1887

Abstract

Summary

This paper proposes a novel mathematical optimization methodology for the conceptual design of an offshore wind-based ammonia plant (OWAP), aiming to provide critical insights for determining utility-scale specifications for renewable ammonia production. The study assumes the OWAP is located in the East Sea gas field, South Korea, utilizing DTU 10 MW turbines, with power outputs precomputed using the Jensen wake model.

The problem formulation considers a PEM electrolyzer with high responsiveness to power fluctuations, where produced hydrogen is directed to the Haber-Bosch line or pressurized storage tanks. Importantly, operational specifications of the OWAP facilities are defined as decision variables.

Results indicate that battery capacity significantly impacts the Levelized Cost of Ammonia (LCOA) and ammonia production yield. Hydrogen tanks also play a crucial role as an alternative storage medium. Furthermore, sensitivity analysis highlights the hydrogen compression system’s performance as highly sensitive to hydrogen tank capacity.

The study concludes by identifying key factors influencing the economic viability of OWAP due to its nonlinear cost structure. A key contribution involves examining the complex interplay of equipment and process elements within renewable ammonia production, thereby yielding vital insights for defining utility-grade specifications.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.202521042
2025-10-27
2026-01-12
Loading full text...

Full text loading...

References

  1. Daiyan, R., MacGill, I., and Amal, R. [2020]. Opportunities and challenges for renewable power-to-X. 3843–3847.
    [Google Scholar]
  2. Navingo [2023]. KRISO secures ABS’ approval for its offshore hydrogen and ammonia production platform, Available at: https://https://www.offshore-energy.biz/kriso-secures-abs-approval-for-its-offshore-hydrogen-and-ammonia-production-platform/ (Accessed: 11 June 2025).
    [Google Scholar]
  3. Lee, N., Woo, J., and Kim, S. [2025]. A deep reinforcement learning ensemble for maintenance scheduling in offshore wind farms. Applied Energy, 377, 124431.
    [Google Scholar]
  4. Bak, C., Zahle, F., Bitsche, R., Kim, T., Yde, A., Henriksen, L.C., Hansen, M.H., Blasques, J.P.A.A., Gaunaa, M., and Natarajan, A. [2013]. The DTU 10-MW reference wind turbine. In Danish wind power research.
    [Google Scholar]
  5. Wu, C., Yang, X., and Zhu, Y. [2021]. On the design of potential turbine positions for physics-informed optimization of wind farm layout. Renewable Energy, 164, 1108–1120.
    [Google Scholar]
  6. Kim, J., Qi, M., Park, J., and Moon, I. [2023]. Revealing the impact of renewable uncertainty on grid-assisted power-to-X: A data-driven reliability-based design optimization approach. Applied Energy, 339, 121015.
    [Google Scholar]
  7. Kim, S., Park, J., Heo, S., and Lee, J. H. [2024]. Green hydrogen vs green ammonia: A hierarchical optimization-based integrated temporal approach for comparative techno-economic analysis of international supply chains. Journal of Cleaner Production, 465, 142750.
    [Google Scholar]
  8. Park, J., Kang, S., Kim, S., Cho, H. S., Heo, S., and Lee, J. H. [2024]. Techno-economic analysis of solar powered green hydrogen system based on multi-objective optimization of economics and productivity. Energy Conversion and Management, 299, 117823.
    [Google Scholar]
  9. Sayed-Ahmed, H., Toldy, Á. I., and Santasalo-Aarnio, A. [2024]. Dynamic operation of proton exchange membrane electrolyzers—Critical review. Renewable and Sustainable Energy Reviews, 189, 113883.
    [Google Scholar]
/content/papers/10.3997/2214-4609.202521042
Loading
/content/papers/10.3997/2214-4609.202521042
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error