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Improvement in the computational efficiency of a technique for assessing the reliability of electric power systems based on the Monte Carlo method

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  • Krupenev, Dmitry
  • Boyarkin, Denis
  • Iakubovskii, Dmitrii

Abstract

The reliability of energy systems is assessed to control their operation and expansion. An effective method for reliability assessment is the Monte Carlo method. This process, however, is often time-consuming due to the large size of the power system. This interferes with subsequent control problems. The speed of reliability assessment and the accuracy of the result for the Monte Carlo method directly depend on the number of randomly generated states of the system, their quality and the complexity of the subproblem to be solved for each state. When solving such a subproblem for reliability assessment, random states can be defined as a shortage and shortage-free ones. To assess the reliability of power systems using the Monte Carlo method, one should analyze only the state of the system with a shortage. We suggest the use of machine learning methods to eliminate or sort the shortage and shortage-free states. The paper demonstrates the effectiveness of two methods: a support vector machine and a random forest. It also shows their performance when the Monte Carlo and quasi-Monte Carlo methods are used.

Suggested Citation

  • Krupenev, Dmitry & Boyarkin, Denis & Iakubovskii, Dmitrii, 2020. "Improvement in the computational efficiency of a technique for assessing the reliability of electric power systems based on the Monte Carlo method," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:reensy:v:204:y:2020:i:c:s0951832020306724
    DOI: 10.1016/j.ress.2020.107171
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    References listed on IDEAS

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    1. Stern, R.E. & Song, J. & Work, D.B., 2017. "Accelerated Monte Carlo system reliability analysis through machine-learning-based surrogate models of network connectivity," Reliability Engineering and System Safety, Elsevier, vol. 164(C), pages 1-9.
    2. Hou, Tianfeng & Nuyens, Dirk & Roels, Staf & Janssen, Hans, 2019. "Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    3. Alban, Andres & Darji, Hardik A. & Imamura, Atsuki & Nakayama, Marvin K., 2017. "Efficient Monte Carlo methods for estimating failure probabilities," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 376-394.
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    8. Yuan, Zixia & Xiong, Guojiang & Fu, Xiaofan & Mohamed, Ali Wagdy, 2023. "Improving fault tolerance in diagnosing power system failures with optimal hierarchical extreme learning machine," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    9. Wang, Can & Xie, Haipeng & Bie, Zhaohong & Li, Gengfeng & Yan, Chao, 2021. "Fast supply reliability evaluation of integrated power-gas system based on stochastic capacity network model and importance sampling," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    10. Kamyab, Shahabeddin & Nematollahi, Mohammadreza & Henneaux, Pierre & Labeau, Pierre-Etienne, 2021. "Development of a hybrid method to assess grid-related LOOP scenarios for an NPP," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
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