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Coordinated voltage control in unbalanced distribution networks with two-stage distributionally robust chance-constrained receding horizon control

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  • Zhang, Zhengfa
  • da Silva, Filipe Faria
  • Guo, Yifei
  • Bak, Claus Leth
  • Chen, Zhe

Abstract

The integration of increasing share of renewable based distributed generation in distribution networks brings great challenges to voltage control. To address this issue, this paper presents a two-stage distributionally robust chance-constrained receding horizon control algorithm. In the proposed method, the distributionally robust chance-constrained reformulation of chance-constrained voltage control is derived, which is not only accurate, but also computationally efficient. Rather than perfect knowledge about the uncertainty associated with renewable generation, the proposed method only requires partial information of the underlying probability distribution. In addition, the mechanical voltage regulation devices and the DG inverters are controlled in two stages, considering their different characteristics in voltage control. By taking into account both the current and forecasted renewable generation, the proposed method utilizes receding horizon control to determine the control actions of voltage regulation devices. The effectiveness of the proposed method is demonstrated by case studies on unbalanced IEEE-123 bus system.

Suggested Citation

  • Zhang, Zhengfa & da Silva, Filipe Faria & Guo, Yifei & Bak, Claus Leth & Chen, Zhe, 2022. "Coordinated voltage control in unbalanced distribution networks with two-stage distributionally robust chance-constrained receding horizon control," Renewable Energy, Elsevier, vol. 198(C), pages 907-915.
    • Handle: RePEc:eee:renene:v:198:y:2022:i:c:p:907-915
    DOI: 10.1016/j.renene.2022.08.086
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    References listed on IDEAS

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    1. Ferdowsi, Farzad & Mehraeen, Shahab & Upton, Gregory B., 2020. "Assessing distribution network sensitivity to voltage rise and flicker under high penetration of behind-the-meter solar," Renewable Energy, Elsevier, vol. 152(C), pages 1227-1240.
    2. Howlader, Abdul Motin & Sadoyama, Staci & Roose, Leon R. & Sepasi, Saeed, 2018. "Distributed voltage regulation using Volt-Var controls of a smart PV inverter in a smart grid: An experimental study," Renewable Energy, Elsevier, vol. 127(C), pages 145-157.
    3. Mohseni-Bonab, Seyed Masoud & Rabiee, Abbas & Mohammadi-Ivatloo, Behnam, 2016. "Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties: A stochastic approach," Renewable Energy, Elsevier, vol. 85(C), pages 598-609.
    4. Acikgoz, Hakan, 2022. "A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting," Applied Energy, Elsevier, vol. 305(C).
    5. Mahmud, Nasif & Zahedi, A., 2016. "Review of control strategies for voltage regulation of the smart distribution network with high penetration of renewable distributed generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 582-595.
    6. Erick Delage & Yinyu Ye, 2010. "Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems," Operations Research, INFORMS, vol. 58(3), pages 595-612, June.
    7. Sadeghian, Hamidreza & Wang, Zhifang, 2020. "A novel impact-assessment framework for distributed PV installations in low-voltage secondary networks," Renewable Energy, Elsevier, vol. 147(P1), pages 2179-2194.
    8. G. C. Calafiore & L. El Ghaoui, 2006. "On Distributionally Robust Chance-Constrained Linear Programs," Journal of Optimization Theory and Applications, Springer, vol. 130(1), pages 1-22, July.
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    Cited by:

    1. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).
    2. Fangfang Zheng & Xiaofang Meng & Tiefeng Xu & Yongchang Sun & Nannan Zhang, 2023. "Voltage Zoning Regulation Method of Distribution Network with High Proportion of Photovoltaic Considering Energy Storage Configuration," Sustainability, MDPI, vol. 15(13), pages 1-19, July.

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