential to understanding the overall cost-competitiveness of H$_{2}$ use. To address this need, we have developed a H$_{2}$ supply chain planning model that determines the least-cost mix of H$_{2}$ generation, storage, transmission, and compression facilities to meet H$_{2}$ demands and is coupled with power systems through electricity prices. We incorporate flexible scheduling for H$_{2}$ trucks and pipeline, allowing them to serve as both H$_{2}$ transmission and storage resources to shift H$_{2}$ demand/production across space and time. The case study results in the U.S. Northeast indicate that the proposed framework for flexible scheduling of H$_{2}$ transmission and storage resources is critical not only to cost minimization but also to the choice of H$_{2}$ production pathways between electrolyzer and centralized natural-gas-based production facilities. Trucks as mobile storage could make electrolyzer more competitive by providing extra spatiotemporal flexibility to respond to the electricity price variability while meeting H$_{2}$ demands. The proposed model also provides a reasonable trade-off between modeling accuracy and solution times.