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A Deep Peak Regulation Auxiliary Service Bidding Strategy for CHP Units Based on a Risk-Averse Model and District Heating Network Energy Storage

Author

Listed:
  • Liang Tian

    (Department of Automation, North China Electric Power University, Baoding 071003, China)

  • Yunlei Xie

    (Department of Automation, North China Electric Power University, Baoding 071003, China)

  • Bo Hu

    (State Grid Liaoning Electric Power Company Limited, Shenyang 110006, China)

  • Xinping Liu

    (Department of Automation, North China Electric Power University, Baoding 071003, China)

  • Tuoyu Deng

    (Department of Automation, North China Electric Power University, Baoding 071003, China)

  • Huanhuan Luo

    (State Grid Liaoning Electric Power Company Limited, Shenyang 110006, China)

  • Fengqiang Li

    (State Grid Liaoning Electric Power Company Limited, Shenyang 110006, China)

Abstract

With the advance of China’s power system reform, combined heat and power (CHP) units can participate in multi-energy market. In order to maximize CHP profit in a multi-energy market, a bidding strategy for deep peak regulation auxiliary service of a CHP based on a two-stage stochastic programming risk-averse model and district heating network (DHN) energy storage was proposed. The quotation set of competitors and load uncertainty was modeled with a Latin hypercube sampling (LHS) method. A dynamic queuing method was used to clear the market for the deep peak regulation auxiliary service to determine the bidding capacities of CHPs in the electricity market and the deep peak regulation auxiliary service market, respectively. Finally, the conditional value-at-risk (CVaR) indicator is used to measure the risk brought by the system uncertainty to the CHP, and the quotation coefficient is determined after considering the expected profit and risk profit comprehensively. The results of the example show that the profits produced by simultaneous participation in both electricity market and the deep peak regulation auxiliary service market are increased by approximately 9.5% compared with the profits produced by only participation in a single market. In addition, the use of DHN energy storage led to a profit increase of approximately 4.6%. As the risk aversion coefficient increases, the expected profit will be further reduced.

Suggested Citation

  • Liang Tian & Yunlei Xie & Bo Hu & Xinping Liu & Tuoyu Deng & Huanhuan Luo & Fengqiang Li, 2019. "A Deep Peak Regulation Auxiliary Service Bidding Strategy for CHP Units Based on a Risk-Averse Model and District Heating Network Energy Storage," Energies, MDPI, vol. 12(17), pages 1-27, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3314-:d:261696
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    Cited by:

    1. Yanjuan Yu & Guohua Zhou & Kena Wu & Cheng Chen & Qiang Bian, 2023. "Optimal Configuration of Power-to-Heat Equipment Considering Peak-Shaving Ancillary Service Market," Energies, MDPI, vol. 16(19), pages 1-18, September.
    2. Yunhai Zhou & Shengkai Guo & Fei Xu & Dai Cui & Weichun Ge & Xiaodong Chen & Bo Gu, 2020. "Multi-Time Scale Optimization Scheduling Strategy for Combined Heat and Power System Based on Scenario Method," Energies, MDPI, vol. 13(7), pages 1-18, April.
    3. Li, Yuanzheng & Huang, Jingjing & Liu, Yun & Zhao, Tianyang & Zhou, Yue & Zhao, Yong & Yuen, Chau, 2022. "Day-ahead risk averse market clearing considering demand response with data-driven load uncertainty representation: A Singapore electricity market study," Energy, Elsevier, vol. 254(PA).
    4. Annelies Vandermeulen & Ina De Jaeger & Tijs Van Oevelen & Dirk Saelens & Lieve Helsen, 2020. "Analysis of Building Parameter Uncertainty in District Heating for Optimal Control of Network Flexibility," Energies, MDPI, vol. 13(23), pages 1-25, November.
    5. Biyun Chen & Yanni Chen & Bin Li & Yun Zhu & Chi Zhang, 2022. "An Optimal Dispatching Model for Integrated Energy Microgrid Considering the Reliability Principal–Agent Contract," Sustainability, MDPI, vol. 14(13), pages 1-16, June.
    6. Yan Zhang & Quan Lyu & Yang Li & Na Zhang & Lijun Zheng & Haoyan Gong & Hui Sun, 2020. "Research on Down-Regulation Cost of Flexible Combined Heat Power Plants Participating in Real-Time Deep Down-Regulation Market," Energies, MDPI, vol. 13(4), pages 1-17, February.
    7. Liang Tian & Xinping Liu & Huanhuan Luo & Tuoyu Deng & Jizhen Liu & Guiping Zhou & Tianting Zhang, 2021. "Soft Sensor of Heating Extraction Steam Flow Rate Based on Frequency Complementary Information Fusion for CHP Plant," Energies, MDPI, vol. 14(12), pages 1-17, June.

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