IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i6p2631-d1093997.html
   My bibliography  Save this article

Optimizing Renewable Injection in Integrated Natural Gas Pipeline Networks Using a Multi-Period Programming Approach

Author

Listed:
  • Emmanuel Ogbe

    (Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
    Department of Chemical Engineering, Khalifa University of Science, Technology and Research (KUSTAR), Abu Dhabi P.O. Box 2533, United Arab Emirates)

  • Ali Almansoori

    (Department of Chemical Engineering, Khalifa University of Science, Technology and Research (KUSTAR), Abu Dhabi P.O. Box 2533, United Arab Emirates)

  • Michael Fowler

    (Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada)

  • Ali Elkamel

    (Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
    Department of Chemical Engineering, Khalifa University of Science, Technology and Research (KUSTAR), Abu Dhabi P.O. Box 2533, United Arab Emirates)

Abstract

In this paper, we propose an optimization model that considers two pathways for injecting renewable content into natural gas pipeline networks. The pathways include (1) power-to-hydrogen or PtH, where off-peak electricity is converted to hydrogen via electrolysis, and (2) power-to-methane, or PtM, where carbon dioxide from different source locations is converted into renewable methane (also known as synthetic natural gas, SNG). The above pathways result in green hydrogen and methane, which can be injected into an existing natural gas pipeline network. Based on these pathways, a multi-period network optimization model that integrates the design and operation of hydrogen from PtH and renewable methane is proposed. The multi-period model is a mixed-integer non-linear programming (MINLP) model that determines (1) the optimal concentration of hydrogen and carbon dioxide in the natural gas pipelines, (2) the optimal location of PtH and carbon dioxide units, while minimizing the overall system cost. We show, using a case study in Ontario, the optimal network structure for injecting renewable hydrogen and methane within an integrated natural gas network system provides a $12M cost reduction. The optimal concentration of hydrogen ranges from 0.2 vol % to a maximum limit of 15.1 vol % across the network, while reaching a 2.5 vol % at the distribution point. This is well below the maximum limit of 5 vol % specification. Furthermore, the optimizer realized a CO 2 concentration ranging from 0.2 vol % to 0.7 vol %. This is well below the target of 1% specified in the model. The study is essential to understanding the practical implication of hydrogen penetration in natural gas systems in terms of constraints on hydrogen concentration and network system costs.

Suggested Citation

  • Emmanuel Ogbe & Ali Almansoori & Michael Fowler & Ali Elkamel, 2023. "Optimizing Renewable Injection in Integrated Natural Gas Pipeline Networks Using a Multi-Period Programming Approach," Energies, MDPI, vol. 16(6), pages 1-24, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2631-:d:1093997
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/6/2631/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/6/2631/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ruth Misener & Christodoulos Floudas, 2014. "ANTIGONE: Algorithms for coNTinuous / Integer Global Optimization of Nonlinear Equations," Journal of Global Optimization, Springer, vol. 59(2), pages 503-526, July.
    2. Mohammed Alfaki & Dag Haugland, 2013. "Strong formulations for the pooling problem," Journal of Global Optimization, Springer, vol. 56(3), pages 897-916, July.
    3. de Boer, Harmen Sytze & Grond, Lukas & Moll, Henk & Benders, René, 2014. "The application of power-to-gas, pumped hydro storage and compressed air energy storage in an electricity system at different wind power penetration levels," Energy, Elsevier, vol. 72(C), pages 360-370.
    4. Guandalini, Giulio & Colbertaldo, Paolo & Campanari, Stefano, 2017. "Dynamic modeling of natural gas quality within transport pipelines in presence of hydrogen injections," Applied Energy, Elsevier, vol. 185(P2), pages 1712-1723.
    5. Yue Su & Jingfa Li & Wangyi Guo & Yanlin Zhao & Jianli Li & Jie Zhao & Yusheng Wang, 2022. "Prediction of Mixing Uniformity of Hydrogen Injection inNatural Gas Pipeline Based on a Deep Learning Model," Energies, MDPI, vol. 15(22), pages 1-19, November.
    6. Sajad Aliakbari Sani & Azadeh Maroufmashat & Frédéric Babonneau & Olivier Bahn & Erick Delage & Alain Haurie & Normand Mousseau & Kathleen Vaillancourt, 2022. "Energy Transition Pathways for Deep Decarbonization of the Greater Montreal Region: An Energy Optimization Framework," Energies, MDPI, vol. 15(10), pages 1-18, May.
    7. Middleton, Richard S. & Bielicki, Jeffrey M., 2009. "A scalable infrastructure model for carbon capture and storage: SimCCS," Energy Policy, Elsevier, vol. 37(3), pages 1052-1060, March.
    8. Collet, Pierre & Flottes, Eglantine & Favre, Alain & Raynal, Ludovic & Pierre, Hélène & Capela, Sandra & Peregrina, Carlos, 2017. "Techno-economic and Life Cycle Assessment of methane production via biogas upgrading and power to gas technology," Applied Energy, Elsevier, vol. 192(C), pages 282-295.
    9. Chiang, Nai-Yuan & Zavala, Victor M., 2016. "Large-scale optimal control of interconnected natural gas and electrical transmission systems," Applied Energy, Elsevier, vol. 168(C), pages 226-235.
    10. Xiang Li & Asgeir Tomasgard & Paul I. Barton, 2011. "Nonconvex Generalized Benders Decomposition for Stochastic Separable Mixed-Integer Nonlinear Programs," Journal of Optimization Theory and Applications, Springer, vol. 151(3), pages 425-454, December.
    11. Shahryar Garmsiri & Marc A. Rosen & Gordon Rymal Smith, 2014. "Integration of Wind Energy, Hydrogen and Natural Gas Pipeline Systems to Meet Community and Transportation Energy Needs: A Parametric Study," Sustainability, MDPI, vol. 6(5), pages 1-21, April.
    12. Azadeh Maroufmashat & Michael Fowler, 2017. "Transition of Future Energy System Infrastructure; through Power-to-Gas Pathways," Energies, MDPI, vol. 10(8), pages 1-22, July.
    13. Ali Ekhtiari & Damian Flynn & Eoin Syron, 2020. "Investigation of the Multi-Point Injection of Green Hydrogen from Curtailed Renewable Power into a Gas Network," Energies, MDPI, vol. 13(22), pages 1-21, November.
    14. Mikolajková, Markéta & Haikarainen, Carl & Saxén, Henrik & Pettersson, Frank, 2017. "Optimization of a natural gas distribution network with potential future extensions," Energy, Elsevier, vol. 125(C), pages 848-859.
    15. R. Misener & C. A. Floudas, 2010. "Piecewise-Linear Approximations of Multidimensional Functions," Journal of Optimization Theory and Applications, Springer, vol. 145(1), pages 120-147, April.
    16. Ogbe, Emmanuel & Li, Xiang, 2017. "A new cross decomposition method for stochastic mixed-integer linear programming," European Journal of Operational Research, Elsevier, vol. 256(2), pages 487-499.
    17. Sirui Tong & Xiang Li & Shien Sun & Chengxu Tu & Xufeng Xia, 2022. "Interchangeability of Hydrogen Injection in Zhejiang Natural Gas Pipelines as a Means to Achieve Carbon Neutrality," Energies, MDPI, vol. 15(17), pages 1-12, September.
    18. Keogh, Niamh & Corr, D. & O'Shea, R. & Monaghan, R.F.D., 2022. "The gas grid as a vector for regional decarbonisation - a techno economic case study for biomethane injection and natural gas heavy goods vehicles," Applied Energy, Elsevier, vol. 323(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Parra, David & Zhang, Xiaojin & Bauer, Christian & Patel, Martin K., 2017. "An integrated techno-economic and life cycle environmental assessment of power-to-gas systems," Applied Energy, Elsevier, vol. 193(C), pages 440-454.
    2. Kolb, Sebastian & Plankenbühler, Thomas & Frank, Jonas & Dettelbacher, Johannes & Ludwig, Ralf & Karl, Jürgen & Dillig, Marius, 2021. "Scenarios for the integration of renewable gases into the German natural gas market – A simulation-based optimisation approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    3. Fan, Di & Gong, Jing & Zhang, Shengnan & Shi, Guoyun & Kang, Qi & Xiao, Yaqi & Wu, Changchun, 2021. "A transient composition tracking method for natural gas pipe networks," Energy, Elsevier, vol. 215(PA).
    4. Witte, Julia & Calbry-Muzyka, Adelaide & Wieseler, Tanja & Hottinger, Peter & Biollaz, Serge M.A. & Schildhauer, Tilman J., 2019. "Demonstrating direct methanation of real biogas in a fluidised bed reactor," Applied Energy, Elsevier, vol. 240(C), pages 359-371.
    5. Mukherjee, Ushnik & Walker, Sean & Maroufmashat, Azadeh & Fowler, Michael & Elkamel, Ali, 2017. "Development of a pricing mechanism for valuing ancillary, transportation and environmental services offered by a power to gas energy system," Energy, Elsevier, vol. 128(C), pages 447-462.
    6. Subramanian, Avinash S.R. & Gundersen, Truls & Barton, Paul I. & Adams, Thomas A., 2022. "Global optimization of a hybrid waste tire and natural gas feedstock polygeneration system," Energy, Elsevier, vol. 250(C).
    7. Kazda, Kody & Li, Xiang, 2024. "A linear programming approach to difference-of-convex piecewise linear approximation," European Journal of Operational Research, Elsevier, vol. 312(2), pages 493-511.
    8. Bermúdez, Alfredo & Shabani, Mohsen, 2022. "Numerical simulation of gas composition tracking in a gas transportation network," Energy, Elsevier, vol. 247(C).
    9. Falk M. Hante & Martin Schmidt, 2019. "Complementarity-based nonlinear programming techniques for optimal mixing in gas networks," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 7(3), pages 299-323, September.
    10. Emmanuel Ogbe & Xiang Li, 2019. "A joint decomposition method for global optimization of multiscenario nonconvex mixed-integer nonlinear programs," Journal of Global Optimization, Springer, vol. 75(3), pages 595-629, November.
    11. Yifei Lu & Thiemo Pesch & Andrea Benigni, 2021. "Simulation of Coupled Power and Gas Systems with Hydrogen-Enriched Natural Gas," Energies, MDPI, vol. 14(22), pages 1-17, November.
    12. Steffen Rebennack, 2016. "Computing tight bounds via piecewise linear functions through the example of circle cutting problems," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 84(1), pages 3-57, August.
    13. McDonagh, Shane & Deane, Paul & Rajendran, Karthik & Murphy, Jerry D., 2019. "Are electrofuels a sustainable transport fuel? Analysis of the effect of controls on carbon, curtailment, and cost of hydrogen," Applied Energy, Elsevier, vol. 247(C), pages 716-730.
    14. Deymi-Dashtebayaz, Mahdi & Ebrahimi-Moghadam, Amir & Pishbin, Seyyed Iman & Pourramezan, Mahdi, 2019. "Investigating the effect of hydrogen injection on natural gas thermo-physical properties with various compositions," Energy, Elsevier, vol. 167(C), pages 235-245.
    15. Pastore, Lorenzo Mario & Lo Basso, Gianluigi & de Santoli, Livio, 2022. "Can the renewable energy share increase in electricity and gas grids takes out the competitiveness of gas-driven CHP plants for distributed generation?," Energy, Elsevier, vol. 256(C).
    16. Fózer, Dániel & Volanti, Mirco & Passarini, Fabrizio & Varbanov, Petar Sabev & Klemeš, Jiří Jaromír & Mizsey, Péter, 2020. "Bioenergy with carbon emissions capture and utilisation towards GHG neutrality: Power-to-Gas storage via hydrothermal gasification," Applied Energy, Elsevier, vol. 280(C).
    17. Chaczykowski, Maciej & Zarodkiewicz, Paweł, 2017. "Simulation of natural gas quality distribution for pipeline systems," Energy, Elsevier, vol. 134(C), pages 681-698.
    18. McDonagh, Shane & O'Shea, Richard & Wall, David M. & Deane, J.P. & Murphy, Jerry D., 2018. "Modelling of a power-to-gas system to predict the levelised cost of energy of an advanced renewable gaseous transport fuel," Applied Energy, Elsevier, vol. 215(C), pages 444-456.
    19. Frank, Elimar & Gorre, Jachin & Ruoss, Fabian & Friedl, Markus J., 2018. "Calculation and analysis of efficiencies and annual performances of Power-to-Gas systems," Applied Energy, Elsevier, vol. 218(C), pages 217-231.
    20. Szoplik, Jolanta & Stelmasińska, Paulina, 2019. "Analysis of gas network storage capacity for alternative fuels in Poland," Energy, Elsevier, vol. 172(C), pages 343-353.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2631-:d:1093997. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.