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Multi-Time-Scale Optimal Scheduling in Active Distribution Network with Voltage Stability Constraints

Author

Listed:
  • Tianhao Song

    (College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China)

  • Xiaoqing Han

    (College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China)

  • Baifu Zhang

    (College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China)

Abstract

The uncertainty associated with loads and renewable-energy sources affects active distribution networks in terms of the operation and voltage stability on different time scales. To address this problem, a multi-time-scale voltage stability constrained optimal scheduling framework is proposed, which includes a day-ahead model with a coarse-grained time resolution and an intra-day model with a fine-grained time resolution. The day-ahead economic-scheduling model maps out a scheme to operate different types of devices with the aim of minimizing the network losses. Following the scheme, the intra-day corrective-adjustment model based on model predictive control is proposed to regulate the flexible devices, such as the energy storage systems and the photovoltaic converters. In particular, the proposed optimal scheduling framework embeds a voltage stability constraint which is constructed by using a novel index, defined based on the Distflow model Jacobian. As the index at each bus is a linear function of the locally measurable power flow variables, the proposed constraint does not introduce additional computational burdens. Simulation results demonstrate the necessity and effectiveness of the proposed multi-time-scale voltage stability constrained optimal scheduling model. The results also show that the variation trend of the proposed index is consistent with that of the commonly used voltage stability index.

Suggested Citation

  • Tianhao Song & Xiaoqing Han & Baifu Zhang, 2021. "Multi-Time-Scale Optimal Scheduling in Active Distribution Network with Voltage Stability Constraints," Energies, MDPI, vol. 14(21), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7107-:d:669667
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    References listed on IDEAS

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    1. Jay, Devika & Swarup, K.S., 2021. "A comprehensive survey on reactive power ancillary service markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    2. Arun Onlam & Daranpob Yodphet & Rongrit Chatthaworn & Chayada Surawanitkun & Apirat Siritaratiwat & Pirat Khunkitti, 2019. "Power Loss Minimization and Voltage Stability Improvement in Electrical Distribution System via Network Reconfiguration and Distributed Generation Placement Using Novel Adaptive Shuffled Frogs Leaping," Energies, MDPI, vol. 12(3), pages 1-12, February.
    3. Oscar Danilo Montoya & Walter Gil-González & Andrés Arias-Londoño & Arul Rajagopalan & Jesus C. Hernández, 2020. "Voltage Stability Analysis in Medium-Voltage Distribution Networks Using a Second-Order Cone Approximation," Energies, MDPI, vol. 13(21), pages 1-15, November.
    4. Maharjan, Salish & Sampath Kumar, Dhivya & Khambadkone, Ashwin M., 2020. "Enhancing the voltage stability of distribution network during PV ramping conditions with variable speed drive loads," Applied Energy, Elsevier, vol. 264(C).
    5. Ma, Wei & Wang, Wei & Chen, Zhe & Wu, Xuezhi & Hu, Ruonan & Tang, Fen & Zhang, Weige, 2021. "Voltage regulation methods for active distribution networks considering the reactive power optimization of substations," Applied Energy, Elsevier, vol. 284(C).
    6. Sultana, U. & Khairuddin, Azhar B. & Aman, M.M. & Mokhtar, A.S. & Zareen, N., 2016. "A review of optimum DG placement based on minimization of power losses and voltage stability enhancement of distribution system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 363-378.
    7. Songkai Liu & Ruoyuan Shi & Yuehua Huang & Xin Li & Zhenhua Li & Lingyun Wang & Dan Mao & Lihuang Liu & Siyang Liao & Menglin Zhang & Guanghui Yan & Lian Liu, 2021. "A Data-Driven and Data-Based Framework for Online Voltage Stability Assessment Using Partial Mutual Information and Iterated Random Forest," Energies, MDPI, vol. 14(3), pages 1-16, January.
    8. Veerasamy, Veerapandiyan & Abdul Wahab, Noor Izzri & Ramachandran, Rajeswari & Othman, Mohammad Lutfi & Hizam, Hashim & Devendran, Vidhya Sagar & Irudayaraj, Andrew Xavier Raj & Vinayagam, Arangarajan, 2021. "Recurrent network based power flow solution for voltage stability assessment and improvement with distributed energy sources," Applied Energy, Elsevier, vol. 302(C).
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