IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v180y2021icp605-615.html
   My bibliography  Save this article

Multi-stage stochastic programming based offering strategy for hydrogen fueling station in joint energy, reserve markets

Author

Listed:
  • Wu, Xiong
  • Zhao, Wencheng
  • Li, Haoyu
  • Liu, Bingwen
  • Zhang, Ziyu
  • Wang, Xiuli

Abstract

Hydrogen fueling stations (HFSs) with onsite hydrogen production systems, which are usually composed of electrolyzers, hydrogen storage tanks and fuel cells, not only supply hydrogen for hydrogen-powered vehicles but also serve as a dispatchable technology that can bid in electricity markets. Except participating in energy market, joining in reserve market can compensate the cost in energy market and increase the total revenue of HFS. This paper proposes a multi-stage stochastic programming model to find the optimal offering strategy of the HFS in energy, reserve markets taking into account a series of uncertainties: day-ahead price, secondary reserve price, system imbalance price and hydrogen demand. Nonanticipativity constraints are employed to guarantee the decisions are made according to the realized uncertainty information up to the present stage. Compared with traditional stochastic programming model, the proposed model adequately considers the sequential bidding decisions with the gradual revealing of the uncertainty over time. Numerical experiments based on one case study indicate that the participation of reserve market greatly increase the revenue of HFS. In addition, the proposed multi-stage stochastic programming model is effective in characterizing the sequential decision.

Suggested Citation

  • Wu, Xiong & Zhao, Wencheng & Li, Haoyu & Liu, Bingwen & Zhang, Ziyu & Wang, Xiuli, 2021. "Multi-stage stochastic programming based offering strategy for hydrogen fueling station in joint energy, reserve markets," Renewable Energy, Elsevier, vol. 180(C), pages 605-615.
    • Handle: RePEc:eee:renene:v:180:y:2021:i:c:p:605-615
    DOI: 10.1016/j.renene.2021.08.076
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148121012428
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2021.08.076?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Petrollese, Mario & Valverde, Luis & Cocco, Daniele & Cau, Giorgio & Guerra, José, 2016. "Real-time integration of optimal generation scheduling with MPC for the energy management of a renewable hydrogen-based microgrid," Applied Energy, Elsevier, vol. 166(C), pages 96-106.
    2. R. T. Rockafellar & Roger J.-B. Wets, 1991. "Scenarios and Policy Aggregation in Optimization Under Uncertainty," Mathematics of Operations Research, INFORMS, vol. 16(1), pages 119-147, February.
    3. Torres-Rincón, Samuel & Sánchez-Silva, Mauricio & Bastidas-Arteaga, Emilio, 2021. "A multistage stochastic program for the design and management of flexible infrastructure networks," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    4. Li, Bei & Roche, Robin & Paire, Damien & Miraoui, Abdellatif, 2017. "Sizing of a stand-alone microgrid considering electric power, cooling/heating, hydrogen loads and hydrogen storage degradation," Applied Energy, Elsevier, vol. 205(C), pages 1244-1259.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Najafi, Arsalan & Homaee, Omid & Jasiński, Michał & Tsaousoglou, Georgios & Leonowicz, Zbigniew, 2023. "Integrating hydrogen technology into active distribution networks: The case of private hydrogen refueling stations," Energy, Elsevier, vol. 278(PB).
    2. Zhu, Junpeng & Meng, Dexin & Dong, Xiaofeng & Fu, Zhixin & Yuan, Yue, 2023. "An integrated electricity - hydrogen market design for renewable-rich energy system considering mobile hydrogen storage," Renewable Energy, Elsevier, vol. 202(C), pages 961-972.
    3. Belessiotis, George V. & Kontos, Athanassios G., 2022. "Plasmonic silver (Ag)-based photocatalysts for H2 production and CO2 conversion: Review, analysis and perspectives," Renewable Energy, Elsevier, vol. 195(C), pages 497-515.
    4. Najafi, Arsalan & Homaee, Omid & Jasiński, Michał & Pourakbari-Kasmaei, Mahdi & Lehtonen, Matti & Leonowicz, Zbigniew, 2023. "Participation of hydrogen-rich energy hubs in day-ahead and regulation markets: A hybrid stochastic-robust model," Applied Energy, Elsevier, vol. 339(C).
    5. Jixian Cui & Chenghao Liao & Ling Ji & Yulei Xie & Yangping Yu & Jianguang Yin, 2021. "A Short-Term Hybrid Energy System Robust Optimization Model for Regional Electric-Power Capacity Development Planning under Different Pollutant Control Pressures," Sustainability, MDPI, vol. 13(20), pages 1-20, October.

    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. Wu, Xiong & Qi, Shixiong & Wang, Zhao & Duan, Chao & Wang, Xiuli & Li, Furong, 2019. "Optimal scheduling for microgrids with hydrogen fueling stations considering uncertainty using data-driven approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Li, Bei & Roche, Robin, 2020. "Optimal scheduling of multiple multi-energy supply microgrids considering future prediction impacts based on model predictive control," Energy, Elsevier, vol. 197(C).
    3. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    4. Furat Dawood & GM Shafiullah & Martin Anda, 2020. "Stand-Alone Microgrid with 100% Renewable Energy: A Case Study with Hybrid Solar PV-Battery-Hydrogen," Sustainability, MDPI, vol. 12(5), pages 1-17, March.
    5. Lee, Jinkyu & Bae, Sanghyeon & Kim, Woo Chang & Lee, Yongjae, 2023. "Value function gradient learning for large-scale multistage stochastic programming problems," European Journal of Operational Research, Elsevier, vol. 308(1), pages 321-335.
    6. Lars M. Hvattum & Arne Løkketangen & Gilbert Laporte, 2006. "Solving a Dynamic and Stochastic Vehicle Routing Problem with a Sample Scenario Hedging Heuristic," Transportation Science, INFORMS, vol. 40(4), pages 421-438, November.
    7. Xin Huang & Duan Li & Daniel Zhuoyu Long, 2020. "Scenario-decomposition Solution Framework for Nonseparable Stochastic Control Problems," Papers 2010.08985, arXiv.org.
    8. Özgün Elçi & John Hooker, 2022. "Stochastic Planning and Scheduling with Logic-Based Benders Decomposition," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2428-2442, September.
    9. Gauvin, Charles & Delage, Erick & Gendreau, Michel, 2017. "Decision rule approximations for the risk averse reservoir management problem," European Journal of Operational Research, Elsevier, vol. 261(1), pages 317-336.
    10. Castro, Jordi & Escudero, Laureano F. & Monge, Juan F., 2023. "On solving large-scale multistage stochastic optimization problems with a new specialized interior-point approach," European Journal of Operational Research, Elsevier, vol. 310(1), pages 268-285.
    11. Wu, Dexiang & Wu, Desheng Dash, 2020. "A decision support approach for two-stage multi-objective index tracking using improved lagrangian decomposition," Omega, Elsevier, vol. 91(C).
    12. Kevin Ryan & Shabbir Ahmed & Santanu S. Dey & Deepak Rajan & Amelia Musselman & Jean-Paul Watson, 2020. "Optimization-Driven Scenario Grouping," INFORMS Journal on Computing, INFORMS, vol. 32(3), pages 805-821, July.
    13. Fan, Yingjie & Schwartz, Frank & Voß, Stefan, 2017. "Flexible supply chain planning based on variable transportation modes," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 654-666.
    14. Listes, O.L. & Dekker, R., 2002. "A scenario aggregation based approach for determining a robust airline fleet composition," Econometric Institute Research Papers EI 2002-17, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    15. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).
    16. Teemu Pennanen & Markku Kallio, 2006. "A splitting method for stochastic programs," Annals of Operations Research, Springer, vol. 142(1), pages 259-268, February.
    17. Barry C. Smith & Ellis L. Johnson, 2006. "Robust Airline Fleet Assignment: Imposing Station Purity Using Station Decomposition," Transportation Science, INFORMS, vol. 40(4), pages 497-516, November.
    18. Gilles Bareilles & Yassine Laguel & Dmitry Grishchenko & Franck Iutzeler & Jérôme Malick, 2020. "Randomized Progressive Hedging methods for multi-stage stochastic programming," Annals of Operations Research, Springer, vol. 295(2), pages 535-560, December.
    19. Andreatta, Giovanni & Dell'Olmo, Paolo & Lulli, Guglielmo, 2011. "An aggregate stochastic programming model for air traffic flow management," European Journal of Operational Research, Elsevier, vol. 215(3), pages 697-704, December.
    20. Huang, Edward & Mital, Pratik & Goetschalckx, Marc & Wu, Kan, 2016. "Optimal assignment of airport baggage unloading zones to outgoing flights," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 94(C), pages 110-122.

    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:eee:renene:v:180:y:2021:i:c:p:605-615. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.