Hydrogen infrastructure planning under uncertainty in an industrial port cluster

Abstract

The industrial sector is one of the most energy consuming and CO2-emitting end-use sectors. To reach climate goals, decarbonization of these industrial sectors is imminent, however, not so apparent. Especially the hard-to-abate industry sectors, such as the chemical, mineral processing, iron, and steel sectors, are difficult to decarbonize as they require either high temperature heat or use fossil fuels as feedstock.

Hydrogen has the potential to reduce carbon emissions in industries such as chemicals, glass, iron and steel, as well as to serve as a cleaner heat source. To reach net-zero emissions by 2050, sectors that currently use fossil fuels for high-temperature processes and as feedstock will likely need to shift towards blue or green hydrogen. Currently, some industrial hydrogen use relies on gray hydrogen, produced from fossil fuels and contributing to emissions. In contrast, blue hydrogen captures and stores CO2 produced from fossil sources, while green hydrogen is entirely emissions-free, generated from renewable energy. In other words, some processes need to change from gray to green/blue but most of them need to change from other fossil fuel based processes to hydrogen processes.

Hydrogen offers a way to cut carbon emissions in industries like chemicals, glass, iron, and steel, and can also act as a cleaner heat source. Achieving net-zero emissions by 2050 will likely require sectors that currently depend on fossil fuels for high-temperature applications and feedstocks to adopt blue or green hydrogen instead. Today, certain industrial applications still use gray hydrogen, derived from fossil fuels and contributing to carbon emissions. However, blue hydrogen captures and stores the CO2 generated, while green hydrogen is emissions-free, produced using renewable energy. In essence, some processes will need to transition from gray to blue or green hydrogen, while many others will shift from fossil-fuel-based processes to hydrogen-based alternatives.

However, the timing and extent of the hydrogen transition are uncertain as they are heavily influenced by external factors such as hydrogen prices, available subsidies, and alternative decarbonization options. Additionally, industrial plant owners may be reluctant to disclose decarbonization plans due to competitive pressures, adding another layer of demand and participant uncertainty that complicates infrastructure planning.

This thesis addresses the planning of hydrogen infrastructure within an industrial port cluster (IPC). IPCs are defined by their proximity to water and concentration of industrial activities related to a specific sector. In order to effectively address spatial constraints, this thesis will plan the hydrogen networks along the current road network within IPCs. Current infrastructure planning methods have a time horizon of ten years. However, as the expectation is that the hydrogen demand will increase towards 2055, a time horizon of ten years can increase the total costs of the network when the network is implemented over time between 2025-2055. This introduces the following research question:

“How can a cost-efficient, robust pipeline network for an industrial port cluster be developed over time under uncertainty?”

To answer this question, the robust backtracking planning method (RBPM) is developed. This method aims to minimize costs over the 2025-2055 time frame while facilitating the hydrogen to the demanding plants. Because the demand for hydrogen is likely to grow over time, this method finds a robust network that is able to facilitate the demand in many possible future demand scenarios of 2055.

The robust network is then implemented incrementally for 2035, 2045, and 2055 using a backtracking approach. In this context, backtracking means that when an industrial plant transitions to hydrogen in one stage, pipelines are installed with the robust networks’ capacity, rather than just the minimum required to meet that plant’s immediate needs. This extra capacity ensures that if other plants transition in later years, the existing network can accommodate the increased demand without needing costly pipeline extensions. By preemptively building capacity, this approach reduces future installation costs and enhances the network’s ability to adapt to evolving demand patterns.

The RBPM is tested on simulations of multiple simplified IPCs. By testing different IPC simulations, it is studied how the difference in industrial plants determines the development of the network. The RBPM is compared to the results of a traditional planning approach which only plans the networks with a time horizon of ten years.

The results show that the RBPM incurs lower costs over 30 years, but it requires a higher investment in 2035 due to the greater capacity installed at that time. This thesis finds that the total potential hydrogen demand and the physical size of an IPC significantly affect the performance of the RBPM compared to the traditional planning approach. Additionally, the projected installation and operational costs over time also impact the RBPM’s performance relative to the traditional planning approach.

For IPCs with comparatively low hydrogen demand — typically clusters with fewer iron and steel facilities, chemical plants, or refineries — the RBPM emerges as the most economical approach. This method requires only slightly higher investment by 2035 but ultimately generates substantial savings by 2055. By installing sufficient pipeline capacity upfront, the RBPM avoids the need for additional pipelines every ten years, leading to long-term cost efficiency through 2055.

For IPCs with high hydrogen demand—typically found in iron and steel plants, basic chemical plants, or refineries—the initial installation costs and ongoing operational expenses of RBPM make it less advantageous. While RBPM may offer slightly better economic profitability over a 30-year period, the substantial investment required in 2035 compared to traditional planning makes implementation challenging due to budget constraints. In these high-demand clusters, the decision between RBPM and the traditional approach for developing a hydrogen pipeline network depends on the cluster’s budget, anticipated future installation costs, and projected operational expenses over time.

Opportunities for further research include the application of the RBPM to a real case study to validate the result, increasing the amount of possible future scenarios by incorporating uncertainty in installation and operating costs and increasing the demand and participant uncertainty range. Lastly, another research direction to explore is the generation of different robust network methods and their performance.