Wind power time series simulation model based on typical daily output processes and Markov algorithm

Abstract

The simulation of wind power time series is a key process in renewable power allocation planning, operation mode calculation, and safety assessment. Traditional single-point modeling methods discretely generate wind power at each moment; however, they ignore the daily output characteristics and are unable to consider both modeling accuracy and efficiency. To resolve this problem, a wind power time series simulation model based on typical daily output processes and Markov algorithm is proposed. First, a typical daily output process classification method based on time series similarity and modified K-means clustering algorithm is presented. Second, considering the typical daily output processes as status variables, a wind power time series simulation model based on Markov algorithm is constructed. Finally, a case is analyzed based on the measured data of a wind farm in China. The proposed model is then compared with traditional methods to verify its effectiveness and applicability. The comparison results indicate that the statistical characteristics, probability distributions, and autocorrelation characteristics of the wind power time series generated by the proposed model are better than those of the traditional methods. Moreover, modeling efficiency considerably improves.

Keywords

• Wind power
• Time series
• Typical daily output processes
• Markov algorithm
• Modified K-means clustering algorithm