Conference Agenda

Shared-use infrastructures, such as CCUS and hydrogen pipeline networks, are expected to be key developments for achieving a low-carbon energy transition by facilitating the decarbonisation of industrial sites and reducing emissions across sectors. Nevertheless, their construction and uptake remain stalled (IEA, 2023; IEA, 2024). Some studies attribute the stagnation to circular dependency in infrastructure dynamics (Brozynski and Leibowicz, 2022), where stakeholders each wait for others to act first. This stalemate creates decision paralysis and deep uncertainties for stakeholders, compounded by the variety of options available for industrial sites, the anticipated first customers of these infrastructures.

These uncertainties are particularly evident in network planning and design, where the location and timing of uptake are unknown. In the literature, pipeline infrastructure is often designed over multiple time periods as a component of a centralised planning strategy where site, equipment, and network planning decisions are determined within an optimisation formulation (Becattini et al., 2022). However, in reality, network planning strategy remains flexible to changes in stakeholder behaviour and mitigates against the risks associated with independent actors’ decisions.

Building on the authors’ previous work (Hammond et al., 2024) using the Steiner tree with Obstacles Genetic Algorithm (StObGA) to route networks across complex cost surfaces, this study proposes a novel modification. The updated algorithm enables infrastructure networks to evolve iteratively, incorporating previous design iterations and adapting to industrial site decisions as they emerge. This offers a flexible and dynamic network planning approach in uncertain environments.

The novel approach is applied to market-growth scenarios in the Humber industrial cluster in the UK, where many large emitters and potential hydrogen consumers reside. The method’s effectiveness is evaluated over a rolling horizon, providing insights into the impact of internal stakeholder decision-making on the development and costs of transitional infrastructures.

References

IEA (2023). Hydrogen production and infrastructure projects database. [online] Available at: https://www.iea.org/data-and-statistics/data-product/hydrogen-production-and-infrastructure-projects-database [Accessed 16 Jul. 2024].

IEA (2024). CCUS projects database. [online] Available at: https://www.iea.org/data-and-statistics/data-product/ccus-projects-database [Accessed 16 Sep. 2024].

Hammond, J., Rosenberg, M. and Brown, S. (2024). A genetic algorithm-based design for hydrogen pipeline infrastructure with real geographical constraints. In: Proceedings of the 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering (ESCAPE34/PSE24) Computer Aided Chemical Engineering, Vol. 53, Springer, pp. 631-636. Available at: https://doi.org/10.1016/B978-0-443-28824-1.50106-X

Becattini, V., Gabrielli, P., Antonini, C., Campos, J., Acquilino, A., Sansavini, G. and Mazzotti, M. (2022). Carbon dioxide capture, transport and storage supply chains: Optimal economic and environmental performance of infrastructure rollout. International Journal of Greenhouse Gas Control, [online] 112, p.103635. Available at: https://doi.org/10.1016/j.ijggc.2022.103635.

Brozynski, M.T. and Leibowicz, B.D. (2022). A multi-level optimization model of infrastructure-dependent technology adoption: Overcoming the chicken-and-egg problem. European Journal of Operational Research, 300, pp.755-770.

Fuel cells are devices that convert chemical energy into electrical energy via transfer of electrons and protons. They possess key characteristics such as high efficiency, high power density, low corrosion, low emissions and moderate operating temperatures, making them highly favorable in the automotive industry. The high power demand of a vehicle often requires several cells that are arranged in a single or multiple ‘Fuel Cell Stacks’. However, each stack may exhibit different efficiencies varying in time due to differences in operating conditions and the rate of cell aging. The efficiency of a stack directly impacts the amount of hydrogen consumed to meet a given power demand. Therefore, careful power distribution between multiple fuel cell stacks is essential to minimize the total hydrogen consumption while fulfilling the vehicle’s power requirements. In this study, we consider two fuel cell stacks, each consisting of 65 parallel cells with different efficiency profiles (i.e., low and high). The fuel cell stacks are modeled using first principles and simulated by MATLAB Simulink. In a given drive cycle, the power demand changes as a funcion of time. A constrained optimization is performed, in which hydogen consumption is minimized by optimally distributing the power to the individual stacks while meeting the total demand. For proper power management each fuel stack has its own power controller which manipulates the stack current to control the stack power at its desired-set point. Computed power values from optimization constitute the desired set-points for the local power PID controllers of the individual stacks. Closed-loop simulations are perfomed by simulating the developed mechanistic model together with optimization and PID controllers in SIMULINK platform. The closed loop simulations demonstrate how well the power demand f the drive cycle is tracked and the hydrogen consumption is minimized. To assess the impact of optimization strategy used in this study, hydrogen consumption is compared to that of equivalent and a daisy-chain power sharing strategies. The results demonstrate that the hydrogen consumption minimization strategy effectively reduces total hydrogen consumption.

The economic and environmental viability of clean (or low-carbon) hydrogen production through water electrolysis, depends on reduction in electricity costs and carbon emissions. To tackle this challenge, we apply demand side management (DSM) to a proton exchange membrane (PEM) electrolysis system, considering the temporal variation of hourly electricity prices and carbon footprints. DSM is further combined with the purchase of renewable energy certificates and the utilization of curtailed electricity to enhance sustainability. We formulate an optimization problem based on the historical data in Jeju Island in South Korea, where renewable energy resources are plentiful. The objective is to minimize the levelized cost of hydrogen (LCOH) while ensuring the compliance with the Clean Hydrogen certification system in South Korea (less than 4 kg-CO₂eq/kg-H₂) by optimizing the hourly operation level. As a result, the optimal operation leads to meeting the carbon footprint requirements while reducing LCOH to below the hydrogen sales price (9,900 KRW/kg-H₂).

Green hydrogen is commonly regarded as a key player in the transition towards a low-carbon future [1]. It can be efficiently produced in regions with abundant renewable resources, which are often remote and distant from major consumption centers. Therefore, the efficient and cost-effective transportation of H₂ from production hubs to end users is essential. However, H₂ has an extremely low volumetric density at ambient conditions. To overcome this issue, hydrogen carriers, such as liquefied hydrogen and ammonia, are being explored for its transportation on a large-scale [2, 3].

This study assesses the energy consumption involved in the supply chain of these carriers, focusing on the processes of hydrogen conversion to the carrier and its reconversion back to hydrogen. The analysis employs on the “net equivalent hydrogen” method, which is similar to the widely adopted “net equivalent methane” method [4]. This approach evaluates the equivalent amount of hydrogen that would need to be burned to power specific equipment. By using this unified energy basis, the method allows for a fair comparison between different processes. The net delivered hydrogen is then calculated by subtracting both the net equivalent hydrogen consumed and the hydrogen lost due to the boil-off phenomenon during storage and shipping from the total hydrogen fed into the plant.

A sensitivity analysis is performed on the assumptions used in designing hydrogen supply chains. Different scenarios are examined, considering variable harbor-to-harbor distances and diverse hydrogen end uses. The study identifies the optimal carrier for each case study and highlights critical issues to guide future large-scale implementations.

References

[1] Pellegrini LA, Spatolisano E, Restelli F, De Guido G, de Angelis AR, Lainati A. Green H2: One of the Allies for Decarbonization. In: Pellegrini LA, Spatolisano E, Restelli F, De Guido G, de Angelis AR, Lainati A, editors. Green H2 Transport through LH2, NH3 and LOHC: Opportunities and Challenges. Cham: Springer Nature Switzerland; 2024. p. 1-6.

[2] Restelli F, Spatolisano E, Pellegrini LA, Cattaneo S, De Angelis AR, Lainati A, et al. Liquefied hydrogen value chain: a detailed techno-economic evaluation for its application in the industrial and mobility sectors. International Journal of Hydrogen Energy. 2024;52:454-66.

[3] Aziz M, Wijayanta AT, Nandiyanto ABD. Ammonia as effective hydrogen storage: A review on production, storage and utilization. Energies. 2020;13:3062.

[4] Pellegrini LA, De Guido G, Valentina V. Energy and exergy analysis of acid gas removal processes in the LNG production chain. Journal of Natural Gas Science and Engineering. 2019;61:303-19.

Due to uncertainty associated with weather forecasting, the production of green hydrogen for energy export requires a broad arsenal of systems engineering modelling tools to derive a realistic view of future prospects. Due to the dearth of full-scale hydrogen export facilities and limited pilot systems in operation, there is currently a substantial impetus to rigorously optimise, forecast and rank different scenarios and design options for the supply chain.

Here we present the findings of a non-deterministic green hydrogen production model. This operations model is focused on synthesising hydrogen for energy export, thus employing either liquefaction or the Haber-Bosch process to convert the hydrogen to a format that is suitable for long-distance transport onboard ships. This model expands on existent research efforts, which primarily employ deterministic optimisation models to derive optimal value chain performance under fixed weather profiles of a year’s ‘representative operation’, which are assumed to repeat for the facility’s entire lifetime. Whilst such analyses derive key findings, especially those which employ temporally distributed meteorological profiles, these Mixed-Integer supply chain models have perfect foresight, so can make unrealistic premeditated actions in response to knowledge of future events that, in reality, would not be known a-priori.

This non-deterministic operations model describes a months’ operation of an islanded hydrogen export facility located in Chile, with an objective of maximising the production rate from the liquefaction and Haber-Bosch process, respectively. In the base case, strategic operational decisions are made on a weekly basis, assuming a perfect meteorological forecast of the next week’s weather data is available. These findings are compared to a counterfactual deterministic supply chain model, which has also been used to generate equipment sizes for the non-deterministic model, subject to a cost-minimisation objective. Such a comparison allows for the robustness of the findings of these deterministic models to be analysed for the first time, especially regarding production volumes, storage requirements, equipment availability and the quantity of supplementary energy purchased from the grid. The robustness to such considerations is crucial, given the capital intense nature of certain technologies, such as hydrogen storage at $500,000 USD tonne^-1.

When subject to the stochastic system, the average availability of the liquefaction plant fell by 20% and Haber-Bosch processes fell by 30%. As this reduction in throughput is symptomatic of the ‘overoptimized’ nature of the deterministic model, current equipment specifications from deterministic studies are likely to see suboptimal performance in real systems. Furthermore, whilst many studies consider the production of hydrogen in either ‘islanded’ or ‘grid-connected’ mode, thereby deriving different equipment sizes for each scenario, it is likely that real systems will need the inherent flexibility to operate in both modes for periods of time. As such, we present a series of pareto fronts, demonstrating the influence of different configurational limitations on the output of the production facility.