A novel importance sampling method of power system reliability assessment considering multi-state units and correlation between wind speed and load

Abstract With the rapid integration of wind energy, the increasing uncertainty and high reliable property of power systems have resulted in great difficulties in reliability assessment. To solve the problem, traditional cross entropy based importance sampling (CE-IS) methods are improved in this paper. The improved method is capable of efficiently assessing the reliability of composite power systems with wind energy integrated. First, we introduce the differences between the improved CE-IS (ICE-IS) and traditional CE-IS. Particularly, ICE-IS takes the correlation of random variables (RVs) into account and models multi-state RVs with multinomial distribution. Therefore, ICE-IS can obtain much better suboptimal distributions for the RVs than CE-IS, which accelerates the reliability assessment. Then the procedures of ICE-IS are detailed by two parts, which are a pre-simulation stage and a main simulation stage, respectively. Finally, we modify IEEE-RTS 79 and IEEE-RTS 96 test systems based on wind speed data observed in northwest China. Several case studies are designed and carried out on the modified systems to validate the proposed method.