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Demand side industrial load control for local utilization of wind power in isolated grids

Author

Listed:
  • Xu, Jian
  • Chen, Yuanfeng
  • Liao, Siyang
  • Sun, Yuanzhang
  • Yao, Liangzhong
  • Fu, Haobo
  • Jiang, Xueyi
  • Ke, Deping
  • Li, Xiong
  • Yang, Jun
  • Peng, Xiaotao

Abstract

Lack of flexible power source results in the high wind curtailment rate in northern and western China. Meanwhile, many energy-intensive industrial loads such as the polysilicon production enterprises went bankrupt because of the high costs of huge electricity consumption. Constructing the high electricity consumption enterprises for local utilization of wind power is effective to alleviate the wind power curtailment problem, and the high cost of the electricity consumption for the industrial load production can be reduced correspondingly. Thus, a new wind power local utilization way that an isolated power grid driven by coal and wind power for polysilicon production is presented. However, without the power support from the bulk power system, frequency stability issue is critical for the industrial isolated power system. Based on the actual production process, a polysilicon load control method by adjusting the pieced voltage value and voltage piecing time is proposed for supplementary frequency regulation. The detailed polysilicon load characteristic model is established considering the constraints of cooling water flow rate and temperature so that the polysilicon production quality is not impacted by the load control. An actual isolated power grid with high wind power penetration is studied as the example case to verify the effectiveness of the proposed control strategy under different working conditions.

Suggested Citation

  • Xu, Jian & Chen, Yuanfeng & Liao, Siyang & Sun, Yuanzhang & Yao, Liangzhong & Fu, Haobo & Jiang, Xueyi & Ke, Deping & Li, Xiong & Yang, Jun & Peng, Xiaotao, 2019. "Demand side industrial load control for local utilization of wind power in isolated grids," Applied Energy, Elsevier, vol. 243(C), pages 47-56.
    • Handle: RePEc:eee:appene:v:243:y:2019:i:c:p:47-56
    DOI: 10.1016/j.apenergy.2019.03.039
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    References listed on IDEAS

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    1. Xydas, Erotokritos & Qadrdan, Meysam & Marmaras, Charalampos & Cipcigan, Liana & Jenkins, Nick & Ameli, Hossein, 2017. "Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators," Applied Energy, Elsevier, vol. 192(C), pages 382-394.
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    Cited by:

    1. Gang Zhang & Yaning Zhu & Tuo Xie & Kaoshe Zhang & Xin He, 2022. "Wind Power Consumption Model Based on the Connection between Mid- and Long-Term Monthly Bidding Power Decomposition and Short-Term Wind-Thermal Power Joint Dispatch," Energies, MDPI, vol. 15(19), pages 1-25, September.
    2. Cheng, Yi & Azizipanah-Abarghooee, Rasoul & Azizi, Sadegh & Ding, Lei & Terzija, Vladimir, 2020. "Smart frequency control in low inertia energy systems based on frequency response techniques: A review," Applied Energy, Elsevier, vol. 279(C).
    3. Xiaoqing Wang & Xin Du & Haiyun Wang & Sizhe Yan & Tianyuan Fan, 2024. "Research on Coordinated Optimization of Source-Load-Storage Considering Renewable Energy and Load Similarity," Energies, MDPI, vol. 17(6), pages 1-16, March.
    4. Pang, Simian & Zheng, Zixuan & Xiao, Xianyong & Huang, Chunjun & Zhang, Shu & Li, Jie & Zong, Yi & You, Shi, 2022. "Collaborative power tracking method of diversified thermal loads for optimal demand response: A MILP-Based decomposition algorithm," Applied Energy, Elsevier, vol. 327(C).

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