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Predictive Reliability Assessment of Generation System

Author

Listed:
  • Martin Onyeka Okoye

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Junyou Yang

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Zhenjiang Lei

    (Science and Technology Communication Department, State Grid Liaoning Electric Power Co. Ltd., Shenyang 110006, China)

  • Jingwei Yuan

    (Science and Technology Communication Department, State Grid Liaoning Electric Power Co. Ltd., Shenyang 110006, China)

  • Huichao Ji

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Haixin Wang

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Jiawei Feng

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Tunmise Ayode Otitoju

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Weidong Li

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

Abstract

Due to increasing load and characteristic stagnation and fluctuations of existing generation systems capacity, the reliability assessment of generation systems is crucial to system adequacy. Furthermore, a rapid load increase could amount to a consequent sudden deficit in the generation supply before the next scheduled assessment. Hence, a reliability assessment is conducted at regular and close intervals to ensure adequacy. This study simulates and establishes the relationship between the load growth and generation capacity using the generation and load data of the IEEE reliability test system (IEEE RTS ‘96 standard). The generation capacity states and the risk model were obtained using the sequential Monte Carlo simulation (MCS) method. The load was gradually increased stepwise and is simulated against the constant generation capacity. In each case, the reliability index was recorded in terms of loss-of-load evaluation (LOLE). The recorded reliability index was thereafter fitted with the load-growth trend by the linear regression approach. A predictive assessment approach is thereafter proffered through the obtained fitting equation. In addition, a reliability threshold is effectively determined at a yield point for a reliability benchmark.

Suggested Citation

  • Martin Onyeka Okoye & Junyou Yang & Zhenjiang Lei & Jingwei Yuan & Huichao Ji & Haixin Wang & Jiawei Feng & Tunmise Ayode Otitoju & Weidong Li, 2020. "Predictive Reliability Assessment of Generation System," Energies, MDPI, vol. 13(17), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4350-:d:402753
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    References listed on IDEAS

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    1. Athanasios Dagoumas, 2019. "Assessing the Impact of Cybersecurity Attacks on Power Systems," Energies, MDPI, vol. 12(4), pages 1-23, February.
    2. Heiko Dunkelberg & Maximilian Sondermann & Henning Meschede & Jens Hesselbach, 2019. "Assessment of Flexibilisation Potential by Changing Energy Sources Using Monte Carlo Simulation," Energies, MDPI, vol. 12(4), pages 1-24, February.
    3. Athraa Ali Kadhem & Noor Izzri Abdul Wahab & Ishak Aris & Jasronita Jasni & Ahmed N. Abdalla, 2017. "Reliability Assessment of Power Generation Systems Using Intelligent Search Based on Disparity Theory," Energies, MDPI, vol. 10(3), pages 1-13, March.
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    Cited by:

    1. Patricio F. Castro & Yuri Percy M. Rodriguez & Fabricio B. S. Carvalho, 2022. "Application of Generation Adequacy Analysis for Reliability Evaluation of a Floating Production Storage and Offloading Platform Power System," Energies, MDPI, vol. 15(15), pages 1-16, July.
    2. Brown, Austin L. & Sperling, Daniel & Austin, Bernadette & DeShazo, JR & Fulton, Lew & Lipman, Timothy & Murphy, Colin W & Saphores, Jean Daniel & Tal, Gil & Abrams, Carolyn & Chakraborty, Debapriya &, 2021. "Driving California’s Transportation Emissions to Zero," Institute of Transportation Studies, Working Paper Series qt3np3p2t0, Institute of Transportation Studies, UC Davis.

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