Dimensional importance based quasi Monte Carlo method for reliability evaluation of power system

Yushen Hou*, Xiuli Wang, Yue Zhang, Jingli Guo

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In view of the degradation of improvement character of the quasi-Monte Carlo method in handling high dimension problems, a dimensional importance based quasi-Monte Carlo method for reliability evaluation is pro-posed. First, the error estimation principle of quasi-Monte Carlo is described, and the uniformity degradation of low discrepancy sequence on increasing dimensions is analyzed. Secondly, the decomposition term of error bounds is derived by analysis of variance and sampling in the descending order of dimensional importance is proposed to reduce the error bounds. Then a model for quantifying dimensional importance is developed. Finally, a quasi-Monte Carlo modelfor power system reliability evaluation based on dimensional importance and a cross entropy qua-si-Monte Carlo model are developed. Then the accuracy index of evaluating the error between computed value and the true value is presented. The accuracy indices in calculating loss of load probability (LOLP) and expected demand not supplied (EDNS) of the proposed method and those of the crude method are compared on the test systems of RTS79 generation system, 292 units generation system and RBTS generation and transmission system. Results show that the method proposed has better error accuracy in the calculation of both indices mentioned above.

Original languageEnglish
Pages (from-to)31-37 and 158
JournalDianli Xitong Zidonghua/Automation of Electric Power Systems
Issue number16
Publication statusPublished - 25 Aug 2016


  • Dimensional importance
  • Expected demand not supplied
  • Loss of load probability
  • Low discrepancy sequence
  • Quasi-Monte Carlo
  • Reliability evaluation


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