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Development of an Optimized Curtailment Scheme through Real-Time Simulation

Author

Listed:
  • Jeong-Hwan Kim

    (Department of Electrical Engineering, Hanbat National University, Daejeon 305-719, Korea)

  • Iseul Nam

    (Department of Electrical Engineering, Hanbat National University, Daejeon 305-719, Korea)

  • Sungwoo Kang

    (School of Electrical Engineering, Korea University, Seoul 136-713, Korea)

  • Seungmin Jung

    (Department of Electrical Engineering, Hanbat National University, Daejeon 305-719, Korea)

Abstract

When a lot of surplus power occurs in wind power system, an output limit is implemented to directly or indirectly curtail the output to maintain a balance between the supply and demand of the power system. The curtailment process of a large-scale wind farm causes loss of power and mechanical loads. Resultantly, imbalanced curtailments often occur, resulting in unilateral burdens for the owners of wind farms. Considering the curtailment issue, the study for minimizing system loss of power plants is required in terms of operational efficiency. This paper proposes an algorithm to achieve flexible control during the actual power curtailment process in a wind farm, considering the wake effect. Here, the Monte Carlo method was adopted to calculate the curtailment weight in wind farms by using power loss terms. In addition, an equivalent model of a real wind farm was implemented and simulated through real simulation computer-aided design (RSCAD) software. This paper verified the effectiveness of the proposed method by applying the curtailment communication signal to a real-time digital simulator (RTDS). The results showed a reduction in the computational loading of individual wind turbine curtailment values with the decline of the total effective power loss.

Suggested Citation

  • Jeong-Hwan Kim & Iseul Nam & Sungwoo Kang & Seungmin Jung, 2022. "Development of an Optimized Curtailment Scheme through Real-Time Simulation," Energies, MDPI, vol. 15(3), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1074-:d:739663
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    References listed on IDEAS

    as
    1. Wang, Han & Yan, Jie & Han, Shuang & Liu, Yongqian, 2020. "Switching strategy of the low wind speed wind turbine based on real-time wind process prediction for the integration of wind power and EVs," Renewable Energy, Elsevier, vol. 157(C), pages 256-272.
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    Cited by:

    1. Rae-Jin Park & Jeong-Hwan Kim & Byungchan Yoo & Minhan Yoon & Seungmin Jung, 2022. "Verification of Prediction Method Based on Machine Learning under Wake Effect Using Real-Time Digital Simulator," Energies, MDPI, vol. 15(24), pages 1-15, December.

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