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Flexibility Transformation Decision-Making Evaluation of Coal-Fired Thermal Power Units Deep Peak Shaving in China

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  • Jianjun Wang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Jikun Huo

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Shuo Zhang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Yun Teng

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Li Li

    (School of Economics and Management, Beijing Information Science & Technology University, Haidian district, Beijing 100192, China)

  • Taoya Han

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

According to China’s economic green ecological sustainability development requirement, the energy reform of China is mainly increasing the proportion of renewable energy, and reducing the proportion of fossil energy. It will continue to force China’s thermal power units, especially coal-fired thermal power units, to carry out the flexibility transformation and upgrading of deep peak shaving ability. Due to the different characteristics of coal-fired thermal power units, it is necessary to make flexible transformation decisions by a scientific and reasonable decision-making evaluation method, so as to provide references for the one machine-one policy flexibility transformation of thermal power units. In this paper, a decision-making evaluation index system for the flexibility transformation of coal-fired thermal power units under the demand of deep peak shaving is established. The index system considers the impact of deep peak shaving on the boilers, steam turbines, and auxiliary equipment of coal-fired thermal power units, as well as the effects of the peak shaving. A hybrid evaluation method combined set-valued iteration and GRA-TOPSIS is employed to obtain the weight of the indexes. Finally, an empirical research was conducted based on the index system and the hybrid evaluation method and targeted “one machine, one policy” recommendations were put forward for the flexibility transformation of the coal-fired thermal power units.

Suggested Citation

  • Jianjun Wang & Jikun Huo & Shuo Zhang & Yun Teng & Li Li & Taoya Han, 2021. "Flexibility Transformation Decision-Making Evaluation of Coal-Fired Thermal Power Units Deep Peak Shaving in China," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:1882-:d:496497
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    1. Garbrecht, Oliver & Bieber, Malte & Kneer, Reinhold, 2017. "Increasing fossil power plant flexibility by integrating molten-salt thermal storage," Energy, Elsevier, vol. 118(C), pages 876-883.
    2. Nuytten, Thomas & Claessens, Bert & Paredis, Kristof & Van Bael, Johan & Six, Daan, 2013. "Flexibility of a combined heat and power system with thermal energy storage for district heating," Applied Energy, Elsevier, vol. 104(C), pages 583-591.
    3. Ye, Liang-Cheng & Lin, Hai Xiang & Tukker, Arnold, 2019. "Future scenarios of variable renewable energies and flexibility requirements for thermal power plants in China," Energy, Elsevier, vol. 167(C), pages 708-714.
    4. Kubik, M.L. & Coker, P.J. & Barlow, J.F., 2015. "Increasing thermal plant flexibility in a high renewables power system," Applied Energy, Elsevier, vol. 154(C), pages 102-111.
    5. B. Kirubakaran & M. Ilangkumaran, 2016. "Selection of optimum maintenance strategy based on FAHP integrated with GRA–TOPSIS," Annals of Operations Research, Springer, vol. 245(1), pages 285-313, October.
    6. Gu, Yujiong & Xu, Jing & Chen, Dongchao & Wang, Zhong & Li, Qianqian, 2016. "Overall review of peak shaving for coal-fired power units in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 723-731.
    7. Jiaomin Liu & Tong Guo & Yue Wang & Yonggang Li & Shanshan Xu, 2020. "Multi-Technical Flexibility Retrofit Planning of Thermal Power Units Considering High Penetration Variable Renewable Energy: The Case of China," Sustainability, MDPI, vol. 12(9), pages 1-16, April.
    8. Finck, Christian & Li, Rongling & Kramer, Rick & Zeiler, Wim, 2018. "Quantifying demand flexibility of power-to-heat and thermal energy storage in the control of building heating systems," Applied Energy, Elsevier, vol. 209(C), pages 409-425.
    9. Tong Guo & Yajing Gao & Xiaojie Zhou & Yonggang Li & Jiaomin Liu, 2018. "Optimal Scheduling of Power System Incorporating the Flexibility of Thermal Units," Energies, MDPI, vol. 11(9), pages 1-17, August.
    10. Zhao, Yongliang & Liu, Ming & Wang, Chaoyang & Li, Xin & Chong, Daotong & Yan, Junjie, 2018. "Increasing operational flexibility of supercritical coal-fired power plants by regulating thermal system configuration during transient processes," Applied Energy, Elsevier, vol. 228(C), pages 2375-2386.
    11. Richter, Marcel & Oeljeklaus, Gerd & Görner, Klaus, 2019. "Improving the load flexibility of coal-fired power plants by the integration of a thermal energy storage," Applied Energy, Elsevier, vol. 236(C), pages 607-621.
    12. Yanjuan Hu & Lizhe Wu & Chao Shi & Yilin Wang & Feifan Zhu, 2020. "Research on optimal decision-making of cloud manufacturing service provider based on grey correlation analysis and TOPSIS," International Journal of Production Research, Taylor & Francis Journals, vol. 58(3), pages 748-757, February.
    13. Turan Arslan, 2017. "A Weighted Euclidean Distance based TOPSIS Method for Modeling Public Subjective Judgments," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(03), pages 1-18, June.
    14. Brouwer, Anne Sjoerd & van den Broek, Machteld & Seebregts, Ad & Faaij, André, 2015. "Operational flexibility and economics of power plants in future low-carbon power systems," Applied Energy, Elsevier, vol. 156(C), pages 107-128.
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