Application of Monte Carlo Simulation to Well-Being Analysis of Large Composite Power Systems

This paper presents a new methodology to evaluating the well-being indices of large composite generation and transmission systems. A well-being framework is used to classify the system states into healthy, marginal and at risk, according to a pre-defined deterministic criterion. In order to combine deterministic and probabilistic concepts, the proposed methodology uses a non-sequential Monte Carlo simulation, a multi-level non-aggregate Markov load model and test functions to estimate the well-being indices for the system and load buses. Moreover, a network reduction is also proposed to find an equivalent well-being framework suitable to practical large power systems. Case studies on an IEEE standard system and on a configuration of the Brazilian network are presented and discussed

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