IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i22p7720-d681774.html
   My bibliography  Save this article

Energetic and Exergetic Performances of a Retrofitted, Large-Scale, Biomass-Fired CHP Coupled to a Steam-Explosion Biomass Upgrading Plant, a Biorefinery Process and a High-Temperature Heat Network

Author

Listed:
  • Roeland De Meulenaere

    (Thermo and Fluid Dynamics (FLOW), Faculty of Engineering, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
    Brussels Institute for Thermal-Fluid Systems and Clean Energy (BRITE), Vrije Universiteit Brussel (VUB) and Université Libre de Bruxelles (ULB), 1050 Brussels, Belgium)

  • Tim Maertens

    (Onyx Power, Missouriweg 69, 3199 LB Maasvlakte, The Netherlands)

  • Ale Sikkema

    (Onyx Power, Missouriweg 69, 3199 LB Maasvlakte, The Netherlands)

  • Rune Brusletto

    (Arbaflame, Henrik Ibsens Gate 90, 0255 Oslo, Norway)

  • Tanja Barth

    (Department of Chemistry, University of Bergen, Allégaten 41, 5007 Bergen, Norway)

  • Julien Blondeau

    (Thermo and Fluid Dynamics (FLOW), Faculty of Engineering, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussels, Belgium
    Brussels Institute for Thermal-Fluid Systems and Clean Energy (BRITE), Vrije Universiteit Brussel (VUB) and Université Libre de Bruxelles (ULB), 1050 Brussels, Belgium)

Abstract

This paper aims at assessing the impact of retrofitting an existing, 730 MW e , coal-fired power plant into a biomass-fired combined heat and power (CHP) plant on its energetic and exergetic performances. A comprehensive thermodynamic model of the power plant was developed and validated against field data, resulting in less than 1 % deviation between the model and the measurements for the main process parameters. The validated model was then used to predict the behaviour of the biomass CHP after retrofitting. The modelled CHP unit is coupled to a steam-explosion biomass upgrading plant, a biorefinery process, and a high-temperature heat network. 13 scenarios were studied. At constant boiler load, delivering heat to the considered heat clients can increase the total energy efficiency of the plant from 44 % (electricity only) to 64 % , while the total exergy efficiency decreases from 39 % to 35 % . A total energy efficiency of 67 % could be reached by lowering the network temperature from 120 ? C to 70 ? C. Identifying the needed heat clients could, however, represent a limiting factor to reach such high efficiencies. For a constant power demand, increasing the boiler load from 80 to 100 % in order to provide additional heat makes the total energy efficiency increase from 43 % to 55 % , while the total exergy efficiency decreases from 39 % to 36 % .

Suggested Citation

  • Roeland De Meulenaere & Tim Maertens & Ale Sikkema & Rune Brusletto & Tanja Barth & Julien Blondeau, 2021. "Energetic and Exergetic Performances of a Retrofitted, Large-Scale, Biomass-Fired CHP Coupled to a Steam-Explosion Biomass Upgrading Plant, a Biorefinery Process and a High-Temperature Heat Network," Energies, MDPI, vol. 14(22), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7720-:d:681774
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/22/7720/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/22/7720/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yin, Chungen, 2020. "Development in biomass preparation for suspension firing towards higher biomass shares and better boiler performance and fuel rangeability," Energy, Elsevier, vol. 196(C).
    2. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    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. Zhou, Yuekuan & Zheng, Siqian, 2020. "Uncertainty study on thermal and energy performances of a deterministic parameters based optimal aerogel glazing system using machine-learning method," Energy, Elsevier, vol. 193(C).
    2. Peng Liu & Tieyan Zhang & Furui Tian & Yun Teng & Miaodong Yang, 2024. "Hybrid Decision Support Framework for Energy Scheduling Using Stochastic Optimization and Cooperative Game Theory," Energies, MDPI, vol. 17(24), pages 1-20, December.
    3. David Morillón Gálvez & Iván García Kerdan & Germán Carmona-Paredes, 2022. "Assessing the Potential of Implementing a Solar-Based Distributed Energy System for a University Using the Campus Bus Stops," Energies, MDPI, vol. 15(10), pages 1-16, May.
    4. Jann Michael Weinand, 2020. "Reviewing Municipal Energy System Planning in a Bibliometric Analysis: Evolution of the Research Field between 1991 and 2019," Energies, MDPI, vol. 13(6), pages 1-18, March.
    5. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    6. Perera, A.T.D. & Wickramasinghe, P.U. & Nik, Vahid M. & Scartezzini, Jean-Louis, 2019. "Machine learning methods to assist energy system optimization," Applied Energy, Elsevier, vol. 243(C), pages 191-205.
    7. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
    8. Perera, A.T.D. & Khayatian, F. & Eggimann, S. & Orehounig, K. & Halgamuge, Saman, 2022. "Quantifying the climate and human-system-driven uncertainties in energy planning by using GANs," Applied Energy, Elsevier, vol. 328(C).
    9. Tian, Zhe & Li, Xiaoyuan & Niu, Jide & Zhou, Ruoyu & Li, Feng, 2024. "Enhancing operation flexibility of distributed energy systems: A flexible multi-objective optimization planning method considering long-term and temporary objectives," Energy, Elsevier, vol. 288(C).
    10. Lu, M.L. & Sun, Y.J. & Kokogiannakis, G. & Ma, Z.J., 2024. "Design of flexible energy systems for nearly/net zero energy buildings under uncertainty characteristics: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 205(C).
    11. Mingzi Xu & Changdong Sheng, 2022. "Modeling the Process and Properties of Ash Formation during Pulverized Biomass Combustion," Energies, MDPI, vol. 15(12), pages 1-17, June.
    12. Kharrazi, A. & Sreeram, V. & Mishra, Y., 2020. "Assessment techniques of the impact of grid-tied rooftop photovoltaic generation on the power quality of low voltage distribution network - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    13. Zhu, Yiming & Su, Haining & Qiu, Tongyu & Zhai, Yingmei & Mikulčić, Hrvoje & Wang, Xuebin & Zhang, Lan & Xie, Jun & Yang, Tianhua, 2024. "Modelling of fly ash viscous deposition and slagging prediction of biomass-fired boiler," Renewable Energy, Elsevier, vol. 227(C).
    14. Javanshir Fouladvand & Niek Mouter & Amineh Ghorbani & Paulien Herder, 2020. "Formation and Continuation of Thermal Energy Community Systems: An Explorative Agent-Based Model for the Netherlands," Energies, MDPI, vol. 13(11), pages 1-22, June.
    15. Sousa, Jorge & Lagarto, João & Carvalho, Ezequiel & Martins, Ana, 2023. "SWHORD simulator: A platform to evaluate energy transition targets in future energy systems with increasing renewable generation, electric vehicles, storage technologies, and hydrogen systems," Energy, Elsevier, vol. 271(C).
    16. Dergunova, Tatyana & Lyden, Andrew, 2024. "Great Britain’s hydrogen infrastructure development — Investment priorities and locational flexibility," Applied Energy, Elsevier, vol. 375(C).
    17. Niu, Jide & Li, Xiaoyuan & Tian, Zhe & Yang, Hongxing, 2023. "A framework for quantifying the value of information to mitigate risk in the optimal design of distributed energy systems under uncertainty," Applied Energy, Elsevier, vol. 350(C).
    18. Kang, Jing & Wang, Shengwei, 2018. "Robust optimal design of distributed energy systems based on life-cycle performance analysis using a probabilistic approach considering uncertainties of design inputs and equipment degradations," Applied Energy, Elsevier, vol. 231(C), pages 615-627.
    19. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    20. Volpato, Gabriele & Carraro, Gianluca & De Giovanni, Luigi & Dal Cin, Enrico & Danieli, Piero & Bregolin, Edoardo & Lazzaretto, Andrea, 2024. "A stochastic optimization procedure to design the fair aggregation of energy users in a Renewable Energy Community," Renewable Energy, Elsevier, vol. 237(PA).

    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:14:y:2021:i:22:p:7720-:d:681774. 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.