IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i2p492-d1322309.html
   My bibliography  Save this article

Numerical Study on Peak Shaving Performance of Combined Heat and Power Unit Assisted by Heating Storage in Long-Distance Pipelines Scheduled by Particle Swarm Optimization Method

Author

Listed:
  • Haoran Ju

    (Heating Research Center, Huadian Electric Power Research Institute, 2 Xiyuan Nine Road, Hangzhou 310030, China
    College of Energy Engineering, Zhejiang University, 38 Zheda Road, Hangzhou 310007, China)

  • Yongxue Wang

    (Heating Research Center, Huadian Electric Power Research Institute, 2 Xiyuan Nine Road, Hangzhou 310030, China)

  • Yiwu Feng

    (Heating Research Center, Huadian Electric Power Research Institute, 2 Xiyuan Nine Road, Hangzhou 310030, China)

  • Lijun Zheng

    (Heating Research Center, Huadian Electric Power Research Institute, 2 Xiyuan Nine Road, Hangzhou 310030, China)

Abstract

Thermal energy storage in long-distance heating supply pipelines can improve the peak shaving and frequency regulation capabilities of combined heat and power (CHP) units participating in the power grid. In this study, a one-dimensional numerical model was established to predict the thermal lag in long-distance pipelines at different scale levels. The dynamic response of the temperature at the end of the heating pipeline was considered. For the one-way pipe lengths of 10 km, 15 km and 20 km, the response times of the temperature at the distal end were 2.33 h, 2.94 h and 3.54 h, respectively. The longer the flow period, the further the warming-up time is delayed. An optimization scheduling approach was also created to illustrate the peak shaving capabilities of a CHP unit combined with a long-distance pipeline thermal energy storage component. It was demonstrated that the maximum heating load of the unit increased up to 503.08 MW, and the heating load could be expanded in the range of 17.88 MW to 203.76 MW at the minimum electric load of the unit of 104.08 MW. Finally, the particle swarm optimization method was adopted to guide the operating strategy through a whole day to meet both the electric power and heating power requirements. For the optimized case, the comprehensive energy utilization efficiency and the exergy efficiency increase to 64.4% and 56.73%. The thermal energy storage applications based on long-distance pipelines were simulated quantitively and proved to be effective in promoting the operational flexibility of the CHP unit.

Suggested Citation

  • Haoran Ju & Yongxue Wang & Yiwu Feng & Lijun Zheng, 2024. "Numerical Study on Peak Shaving Performance of Combined Heat and Power Unit Assisted by Heating Storage in Long-Distance Pipelines Scheduled by Particle Swarm Optimization Method," Energies, MDPI, vol. 17(2), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:492-:d:1322309
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/2/492/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/2/492/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Brown, Alastair & Foley, Aoife & Laverty, David & McLoone, Seán & Keatley, Patrick, 2022. "Heating and cooling networks: A comprehensive review of modelling approaches to map future directions," Energy, Elsevier, vol. 261(PB).
    2. Martínez-Lera, S. & Ballester, J. & Martínez-Lera, J., 2013. "Analysis and sizing of thermal energy storage in combined heating, cooling and power plants for buildings," Applied Energy, Elsevier, vol. 106(C), pages 127-142.
    3. Liu, Ming & Ma, Guofeng & Wang, Shan & Wang, Yu & Yan, Junjie, 2021. "Thermo-economic comparison of heat–power decoupling technologies for combined heat and power plants when participating in a power-balancing service in an energy hub," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    4. Wang, Jiang-Jiang & Jing, You-Yin & Zhang, Chun-Fa, 2010. "Optimization of capacity and operation for CCHP system by genetic algorithm," Applied Energy, Elsevier, vol. 87(4), pages 1325-1335, April.
    5. Zheng, Xuyue & Wu, Guoce & Qiu, Yuwei & Zhan, Xiangyan & Shah, Nilay & Li, Ning & Zhao, Yingru, 2018. "A MINLP multi-objective optimization model for operational planning of a case study CCHP system in urban China," Applied Energy, Elsevier, vol. 210(C), pages 1126-1140.
    6. Pérez-Iribarren, E. & González-Pino, I. & Azkorra-Larrinaga, Z. & Gómez-Arriarán, I., 2020. "Optimal design and operation of thermal energy storage systems in micro-cogeneration plants," Applied Energy, Elsevier, vol. 265(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. Pérez-Iribarren, E. & González-Pino, I. & Azkorra-Larrinaga, Z. & Gómez-Arriarán, I., 2020. "Optimal design and operation of thermal energy storage systems in micro-cogeneration plants," Applied Energy, Elsevier, vol. 265(C).
    2. Xiaofeng Liu & Shijun Wang & Jiawen Sun, 2018. "Energy Management for Community Energy Network with CHP Based on Cooperative Game," Energies, MDPI, vol. 11(5), pages 1-18, April.
    3. Benalcazar, Pablo, 2021. "Optimal sizing of thermal energy storage systems for CHP plants considering specific investment costs: A case study," Energy, Elsevier, vol. 234(C).
    4. Knudsen, Brage Rugstad & Rohde, Daniel & Kauko, Hanne, 2021. "Thermal energy storage sizing for industrial waste-heat utilization in district heating: A model predictive control approach," Energy, Elsevier, vol. 234(C).
    5. Chen, Ke & Pan, Ming, 2021. "Operation optimization of combined cooling, heating, and power superstructure system for satisfying demand fluctuation," Energy, Elsevier, vol. 237(C).
    6. Putna, Ondřej & Janošťák, František & Šomplák, Radovan & Pavlas, Martin, 2018. "Demand modelling in district heating systems within the conceptual design of a waste-to-energy plant," Energy, Elsevier, vol. 163(C), pages 1125-1139.
    7. Blarke, Morten B., 2012. "Towards an intermittency-friendly energy system: Comparing electric boilers and heat pumps in distributed cogeneration," Applied Energy, Elsevier, vol. 91(1), pages 349-365.
    8. Wang, Jiangjiang & Zhai, Zhiqiang (John) & Jing, Youyin & Zhang, Chunfa, 2010. "Optimization design of BCHP system to maximize to save energy and reduce environmental impact," Energy, Elsevier, vol. 35(8), pages 3388-3398.
    9. Huang, Jinbo & Li, Zhigang & Wu, Q.H., 2017. "Coordinated dispatch of electric power and district heating networks: A decentralized solution using optimality condition decomposition," Applied Energy, Elsevier, vol. 206(C), pages 1508-1522.
    10. Junshan Guo & Wei Zheng & Zhuang Cong & Panfeng Shang & Congyu Wang & Jiwei Song, 2021. "Steam-Water Modelling and the Coal-Saving Scheduling Strategy of Combined Heat and Power Systems," Energies, MDPI, vol. 15(1), pages 1-16, December.
    11. Nazar, Mehrdad Setayesh & Jafarpour, Pourya & Shafie-khah, Miadreza & Catalão, João P.S., 2024. "Optimal planning of self-healing multi-carriers energy systems considering integration of smart buildings and parking lots energy resources," Energy, Elsevier, vol. 286(C).
    12. Afzali, Sayyed Faridoddin & Mahalec, Vladimir, 2017. "Optimal design, operation and analytical criteria for determining optimal operating modes of a CCHP with fired HRSG, boiler, electric chiller and absorption chiller," Energy, Elsevier, vol. 139(C), pages 1052-1065.
    13. Zhang, Zhaoli & Alelyani, Sami M. & Zhang, Nan & Zeng, Chao & Yuan, Yanping & Phelan, Patrick E., 2018. "Thermodynamic analysis of a novel sodium hydroxide-water solution absorption refrigeration, heating and power system for low-temperature heat sources," Applied Energy, Elsevier, vol. 222(C), pages 1-12.
    14. Piacentino, Antonio & Barbaro, Chiara & Cardona, Fabio & Gallea, Roberto & Cardona, Ennio, 2013. "A comprehensive tool for efficient design and operation of polygeneration-based energy μgrids serving a cluster of buildings. Part I: Description of the method," Applied Energy, Elsevier, vol. 111(C), pages 1204-1221.
    15. Jie, Pengfei & Yan, Fuchun & Li, Jing & Zhang, Yumei & Wen, Zhimei, 2019. "Optimizing the insulation thickness of walls of existing buildings with CHP-based district heating systems," Energy, Elsevier, vol. 189(C).
    16. Shen, Feifei & Zhao, Liang & Du, Wenli & Zhong, Weimin & Qian, Feng, 2020. "Large-scale industrial energy systems optimization under uncertainty: A data-driven robust optimization approach," Applied Energy, Elsevier, vol. 259(C).
    17. Guozheng Li & Rui Wang & Tao Zhang & Mengjun Ming, 2018. "Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g," Energies, MDPI, vol. 11(4), pages 1-26, March.
    18. Glotić, Arnel & Zamuda, Aleš, 2015. "Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution," Applied Energy, Elsevier, vol. 141(C), pages 42-56.
    19. Ma, Weiwu & Fang, Song & Liu, Gang, 2017. "Hybrid optimization method and seasonal operation strategy for distributed energy system integrating CCHP, photovoltaic and ground source heat pump," Energy, Elsevier, vol. 141(C), pages 1439-1455.
    20. Jin, Baohong, 2023. "Impact of renewable energy penetration in power systems on the optimization and operation of regional distributed energy systems," Energy, Elsevier, vol. 273(C).

    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:17:y:2024:i:2:p:492-:d:1322309. 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.