A model to represent correlated time series in reliability evaluation by nonsequential Monte Carlo simulation

This paper proposes a model that represents statistically dependent time-varying quantities, such as loads, wind power generation, and water inflows, and can be applied to evaluate power systems composite reliability by Non-Sequential Monte Carlo Simulation (MCS). This proposal is based on nonparametric stochastic models, which do not require a priori characterizations of the probability density functions of the random variables. Additionally, the maximal information coefficient, which allows mapping nonlinear relationships between the variables, and Bayesian network structures, which allow handling high dimensionality problems when multiple time series are represented, are applied. The proposed model allows reliability indices to be obtained with the same accuracy as the Sequential MCS but with computational costs on the order of the Non-Sequential MCS. The model is flexible enough to represent the relationships between variables with different levels of discretization, as in the case of the wind power generation and water inflows of a hydroelectric system.