An efficient method with tunable accuracy for estimating expected interruption cost of distribution systems

The paper presents a novel efficient method with tunable accuracy for estimating expected interruption cost (ECOST) of distribution systems. ECOST index quantifies the reliability of a distribution system in monetary basis. The performance of ECOST estimation could be influenced by various factors such as time-varying load and cost models, computational limitation and random nature of component failure and repair time. Generally, the interruption cost is estimated based on an analytical method which does not consider the input and parameter uncertainties that are represented as random variables. The simulation method based on Monte Carlo (MC) simulation could provide a more accurate approximation of ECOST due to consideration of stochastic factors. However, one basic challenge related to the MC method is the high computational cost in order to run a large number of iterations for a specified high accuracy. Speed up the accurate estimation process using fast computation method could be an important feature in distribution systems management software. An advancement of the MC method with controllable accuracy is the Multilevel Monte Carlo (MLMC) estimator which is proposed to estimate the system ECOST. The proposed method could reduce the huge computational cost needed for accurately estimating the index. To illustrate the performance of this method, five different size distribution systems of Roy Billinton Test System are utilized. The impacts of the network topology, customer load type, time-varying load and cost models, failure and repair statistics on the MLMC based system ECOST assessment performance are also investigated.