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Risk-Based Probabilistic Voltage Stability Assessment in Uncertain Power System

Author

Listed:
  • Weisi Deng

    (School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

  • Buhan Zhang

    (School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

  • Hongfa Ding

    (School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

  • Hang Li

    (School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, China)

Abstract

The risk-based assessment is a new approach to the voltage stability assessment in power systems. Under several uncertainties, the security risk of static voltage stability with the consideration of wind power can be evaluated. In this paper, we first build a probabilistic forecast model for wind power generation based on real historical data. Furthermore, we propose a new probability voltage stability approach based on Conditional Value-at-Risk (CVaR) and Quasi-Monte Carlo (QMC) simulation. The QMC simulation is used to speed up Monte Carlo (MC) simulation by improving the sampling technique. Our CVaR-based model reveals critical characteristics of static voltage stability. The distribution of the local voltage stability margin, which considers the security risk at a forecast operating time interval, is estimated to evaluate the probability voltage stability. Tested on the modified IEEE New England 39-bus system and the IEEE 118-bus system, results from the proposal are compared against the result of the conventional proposal. The effectiveness and advantages of the proposed method are demonstrated by the test results.

Suggested Citation

  • Weisi Deng & Buhan Zhang & Hongfa Ding & Hang Li, 2017. "Risk-Based Probabilistic Voltage Stability Assessment in Uncertain Power System," Energies, MDPI, vol. 10(2), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:180-:d:89437
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    References listed on IDEAS

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    1. van der Veen, Reinier A.C. & Abbasy, Alireza & Hakvoort, Rudi A., 2012. "Agent-based analysis of the impact of the imbalance pricing mechanism on market behavior in electricity balancing markets," Energy Economics, Elsevier, vol. 34(4), pages 874-881.
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    Cited by:

    1. M. Rambabu & G. V. Nagesh Kumar & S. Sivanagaraju, 2019. "Optimal Power Flow of Integrated Renewable Energy System using a Thyristor Controlled SeriesCompensator and a Grey-Wolf Algorithm," Energies, MDPI, vol. 12(11), pages 1-18, June.
    2. Heng-Yi Su & Tzu-Yi Liu, 2017. "A PMU-Based Method for Smart Transmission Grid Voltage Security Visualization and Monitoring," Energies, MDPI, vol. 10(8), pages 1-16, July.
    3. Ali M. Hakami & Kazi N. Hasan & Mohammed Alzubaidi & Manoj Datta, 2022. "A Review of Uncertainty Modelling Techniques for Probabilistic Stability Analysis of Renewable-Rich Power Systems," Energies, MDPI, vol. 16(1), pages 1-26, December.
    4. Mohammed Alzubaidi & Kazi N. Hasan & Lasantha Meegahapola & Mir Toufikur Rahman, 2021. "Identification of Efficient Sampling Techniques for Probabilistic Voltage Stability Analysis of Renewable-Rich Power Systems," Energies, MDPI, vol. 14(8), pages 1-15, April.
    5. Qi Wang & Dasong Sun & Jianxiong Hu & Yi Wu & Ji Zhou & Yi Tang, 2019. "Risk Assessment Method for Integrated Transmission–Distribution System Considering the Reactive Power Regulation Capability of DGs," Energies, MDPI, vol. 12(16), pages 1-14, August.
    6. Jie Zhu & Buxiang Zhou & Yiwei Qiu & Tianlei Zang & Yi Zhou & Shi Chen & Ningyi Dai & Huan Luo, 2023. "Survey on Modeling of Temporally and Spatially Interdependent Uncertainties in Renewable Power Systems," Energies, MDPI, vol. 16(16), pages 1-19, August.

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