IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i8p2328-d539805.html
   My bibliography  Save this article

Identification of Efficient Sampling Techniques for Probabilistic Voltage Stability Analysis of Renewable-Rich Power Systems

Author

Listed:
  • Mohammed Alzubaidi

    (Electrical and Biomedical Engineering, School of Engineering, RMIT University, Melbourne 3001, Australia
    College of Engineering, Umm Al-Qura University, Al-Qunfudhah 21912, Saudi Arabia)

  • Kazi N. Hasan

    (Electrical and Biomedical Engineering, School of Engineering, RMIT University, Melbourne 3001, Australia)

  • Lasantha Meegahapola

    (Electrical and Biomedical Engineering, School of Engineering, RMIT University, Melbourne 3001, Australia)

  • Mir Toufikur Rahman

    (Electrical and Biomedical Engineering, School of Engineering, RMIT University, Melbourne 3001, Australia)

Abstract

This paper presents a comparative analysis of six sampling techniques to identify an efficient and accurate sampling technique to be applied to probabilistic voltage stability assessment in large-scale power systems. In this study, six different sampling techniques are investigated and compared to each other in terms of their accuracy and efficiency, including Monte Carlo (MC), three versions of Quasi-Monte Carlo (QMC), i.e., Sobol, Halton, and Latin Hypercube, Markov Chain MC (MCMC), and importance sampling (IS) technique, to evaluate their suitability for application with probabilistic voltage stability analysis in large-scale uncertain power systems. The coefficient of determination (R 2 ) and root mean square error (RMSE) are calculated to measure the accuracy and the efficiency of the sampling techniques compared to each other. All the six sampling techniques provide more than 99% accuracy by producing a large number of wind speed random samples (8760 samples). In terms of efficiency, on the other hand, the three versions of QMC are the most efficient sampling techniques, providing more than 96% accuracy with only a small number of generated samples (150 samples) compared to other techniques.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2328-:d:539805
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/8/2328/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/8/2328/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Julio César Cuenca Tinitana & Carlos Adrian Correa-Florez & Diego Patino & José Vuelvas, 2020. "Spatio-Temporal Kriging Based Economic Dispatch Problem Including Wind Uncertainty," Energies, MDPI, vol. 13(23), pages 1-26, December.
    2. 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.
    3. Shuoliang Ding & Lionel Pichon, 2020. "Sensitivity Analysis of an Implanted Antenna within Surrounding Biological Environment," Energies, MDPI, vol. 13(4), pages 1-10, February.
    4. Zain Anwer Memon & Riccardo Trinchero & Paolo Manfredi & Flavio Canavero & Igor S. Stievano, 2020. "Compressed Machine Learning Models for the Uncertainty Quantification of Power Distribution Networks," Energies, MDPI, vol. 13(18), pages 1-18, September.
    5. Tansu Filik, 2016. "Improved Spatio-Temporal Linear Models for Very Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 9(3), pages 1-15, March.
    6. Lasantha Meegahapola & Alfeu Sguarezi & Jack Stanley Bryant & Mingchen Gu & Eliomar R. Conde D. & Rafael B. A. Cunha, 2020. "Power System Stability with Power-Electronic Converter Interfaced Renewable Power Generation: Present Issues and Future Trends," Energies, MDPI, vol. 13(13), pages 1-35, July.
    7. Hasan, Kazi Nazmul & Preece, Robin & Milanović, Jovica V., 2019. "Existing approaches and trends in uncertainty modelling and probabilistic stability analysis of power systems with renewable generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 168-180.
    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. 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.
    2. Juseung Choi & Hoyong Eom & Seung-Mook Baek, 2022. "A Wind Power Probabilistic Model Using the Reflection Method and Multi-Kernel Function Kernel Density Estimation," Energies, MDPI, vol. 15(24), pages 1-17, December.

    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. 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.
    2. 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.
    3. Davi-Arderius, Daniel & Schittekatte, Tim, 2023. "Carbon emissions impacts of operational network constraints: The case of Spain during the Covid-19 crisis," Energy Economics, Elsevier, vol. 128(C).
    4. 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.
    5. Hui Wang & Jingxuan Sun & Jianbo Sun & Jilong Wang, 2017. "Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models," Energies, MDPI, vol. 10(10), pages 1-13, October.
    6. Giorgio M. Giannuzzi & Viktoriya Mostova & Cosimo Pisani & Salvatore Tessitore & Alfredo Vaccaro, 2022. "Enabling Technologies for Enhancing Power System Stability in the Presence of Converter-Interfaced Generators," Energies, MDPI, vol. 15(21), pages 1-13, October.
    7. Akçay, Hüseyin & Filik, Tansu, 2017. "Short-term wind speed forecasting by spectral analysis from long-term observations with missing values," Applied Energy, Elsevier, vol. 191(C), pages 653-662.
    8. Duberney Murillo-Yarce & José Alarcón-Alarcón & Marco Rivera & Carlos Restrepo & Javier Muñoz & Carlos Baier & Patrick Wheeler, 2020. "A Review of Control Techniques in Photovoltaic Systems," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    9. Jay, Devika & Swarup, K.S., 2021. "A comprehensive survey on reactive power ancillary service markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    10. Roy, Nibir Baran & Das, Debapriya, 2024. "Stochastic power allocation of distributed tri-generation plants and energy storage units in a zero bus microgrid with electric vehicles and demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    11. Auza, Anna & Asadi, Ehsan & Chenari, Behrang & Gameiro da Silva, Manuel, 2024. "Review of cost objective functions in multi-objective optimisation analysis of buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    12. Matheus Schramm Dall’Asta & Telles Brunelli Lazzarin, 2024. "A Review of Fast Power-Reserve Control Techniques in Grid-Connected Wind Energy Conversion Systems," Energies, MDPI, vol. 17(2), pages 1-29, January.
    13. Yan, Donglin & Zheng, Yang & Liu, Wanying & Chen, Tianya & Chen, Qijuan, 2022. "Interval uncertainty analysis of vibration response of hydroelectric generating unit based on Chebyshev polynomial," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    14. Tsao, Yu-Chung & Thanh, Vo-Van & Chang, Yi-Ying & Wei, Hsi-Hsien, 2021. "COVID-19: Government subsidy models for sustainable energy supply with disruption risks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    15. Pepiciello, Antonio & Domínguez-García, José Luis, 2024. "Small-signal stability analysis of uncertain power systems: A comprehensive survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
    16. Gupta, S. & Maulik, A. & Das, D. & Singh, A., 2022. "Coordinated stochastic optimal energy management of grid-connected microgrids considering demand response, plug-in hybrid electric vehicles, and smart transformers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    17. Müller, Alfred & Reuber, Matthias, 2023. "A copula-based time series model for global horizontal irradiation," International Journal of Forecasting, Elsevier, vol. 39(2), pages 869-883.
    18. Nikolay Nikolaev & Kiril Dimitrov & Yulian Rangelov, 2021. "A Comprehensive Review of Small-Signal Stability and Power Oscillation Damping through Photovoltaic Inverters," Energies, MDPI, vol. 14(21), pages 1-26, November.
    19. Haixiang Zang & Miaomiao Wang & Jing Huang & Zhinong Wei & Guoqiang Sun, 2016. "A Hybrid Method for Generation of Typical Meteorological Years for Different Climates of China," Energies, MDPI, vol. 9(12), pages 1-19, December.
    20. Ahmed, Faraedoon & Al Kez, Dlzar & McLoone, Seán & Best, Robert James & Cameron, Ché & Foley, Aoife, 2023. "Dynamic grid stability in low carbon power systems with minimum inertia," Renewable Energy, Elsevier, vol. 210(C), pages 486-506.

    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:jeners:v:14:y:2021:i:8:p:2328-:d:539805. 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.