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Exploring Hydrogen Supply/Demand Networks: Modeller and Domain Expert Views

Authors Matthias Klapperstueck , Frits de Nijs , Ilankaikone Senthooran , Jack Lee-Kopij, Maria Garcia de la Banda , Michael Wybrow

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Author Details

Matthias Klapperstueck
  • Department of Human-Centred Computing, Faculty of IT, Monash University, Clayton, VIC, Australia
Frits de Nijs
  • Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC, Australia
  • ARC Industrial Training and Transformation Centre OPTIMA, Carlton, VIC, Australia
Ilankaikone Senthooran
  • Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC, Australia
  • ARC Industrial Training and Transformation Centre OPTIMA, Carlton, VIC, Australia
Jack Lee-Kopij
  • Woodside Energy Ltd., Perth, Australia
Maria Garcia de la Banda
  • Department of Data Science and AI, Faculty of IT, Monash University, Clayton, VIC, Australia
  • ARC Industrial Training and Transformation Centre OPTIMA, Carlton, VIC, Australia
Michael Wybrow
  • Department of Human-Centred Computing, Faculty of IT, Monash University, Clayton, VIC, Australia

Funding

Woodside Energy Ltd.

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Matthias Klapperstueck, Frits de Nijs, Ilankaikone Senthooran, Jack Lee-Kopij, Maria Garcia de la Banda, and Michael Wybrow. Exploring Hydrogen Supply/Demand Networks: Modeller and Domain Expert Views. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 21:1-21:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.CP.2023.21

Abstract

Energy companies are considering producing renewable fuels such as hydrogen/ammonia. Setting up a production network means deciding where to build production plants, and how to operate them at minimum electricity and transport costs. These decisions are complicated by many factors including the difficulty in obtaining accurate current data (e.g., electricity price and transport costs) for potential supply locations, the accuracy of data predictions (e.g., for demand and costs), and the need for some decisions to be made due to external (not modelled) factors. Thus, decision-makers need access to a user-centric decision system that helps them visualise, explore, interact and compare the many possible solutions of many different scenarios. This paper describes the system we have built to support our energy partner in making such decisions, and shows the advantages of having a graphical user-focused interactive tool, and of using a high-level constraint modelling language (MiniZinc) to implement the underlying model.

Subject Classification

ACM Subject Classification
  • Theory of computation → Constraint and logic programming
  • Theory of computation → Integer programming
  • Human-centered computing → Information visualization
Keywords
  • Facility Location
  • Hydrogen Supply Chain
  • Human-Centric Optimisation

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References

  1. Gleb Belov, Tobias Czauderna, Maria Garcia de la Banda, Matthias Klapperstueck, Ilankaikone Senthooran, Mitch Smith, Michael Wybrow, and Mark Wallace. Process Plant Layout Optimization: Equipment Allocation. In John Hooker, editor, Principles and Practice of Constraint Programming, pages 473-489. Springer, 2018. URL: https://doi.org/10.1007/978-3-319-98334-9_31.
  2. M. W. Dawande and J. N. Hooker. Inference-based sensitivity analysis for mixed integer/linear programming. Operations Research, 48(4):505-660, 2000. URL: https://doi.org/10.1287/opre.48.4.623.12420.
  3. Jip J. Dekker. MiniZinc Python, 2023. URL: https://minizinc-python.readthedocs.io/en/latest/.
  4. C. Doga Demirhan, William W. Tso, Joseph B. Powell, and Efstratios N. Pistikopoulos. A multi-scale energy systems engineering approach towards integrated multi-product network optimization. Applied Energy, 281:116020, 2021. URL: https://doi.org/10.1016/j.apenergy.2020.116020.
  5. Ibrahim Dincer and Canan Acar. Review and evaluation of hydrogen production methods for better sustainability. International Journal of Hydrogen Energy, 40(34):11094-11111, 2015. Google Scholar
  6. Graeme Gange, Jeremias Berg, Emir Demirović, and Peter J. Stuckey. Core-guided and core-boosted search for CP. In Emmanuel Hebrard and Nysret Musliu, editors, Integration of Constraint Programming, Artificial Intelligence, and Operations Research, pages 205-221, Cham, 2020. Springer International Publishing. URL: https://doi.org/10.1007/978-3-030-58942-4_14.
  7. Gurobi Optimization, LLC. Gurobi Optimizer Reference Manual, 2023. URL: https://www.gurobi.com.
  8. Q. Huangfu and J. A. J. Hall. Parallelizing the dual revised simplex method. Mathematical Programming Computation, 10(1):119-142, March 2018. URL: https://doi.org/10.1007/s12532-017-0130-5.
  9. IEA. Net zero by 2050. Technical report, IEA, Paris, May 2021. URL: https://www.iea.org/reports/net-zero-by-2050.
  10. Helgi Thor Ingason, Hjalti Pall Ingolfsson, and Pall Jensson. Optimizing site selection for hydrogen production in Iceland. International Journal of Hydrogen Energy, 33(14):3632-3643, 2008. TMS07: Symposium on Materials in Clean Power Systems. URL: https://doi.org/10.1016/j.ijhydene.2008.04.046.
  11. Linnea Ingmar, Maria Garcia de la Banda, Peter J Stuckey, and Guido Tack. Modelling diversity of solutions. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1528-1535, 2020. Google Scholar
  12. IPCC. Climate change 2022: Impacts, adaptation, and vulnerability. Technical report, Cambridge University Press, Cambridge, UK and New York, NY, USA, 2022. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. URL: https://doi.org/10.1017/9781009325844.
  13. Minsoo Kim and Jiyong Kim. Optimization model for the design and analysis of an integrated renewable hydrogen supply (IRHS) system: Application to Korea’s hydrogen economy. International Journal of Hydrogen Energy, 41(38):16613-16626, 2016. URL: https://doi.org/10.1016/j.ijhydene.2016.07.079.
  14. Jayanta Mandi, Emir Demiroviç, Peter J. Stuckey, and Tias Guns. Smart predict-and-optimize for hard combinatorial optimization problems. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 1603-1610, 2020. URL: https://doi.org/10.1609/aaai.v34i02.5521.
  15. Nicholas Nethercote, Peter J Stuckey, Ralph Becket, Sebastian Brand, Gregory J Duck, and Guido Tack. MiniZinc: Towards a standard CP modelling language. In Principles and Practice of Constraint Programming-CP 2007: 13th International Conference, CP 2007, Providence, RI, USA, September 23-27, 2007. Proceedings 13, pages 529-543. Springer, 2007. Google Scholar
  16. Jefferson A. Riera, Ricardo M. Lima, and Omar M. Knio. A review of hydrogen production and supply chain modeling and optimization. International Journal of Hydrogen Energy, 48(37):13731-13755, 2023. URL: https://doi.org/10.1016/j.ijhydene.2022.12.242.
  17. Ilankaikone Senthooran, Matthias Klapperstueck, Gleb Belov, Tobias Czauderna, Kevin Leo, Mark Wallace, Michael Wybrow, and Maria Garcia de la Banda. Human-Centred Feasibility Restoration in Practice. Constraints, 2023. (in publication). URL: https://doi.org/10.1007/s10601-023-09344-5.
  18. Shammamah Hossain. Visualization of Bioinformatics Data with Dash Bio. In Chris Calloway, David Lippa, Dillon Niederhut, and David Shupe, editors, Proceedings of the 18th Python in Science Conference, pages 126-133, 2019. URL: https://doi.org/10.25080/Majora-7ddc1dd1-012.
  19. Lawrence V. Snyder. Facility location under uncertainty: a review. IIE Transactions, 38(7):547-564, 2006. URL: https://doi.org/10.1080/07408170500216480.
  20. Šárka Štádlerová, Trygve Magnus Aglen, Andreas Hofstad, and Peter Schütz. Locating hydrogen production in Norway under uncertainty. In Jesica de Armas, Helena Ramalhinho, and Stefan Voß, editors, Computational Logistics, pages 306-321, Cham, 2022. Springer International Publishing. URL: https://doi.org/10.1007/978-3-031-16579-5_21.
  21. Mark van den Brand and Jan Friso Groote. Software engineering: Redundancy is key. Science of Computer Programming, 97:75-81, 2015. Special Issue on New Ideas and Emerging Results in Understanding Software. URL: https://doi.org/10.1016/j.scico.2013.11.020.
  22. H. Paul Williams. Model building in mathematical programming. John Wiley & Sons, 5th edition, 2013. Google Scholar
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