IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i11p2021-d444359.html
   My bibliography  Save this article

An Efficient Framework for Adequacy Evaluation through Extraction of Rare Load Curtailment Events in Composite Power Systems

Author

Listed:
  • Amir Abdel Menaem

    (Electrical Engineering Department, Mansoura University, Mansoura 35516, Egypt
    Department of Automated Electric Systems, Ural Power Engineering Institute, Ural Federal University, 620002 Yekaterinburg, Russia)

  • Rustam Valiev

    (Department of Automated Electric Systems, Ural Power Engineering Institute, Ural Federal University, 620002 Yekaterinburg, Russia)

  • Vladislav Oboskalov

    (Department of Automated Electric Systems, Ural Power Engineering Institute, Ural Federal University, 620002 Yekaterinburg, Russia
    Science and Engineering Center “Reliability and Safety of Large Systems and Machines” UB RAS, 620002 Yekaterinburg, Russia)

  • Taher S. Hassan

    (Department of Mathematics, Faculty of Science, University of Ha’il, Ha’il 2440, Saudi Arabia
    Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt)

  • Hegazy Rezk

    (College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Aldawaser 11991, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt)

  • Mohamed N. Ibrahim

    (Electrical Engineering Department, Kafrelshiekh University, Kafr El-Sheikh 33511, Egypt
    Department of Electromechanical, Systems and Metal Engineering, Ghent University, 9000 Ghent, Belgium
    FlandersMake@UGent–corelab EEDT-MP, 3001 Leuven, Belgium)

Abstract

With the growing robustness of modern power systems, the occurrence of load curtailment events is becoming lower. Hence, the simulation of these events constitutes a challenge in adequacy indices assessment. Due to the rarity of the load curtailment events, the standard Monte Carlo simulation (MCS) estimator of adequacy indices is not practical. Therefore, a framework based on the enhanced cross-entropy-based importance sampling (ECE-IS) method is introduced in this paper for computing the adequacy indices. The framework comprises two stages. Using the proposed ECE-IS method, the first stage’s purpose is to identify the samples or states of the nodal generation and load that are greatly significant to the adequacy indices estimators. In the second stage, the density of the input variables’ conditional on the load curtailment domain obtained by the first stage are used to compute the nodal and system adequacy indices. The performance of the ECE-IS method is verified through a comparison with the standard MCS method and the recent techniques of rare events simulation in literature. The results confirm that the proposed method develops an accurate estimation for the nodal and system adequacy indices (loss of load probability (LOLP), expected power not supplied (EPNS)) with appropriate convergence value and low computation time.

Suggested Citation

  • Amir Abdel Menaem & Rustam Valiev & Vladislav Oboskalov & Taher S. Hassan & Hegazy Rezk & Mohamed N. Ibrahim, 2020. "An Efficient Framework for Adequacy Evaluation through Extraction of Rare Load Curtailment Events in Composite Power Systems," Mathematics, MDPI, vol. 8(11), pages 1-21, November.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:2021-:d:444359
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/11/2021/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/11/2021/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhe Zhang & Hang Yang & Xianggen Yin & Jiexiang Han & Yong Wang & Guoyan Chen, 2018. "A Load-Shedding Model Based on Sensitivity Analysis in on-Line Power System Operation Risk Assessment," Energies, MDPI, vol. 11(4), pages 1-17, March.
    2. Nannapaneni, Saideep & Mahadevan, Sankaran, 2020. "Probability-space surrogate modeling for fast multidisciplinary optimization under uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    3. Anna Kalinina & Matteo Spada & David F. Vetsch & Stefano Marelli & Calvin Whealton & Peter Burgherr & Bruno Sudret, 2020. "Metamodeling for Uncertainty Quantification of a Flood Wave Model for Concrete Dam Breaks," Energies, MDPI, vol. 13(14), pages 1-25, July.
    4. Cao, Quoc Dung & Choe, Youngjun, 2019. "Cross-entropy based importance sampling for stochastic simulation models," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hasan M. Salman & Jagadeesh Pasupuleti & Ahmad H. Sabry, 2023. "Review on Causes of Power Outages and Their Occurrence: Mitigation Strategies," Sustainability, MDPI, vol. 15(20), pages 1-34, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Meide & Zhang, Dequan & Jiang, Chao & Han, Xu & Li, Qing, 2021. "A hybrid adaptive Kriging-based single loop approach for complex reliability-based design optimization problems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Biyun Chen & Haoying Chen & Yiyi Zhang & Junhui Zhao & Emad Manla, 2019. "Risk Assessment for the Power Grid Dispatching Process Considering the Impact of Cyber Systems," Energies, MDPI, vol. 12(6), pages 1-18, March.
    3. Zheng, Xiao-Wei & Li, Hong-Nan & Gardoni, Paolo, 2023. "Hybrid Bayesian-Copula-based risk assessment for tall buildings subject to wind loads considering various uncertainties," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    4. Michael Felix Pacevicius & Marilia Ramos & Davide Roverso & Christian Thun Eriksen & Nicola Paltrinieri, 2022. "Managing Heterogeneous Datasets for Dynamic Risk Analysis of Large-Scale Infrastructures," Energies, MDPI, vol. 15(9), pages 1-40, April.
    5. Mehni, Moien Barkhori & Mehni, Mohammad Barkhori, 2023. "Reliability analysis with cross-entropy based adaptive Markov chain importance sampling and control variates," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    6. El Masri, Maxime & Morio, Jérôme & Simatos, Florian, 2021. "Improvement of the cross-entropy method in high dimension for failure probability estimation through a one-dimensional projection without gradient estimation," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    7. Pau Casals-Torrens & Juan A. Martinez-Velasco & Alexandre Serrano-Fontova & Ricard Bosch, 2020. "Assessment of Unintentional Islanding Operations in Distribution Networks with Large Induction Motors," Energies, MDPI, vol. 13(2), pages 1-25, January.
    8. Kröker, Ilja & Oladyshkin, Sergey, 2022. "Arbitrary multi-resolution multi-wavelet-based polynomial chaos expansion for data-driven uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    9. Zhang, Xiaobo & Lu, Zhenzhou & Cheng, Kai, 2022. "Cross-entropy-based directional importance sampling with von Mises-Fisher mixture model for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    10. Jakeman, John D. & Kouri, Drew P. & Huerta, J. Gabriel, 2022. "Surrogate modeling for efficiently, accurately and conservatively estimating measures of risk," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    11. Xia Xiao & Hanwen Qin & Huijuan Fu & Chengde Zhang, 2019. "Improving the Professional Level of Managers Through Individualized Recommendation to Enhance the Quality of Air Pollutant Management in China," Sustainability, MDPI, vol. 11(21), pages 1-15, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:2021-:d:444359. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.