Design of hydrogen supply chains under demand uncertainty – a case study of passenger transport in Germany

Anton Ochoa Bique; Leonardo K. K. Maia; Ignacio E. Grossmann; Edwin Zondervan

doi:10.1515/psr-2020-0052

Abstract

A strategy for the design of a hydrogen supply chain (HSC) network in Germany incorporating the uncertainty in the hydrogen demand is proposed. Based on univariate sensitivity analysis, uncertainty in hydrogen demand has a very strong impact on the overall system costs. Therefore we consider a scenario tree for a stochastic mixed integer linear programming model that incorporates the uncertainty in the hydrogen demand. The model consists of two configurations, which are analyzed and compared to each other according to production types: water electrolysis versus steam methane reforming. Each configuration has a cost minimization target. The concept of value of stochastic solution (VSS) is used to evaluate the stochastic optimization results and compare them to their deterministic counterpart. The VSS of each configuration shows significant benefits of a stochastic optimization approach for the model presented in this study, corresponding up to 26% of infrastructure investments savings.

Keywords: fuel infrastructures; hydrogen supply chain design; mixed integer linear programming; stochastic optimization; water electrolysis technology

Corresponding author: Anton Ochoa Bique, Department of Production Engineering, Laboratory of Process Systems Engineering, Universität Bremen, Leobener Str. 6, 28359 Bremen, Germany, E-mail: anthonob@gmail.com

Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

1. Pregger, T, Nitsch, J, Naegler, T. Long-term scenarios and strategies for the deployment of renewable energies in Germany. Energy Pol 2013;59:350–60. https://doi.org/10.1016/j.enpol.2013.03.049.Search in Google Scholar

2. Schill, WP. Residual load, renewable surplus generation and storage requirements in Germany. Energy Pol 2014;73:65–79. https://doi.org/10.1016/j.enpol.2014.05.032.Search in Google Scholar

3. Lahnaoui, A, Wulf, C, Heinrichs, H, Dalmazzone, D. Optimizing hydrogen transportation system for mobility by minimizing the cost of transportation via compressed gas truck in North Rhine-Westphalia. Appl Energy 2018;223:317–28. https://doi.org/10.1016/j.apenergy.2018.03.099.Search in Google Scholar

4. Wulf, C, Reuß, M, Grube, T, Zapp, P, Robinius, M, Hake, J-F, et al.. Life cycle assessment of hydrogen transport and distribution options. J Clean Prod 2018;199:431–43. https://doi.org/10.1016/j.jclepro.2018.07.180.Search in Google Scholar

5. Grüger, F, Hoch, O, Hartmann, J, Robinius, M, Stolten, D. Optimized electrolyzer operation: employing forecasts of wind energy availability, hydrogen demand, and electricity prices. Int J Hydrogen Energy 2018;44:1–11. https://doi.org/10.1016/j.ijhydene.2018.07.165.Search in Google Scholar

6. Robinius, M, Linßen, J, Grube, T, Reuß, M, Stenzel, P, Syranidis, K, et al.. Comparative analysis of infrastructures: hydrogen fueling and electric charging of vehicles. Forschungszentrum Jülich GmbH: Zentralbibliothek; 2018.Search in Google Scholar

7. Reuß, M, Grube, T, Robinius, M, Stolten, D. A hydrogen supply chain with spatial resolution: comparative analysis of infrastructure technologies in Germany. Appl Energy 2019;247:438–53. https://doi.org/10.1016/j.apenergy.2019.04.064.Search in Google Scholar

8. Emonts, B, Reuß, M, Stenzel, P, Welder, L, Knicker, F, Grube, T, et al.. Flexible sector coupling with hydrogen: a climate- friendly fuel supply for road transport. Int J Hydrogen Energy 2019;44:12918–30. https://doi.org/10.1016/j.ijhydene.2018.07.165.Search in Google Scholar

9. International Energy Agency. Technology roadmap. SpringerReference 2015;81:1–81. https://doi.org/10.1007/SpringerReference_7300.Search in Google Scholar

10. Ball, M, Wietschel, M, Rentz, O. Integration of a hydrogen economy into the German energy system: an optimising modelling approach. Int J Hydrogen Energy 2007;32:1355–68. https://doi.org/10.1016/j.ijhydene.2006.10.016.Search in Google Scholar

11. Wulf, C, Kaltschmitt, M. Hydrogen supply chains for mobility-environmental and economic assessment. Sustainability 2018;10:1–26. https://doi.org/10.3390/su10061699.Search in Google Scholar

12. Robles, JO, Almaraz, SD-L, Azzaro-Pantel, C. Optimization of a hydrogen supply chain network design by multi-objective genetic algorithms. Comput Aided Chem Eng 2016;38:805–10. https://doi.org/10.1016/b978-0-444-63428-3.50139-9.Search in Google Scholar

13. Hugo, A, Rutter, P, Pistikopoulos, S, Amorelli, A, Zoia, G. Hydrogen infrastructure strategic planning using multi-objective optimization. Int J Hydrogen Energy 2005;30:1523–34. https://doi.org/10.1016/j.ijhydene.2005.04.017.Search in Google Scholar

14. Kim, J, Moon, I. Strategic design of hydrogen infrastructure considering cost and safety using multiobjective optimization. Int J Hydrogen Energy 2008;33:5887–96. https://doi.org/10.1016/j.ijhydene.2008.07.028.Search in Google Scholar

15. De-León Almaraz, S, Azzaro-Pantel, C, Montastruc, L, Pibouleau, L, Senties, OB. Assessment of mono and multi-objective optimization to design a hydrogen supply chain. Int J Hydrogen Energy 2013;38:14121–45. https://doi.org/10.1016/j.ijhydene.2013.07.059.Search in Google Scholar

16. Almansoori, A, Shah, N. Design and operation of a future hydrogen supply chain: multi-period model. Int J Hydrogen Energy 2009;34:7883–97. https://doi.org/10.1016/j.ijhydene.2009.07.109.Search in Google Scholar

17. Almansoori, A, Betancourt-Torcat, A. Design of optimization model for a hydrogen supply chain under emission constraints – a case study of Germany. Energy 2016;111:414–29. https://doi.org/10.1016/j.energy.2016.05.123.Search in Google Scholar

18. Grossmann, I. Enterprise-wide optimization: a new frontier in process systems engineering. AIChE J 2005;51:1846–57. https://doi.org/10.1002/aic.10617.Search in Google Scholar

19. You, F, Grossmann, IE. Multicut Benders decomposition algorithm for process supply chain planning under uncertainty. Ann Oper Res 2013;210:191–211. https://doi.org/10.1007/s10479-011-0974-4.Search in Google Scholar

20. Kim, J, Lee, Y, Moon, I. Optimization of a hydrogen supply chain under demand uncertainty. Int J Hydrogen Energy 2008;33:4715–29. https://doi.org/10.1016/j.ijhydene.2008.06.007.Search in Google Scholar

21. Almansoori, A, Shah, N. Design and operation of a stochastic hydrogen supply chain network under demand uncertainty. Int J Hydrogen Energy 2012;37:3965–77. https://doi.org/10.1016/j.ijhydene.2011.11.091.Search in Google Scholar

22. Ochoa Bique, A, Zondervan, E. An outlook towards hydrogen supply chain networks in 2050 — design of novel fuel infrastructures in Germany. Chem Eng Res Des 2018;134:90–103. https://doi.org/10.1016/j.cherd.2018.03.037.Search in Google Scholar

23. Bique, AO, Maia, LKK, La Mantia, F, Manca, D, Zondervan, E. Balancing costs, safety and CO2 emissions in the design of hydrogen supply chains. Comput Aided Chem Eng 2018;43:603–8. https://doi.org/10.1016/b978-0-444-64235-6.50108-x.Search in Google Scholar

24. Sahinidis, NV. Optimization under uncertainty: state-of-the-art and opportunities. Comput Chem Eng 2004;28:971–83. https://doi.org/10.1016/j.compchemeng.2003.09.017.Search in Google Scholar

25. Tang, C, Tomlin, B. The power of flexibility for mitigating supply chain risks. In: Pawar KS, Rogers H, Potter A, Naim M, editors. Developments in logistics and supply chain management: past, present and future. London UK: Palgrave Macmillan; 2016;80–9 pphttps://doi.org/10.1057/9781137541253_8.Search in Google Scholar

26. Dayhim, M, Jafari, MA, Mazurek, M. Planning sustainable hydrogen supply chain infrastructure with uncertain demand. Int J Hydrogen Energy 2014;39:6789–801. https://doi.org/10.1016/j.ijhydene.2014.02.132.Search in Google Scholar

27. Grossmann, IE, Apap, RM, Calfa, BA, Garcia-Herreros, P, Zhang, Q. Mathematical programming techniques for optimization under uncertainty and their application in process systems engineering. Theor Found Chem Eng 2017;51:893–909. https://doi.org/10.1134/s0040579517060057.Search in Google Scholar

28. Smith, A, Coit, D. Penalty functions. Evol Comput 2000;2:41–8. https://doi.org/10.1201/9781420034349.ch7.Search in Google Scholar

29. Birge, JR, Louveaux, F. Introduction to stochastic programming. New York, NY: Springer New York; 2011.10.1007/978-1-4614-0237-4Search in Google Scholar

30. Statistisches Bundesamt. Koordinierte Bevölkerungsvorausberechnung nach Bundesländern; n.d. https://www.destatis.de/EN/Homepage.html [Accessed 25 May 2017].Search in Google Scholar

31. ISE FI for SES. Energy charts; n.d. https://www.energy-charts.de/index.htm [Accessed 25 May 2017].Search in Google Scholar

32. Ruth, M, Laffen, M, Timbario, TA. Hydrogen pathways: cost, well-to-wheels energy use, and emissions for the current technology status of seven hydrogen production, delivery, and distribution scenarios. Golden, CO (United States): A national laboratory of the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy; 2009.10.2172/966286Search in Google Scholar

33. Saba, SM, Müller, M, Robinius, M, Stolten, D. The investment costs of electrolysis – a comparison of cost studies from the past 30 years. Int J Hydrogen Energy 2018;43:1209–23. https://doi.org/10.1016/j.ijhydene.2017.11.115.Search in Google Scholar

Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/PSR-2020-0052).

Published Online: 2021-08-23

You are currently not able to access this content.

Supplementary Material

Design of hydrogen supply chains under demand uncertainty – a case study of passenger transport in Germany

Abstract

References

Supplementary Material

Articles in the same Issue

Articles in the same Issue