Abstract
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.
Highlights
Wind power has become one of the most popular renewable energy sources in the world, as it reduces the use of fossil fuels and saves greenhouse gas emission costs
To study reliability assessment for the uncertainty of wind power integrated into the grid, we first build a wind power probabilistic interval prediction model that combines an autoregressive integrated moving average (ARIMA) model based on a Markov chain Monte Carlo (MCMC)-based Bayesian estimation with optimized interval weights using a quantum genetic algorithm (QGA)
SUMMARY In this paper, considering the uncertainty of wind power, the reliability indices of the grid connection are presented with probabilistic prediction interval model of wind power
Summary
Wind power has become one of the most popular renewable energy sources in the world, as it reduces the use of fossil fuels and saves greenhouse gas emission costs. To study reliability assessment for the uncertainty of wind power integrated into the grid, we first build a wind power probabilistic interval prediction model that combines an autoregressive integrated moving average (ARIMA) model based on a Markov chain Monte Carlo (MCMC)-based Bayesian estimation with optimized interval weights using a quantum genetic algorithm (QGA). To address this research gap, our paper aims to outline a reliability assessment approach for electrical power systems considering of wind power uncertainty based on a probabilistic interval prediction model. The contributions of this paper mainly include two parts: One is that the variation ranges of reliability indices by sequential Monte Carlo methods are obtained for the first time for a power system under an IEEE-RTS79 reliability test system, which is combined with wind power probabilistic interval prediction model.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
