IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v282y2023ics0360544223017863.html
   My bibliography  Save this article

Method for increasing net power of power plant based on operation optimization of circulating cooling water system

Author

Listed:
  • Wang, Huijie
  • Qiu, Baoyun
  • Zhao, Fangling
  • Yan, Tianxu

Abstract

Due to the low power generation performance caused by the unreasonable regulation of the circulating cooling water system (CCWS), a method for increasing the net power of a thermal power plant by optimizing the CCWS operation points was proposed. First, an iterative solver was developed to calculate the stable heat transfer parameters of water circulation processes. Then, the net power model of power plants was established according to the relationship between the CCWS operating points and steam turbine power. Considering the constraints of water flow and the number of pumps in operation, an optimization model with the objective of maximum net power was established. A joint program for solving the objective considering environmental parameters was developed based on artificial neural networks and a hybrid improved particle swarm algorithm. The joint solver could obtain the stable operating temperature of cooling water, the optimal operating point of CCWSs, and the maximum net power of power plants. In addition, the influence of steam load and environmental parameters on optimal solutions was summarized. After executing the operation optimization for a CCWS, the results showed that the net power of typical operating conditions in different seasons increased by 35.85–1423.64 kW, indicating a remarkable effect.

Suggested Citation

  • Wang, Huijie & Qiu, Baoyun & Zhao, Fangling & Yan, Tianxu, 2023. "Method for increasing net power of power plant based on operation optimization of circulating cooling water system," Energy, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s0360544223017863
    DOI: 10.1016/j.energy.2023.128392
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223017863
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.128392?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. He, Wei & Ding, Su & Zhang, Jifang & Pei, Chenchen & Zhang, Zhiheng & Wang, Yulin & Li, Hailong, 2021. "Performance optimization of server water cooling system based on minimum energy consumption analysis," Applied Energy, Elsevier, vol. 303(C).
    2. Payet-Burin, Raphael & Bertoni, Federica & Davidsen, Claus & Bauer-Gottwein, Peter, 2018. "Optimization of regional water - power systems under cooling constraints and climate change," Energy, Elsevier, vol. 155(C), pages 484-494.
    3. Ma, Keyan & Liu, Mingsheng & Zhang, Jili, 2021. "Online optimization method of cooling water system based on the heat transfer model for cooling tower," Energy, Elsevier, vol. 231(C).
    4. Coccia, Gianluca & Mugnini, Alice & Polonara, Fabio & Arteconi, Alessia, 2021. "Artificial-neural-network-based model predictive control to exploit energy flexibility in multi-energy systems comprising district cooling," Energy, Elsevier, vol. 222(C).
    5. Chen, Chaofan & Witte, Francesco & Tuschy, Ilja & Kolditz, Olaf & Shao, Haibing, 2022. "Parametric optimization and comparative study of an organic Rankine cycle power plant for two-phase geothermal sources," Energy, Elsevier, vol. 252(C).
    6. Wei, Xiupeng & Xu, Guanglin & Kusiak, Andrew, 2014. "Modeling and optimization of a chiller plant," Energy, Elsevier, vol. 73(C), pages 898-907.
    7. Khalid Abdulaziz Alnowibet & Salem Mahdi & Mahmoud El-Alem & Mohamed Abdelawwad & Ali Wagdy Mohamed, 2022. "Guided Hybrid Modified Simulated Annealing Algorithm for Solving Constrained Global Optimization Problems," Mathematics, MDPI, vol. 10(8), pages 1-25, April.
    8. Thangavelu, Sundar Raj & Myat, Aung & Khambadkone, Ashwin, 2017. "Energy optimization methodology of multi-chiller plant in commercial buildings," Energy, Elsevier, vol. 123(C), pages 64-76.
    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. Xiaoqing Wei & Nianping Li & Jinqing Peng & Jianlin Cheng & Jinhua Hu & Meng Wang, 2017. "Modeling and Optimization of a CoolingTower-Assisted Heat Pump System," Energies, MDPI, vol. 10(5), pages 1-18, May.
    2. Ono, Hitoi & Ohtani, Yuichi & Matsuo, Minoru & Yamaguchi, Toru & Yokoyama, Ryohei, 2021. "Optimal operation of heat source and air conditioning system with thermal storage tank using nonlinear programming," Energy, Elsevier, vol. 222(C).
    3. Ho, W.T. & Yu, F.W., 2021. "Improved model and optimization for the energy performance of chiller system with diverse component staging," Energy, Elsevier, vol. 217(C).
    4. Ju-wan Ha & Yu-jin Kim & Kyung-soon Park & Young-hak Song, 2022. "Energy Saving Evaluation with Low Liquid to Gas Ratio Operation in HVAC&R System," Energies, MDPI, vol. 15(19), pages 1-29, October.
    5. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    6. Pavičević, Matija & De Felice, Matteo & Busch, Sebastian & Hidalgo González, Ignacio & Quoilin, Sylvain, 2021. "Water-energy nexus in African power pools – The Dispa-SET Africa model," Energy, Elsevier, vol. 228(C).
    7. Oscar Villegas Mier & Anna Dittmann & Wiebke Herzberg & Holger Ruf & Elke Lorenz & Michael Schmidt & Rainer Gasper, 2023. "Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast," Energies, MDPI, vol. 16(19), pages 1-19, October.
    8. Kim, Icksung & Kim, Woohyun, 2023. "Application of market-based control with thermal energy storage system for demand limiting and real-time pricing control," Energy, Elsevier, vol. 263(PA).
    9. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2020. "A risk-based robust optimal chiller sequencing control strategy for energy-efficient operation considering measurement uncertainties," Applied Energy, Elsevier, vol. 280(C).
    10. Thangavelu, Sundar Raj & Myat, Aung & Khambadkone, Ashwin, 2017. "Energy optimization methodology of multi-chiller plant in commercial buildings," Energy, Elsevier, vol. 123(C), pages 64-76.
    11. Borkowski, Mateusz & Piłat, Adam Krzysztof, 2022. "Customized data center cooling system operating at significant outdoor temperature fluctuations," Applied Energy, Elsevier, vol. 306(PB).
    12. Qinli Deng & Liangxin Xu & Tingfang Zhao & Xuexin Hong & Xiaofang Shan & Zhigang Ren, 2022. "Cooperative Optimization of A Refrigeration System with A Water-Cooled Chiller and Air-Cooled Heat Pump by Coupling BPNN and PSO," Energies, MDPI, vol. 15(19), pages 1-19, September.
    13. Steinhäuser, J. Micha & Eisenack, Klaus, 2020. "How market design shapes the spatial distribution of power plant curtailment costs," Energy Policy, Elsevier, vol. 144(C).
    14. Verma, Anoop & Asadi, Ali & Yang, Kai & Tyagi, Satish, 2015. "A data-driven approach to identify households with plug-in electrical vehicles (PEVs)," Applied Energy, Elsevier, vol. 160(C), pages 71-79.
    15. Ahmad M. Alshamrani & Adel Fahad Alrasheedi & Khalid Abdulaziz Alnowibet & Salem Mahdi & Ali Wagdy Mohamed, 2022. "A Hybrid Stochastic Deterministic Algorithm for Solving Unconstrained Optimization Problems," Mathematics, MDPI, vol. 10(17), pages 1-26, August.
    16. Suryakant, & Tyagi, Satish, 2015. "Optimization of a platform configuration with generational changes," International Journal of Production Economics, Elsevier, vol. 169(C), pages 299-309.
    17. Tang, Rui & Wang, Shengwei & Shan, Kui & Cheung, Howard, 2018. "Optimal control strategy of central air-conditioning systems of buildings at morning start period for enhanced energy efficiency and peak demand limiting," Energy, Elsevier, vol. 151(C), pages 771-781.
    18. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2019. "Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm," Energies, MDPI, vol. 12(15), pages 1-13, July.
    19. Liu, Xuefeng & Huang, Bin & Zheng, Yulan, 2023. "Control strategy for dynamic operation of multiple chillers under random load constraints," Energy, Elsevier, vol. 270(C).
    20. Serafín Alonso & Antonio Morán & Miguel Ángel Prada & Perfecto Reguera & Juan José Fuertes & Manuel Domínguez, 2019. "A Data-Driven Approach for Enhancing the Efficiency in Chiller Plants: A Hospital Case Study," Energies, MDPI, vol. 12(5), pages 1-28, March.

    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:energy:v:282:y:2023:i:c:s0360544223017863. 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/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.