A New Approach for Modeling Wind Power in Reliability Studies

Tremendous growth of wind power worldwide in the past decade requires serious research in various fields. Because wind power is weather dependent, it is stochastic and varies over various time-scales. Therefore, accuracy in wind power modeling is recognized as a major contribution for reliable large-scale wind power integration. In this paper, a method for generating synthetic wind power is proposed. The proposed method combines the random nature of wind with the operational information of the wind turbines (i.e., failure and repair rates). It uses chronological or sequential Monte Carlo Simulation (MCS) instead of non-sequential one owing to its usefulness and flexibility in preserving statistical characteristics of the chronological processes. The validity of the synthetic values generated by the proposed method and the Auto Regressive Moving Average (ARMA) time series is compared with the measured data in terms of reliability indices. Finally, the effect of some network parameters, such as network dimensions, the average coefficient of wind speed on the reliability of the power system has been evaluated. In this regard, historical wind speed data of Manjil area located in the north of Iran is used.

Keywords:

Modeling wind power , Reliability , Markov Chain , Monte Carlo Simulation‎