Speeding Up Composite Power System Reliability Evaluation via Efficient Random Line Importance Sampling Combined With Cross-Entropy Method

Composite power system reliability evaluation using Monte Carlo simulation often suffers from high computational cost due to the difficulty in capturing rare loss-of-load states. To address this challenge, a novel technique called Random Line Importance Sampling integrated with the Cross-Entropy method (RLIS-C) is proposed. Unlike crude Monte Carlo simulation and the conventional cross-entropy method, which operate in a mixed discrete–continuous space, the proposed approach transforms this irregular space into a standard normal space composed of independent standard normal variables. In this transformed space, a random line sampling strategy based on spatial geometry is developed. Furthermore, RLIS constructs a one-dimensional Kriging surrogate-based importance sampling density function for each randomly generated failure line, enabling efficient extraction of rare loss-of-load states on the failure line. In addition, RLIS-C using the cross-entropy method to zoom in on the rare loss-of-load domain is further presented to enhance the capturing efficiency of failure lines. The proposed RLIS-C demonstrates clear efficiency gains over standalone RLIS or cross-entropy approaches. Its effectiveness is validated through reliability evaluations on an illustrative 3-bus system, as well as the IEEE-RTS79, IEEE-RTS96 systems, including their modified versions.