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Robust frequency risk-constrained unit commitment model for AC-DC system considering wind uncertainty

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  • Jiang, Sufan
  • Wu, Chuanshen
  • Gao, Shan
  • Pan, Guangsheng
  • Liu, Yu
  • Zhao, Xin
  • Wang, Sicheng

Abstract

With the increasing penetration of volatile renewable energy sources (RES) in power systems, the formulation of RES uncertainty set in the unit commitment (UC) problem poses significant challenges to the operators especially those in AC-DC systems. To overcome the over-conservativeness and computational redundancy in conventional uncertainty formulation methods, this paper induces the stochastic rationale into the robust dispatch model and proposes a two-stage robust frequency risk-constrained unit commitment (FRCUC) model to excavate the system flexibility. Based on the typical characteristics of AC-DC dispatch system, the frequency violation risk (FVR) factors are proposed to enhance the adaptability of uncertainty sets, wherein the HVDC regulation is activated to cope with the extreme uncertain scenarios with lower probability. As a result, being aware of the wind power distribution, the proposed model succeeds in managing the operational risks by fully exploiting the controllability of HVDC system, and the over-conservativeness from conventional robust model is handled by the proposed FVR factors. The outperformance of our model is validated by the numerical experiments on a modified AC-DC system, it is validated that the operational risks are evaluated when considering the violated wind power distribution, and therefore the model economic benefits are promoted when ensuring the reliability.

Suggested Citation

  • Jiang, Sufan & Wu, Chuanshen & Gao, Shan & Pan, Guangsheng & Liu, Yu & Zhao, Xin & Wang, Sicheng, 2022. "Robust frequency risk-constrained unit commitment model for AC-DC system considering wind uncertainty," Renewable Energy, Elsevier, vol. 195(C), pages 395-406.
    • Handle: RePEc:eee:renene:v:195:y:2022:i:c:p:395-406
    DOI: 10.1016/j.renene.2022.06.017
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    Cited by:

    1. Jiménez, Diego & Angulo, Alejandro & Street, Alexandre & Mancilla-David, Fernando, 2023. "A closed-loop data-driven optimization framework for the unit commitment problem: A Q-learning approach under real-time operation," Applied Energy, Elsevier, vol. 330(PB).
    2. Qiu, Haifeng & Sun, Qirun & Lu, Xi & Beng Gooi, Hoay & Zhang, Suhan, 2022. "Optimality-feasibility-aware multistage unit commitment considering nonanticipative realization of uncertainty," Applied Energy, Elsevier, vol. 327(C).

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