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A Practical Load-Source Coordinative Method for Further Reducing Curtailed Wind Power in China with Energy-Intensive Loads

Author

Listed:
  • Dandan Zhu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Wenying Liu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Yang Hu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Weizhou Wang

    (State Grid Corporation of Gansu Province, Lanzhou 730000, China)

Abstract

Large-scale wind power farms in China are suffering from large amounts of curtailed wind power in the conventional dispatching mode. Energy-intensive loads have great potential in providing service regulation to help consume the curtailed wind power. However, constrained by technical limitations, the regulating power of energy-intensive loads cannot fully follow the fluctuating wind power. This would result in insufficient utilization of the regulation capability of energy-intensive loads, which limits the promotion in consumption of curtailed wind power brought by load-source coordination. With this concern, a brand new method for further consuming the curtailed wind power, which involves using coal-fueled units to provide ancillary regulation for the coordination of energy-intensive loads and wind power is presented, and a corresponding bi-level coordinative model is also established. The first level of optimization is intended to maximize the consumption of curtailed wind power with minimum ancillary regulation energy. The second level of optimization is to allocate the ancillary regulating power with minimum regulation cost. Wind power consumption was increased by 3,369.25 MWh and utilization rate of energy-intensive loads was promoted to 100% in the case analysis, which verifies the effectiveness of the proposed method.

Suggested Citation

  • Dandan Zhu & Wenying Liu & Yang Hu & Weizhou Wang, 2018. "A Practical Load-Source Coordinative Method for Further Reducing Curtailed Wind Power in China with Energy-Intensive Loads," Energies, MDPI, vol. 11(11), pages 1-14, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2925-:d:178578
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    References listed on IDEAS

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    1. Chen, Runze & Sun, Hongbin & Guo, Qinglai & Jin, Hongyang & Wu, Wenchuan & Zhang, Boming, 2015. "Profit-seeking energy-intensive enterprises participating in power system scheduling: Model and mechanism," Applied Energy, Elsevier, vol. 158(C), pages 263-274.
    2. Ashok, S. & Banerjee, R., 2000. "Load-management applications for the industrial sector," Applied Energy, Elsevier, vol. 66(2), pages 105-111, June.
    3. Ramin, D. & Spinelli, S. & Brusaferri, A., 2018. "Demand-side management via optimal production scheduling in power-intensive industries: The case of metal casting process," Applied Energy, Elsevier, vol. 225(C), pages 622-636.
    4. Ashok, S., 2006. "Peak-load management in steel plants," Applied Energy, Elsevier, vol. 83(5), pages 413-424, May.
    5. Paulus, Moritz & Borggrefe, Frieder, 2011. "The potential of demand-side management in energy-intensive industries for electricity markets in Germany," Applied Energy, Elsevier, vol. 88(2), pages 432-441, February.
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    Cited by:

    1. Yuwei Zhang & Wenying Liu & Yue Huan & Qiang Zhou & Ningbo Wang, 2020. "An Optimal Day-Ahead Thermal Generation Scheduling Method to Enhance Total Transfer Capability for the Sending-Side System with Large-Scale Wind Power Integration," Energies, MDPI, vol. 13(9), pages 1-19, May.

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