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The intermittence and uncertainty of wind power pose challenges to large-scale wind power grid integration. The study of wind power uncertainty is becoming increasingly important for power system planning and operation. This paper proposes a wind power probabilistic interval prediction model, and a novel reliability assessment approach is presented for electrical power systems. First, the unknown parameters estimation of the autoregressive integrated moving average (ARIMA) prediction model is based on the Markov chain Monte Carlo (MCMC)-based Bayesian estimation method to improve the quality of statistical inference. Then, a quantum genetic algorithm is used to segment the power to determine the best output for each power segment weight and calculate the probabilistic prediction interval of wind power. Finally, reliability assessment by the sequential Monte Carlo simulation is presented combining with the probabilistic prediction interval of wind power on IEEE-RTS79 reliability test system. The simulation results that proposed variation range of reliability assessment indices consider the uncertain scenario of wind power and has guiding significance for power generation scheduling. Compared with genetic algorithm and particle swarm optimization algorithm, it is proved that the proposed prediction interval model has better prediction interval coverage probability index and interval average bandwidth index.

Research on the uncertainty of wind power has a significant influence on power system planning and decision-making. This paper proposes a novel method for wind power interval forecasting based on rough sets theory, weighted Markov chain, and kernel density estimation (KDE) method. Since the wind power prediction is significantly correlated to its historical record, this method first applies the Markov chain method to predict the power at different steps based on historical power data, and then the overall power is calculated via rough set weighted summation. Finally, the obtained forecasting power is fed into the KDE forecasting model to obtain both upper and lower bounds of the probability interval of the wind power at a certain confidence level. The predicted interval coverage probability and average bandwidth are two of the criterions used to evaluate the proposed method. Moreover, the simulation results obtained via the Markov chain-KDE method and the weighted Markov chain-KDE method are compared against the results of the proposed method. These comparisons show that the proposed method based on rough set theory and weighted Markov chain KDE method offers unique advantages over the other methods for probability interval prediction of wind power, which are higher coverage, narrower average bandwidth, and a more accurate result.