Multiscale Dynamic Correlation Analysis of Wind-PV Power Station Output Based on TDIC

• Main Authors: Min Wang, Chao Wu, Peng Zhang, Zongyin Fan, Zixuan Yu
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Complete ensemble empirical mode decomposition with adaptive noise; dynamic correlation; empirical mode decomposition; renewable energy sources; time-dependent intrinsic correlation
• Online Access: https://ieeexplore.ieee.org/document/9247147/

The stochastic characteristics of wind power and photovoltaic (PV) make the resource allocation of power system difficult. Therefore, it is necessary to consider the correlation between the power generation of wind and PV power stations to avoid resource waste and guarantee system power supply, while the traditional correlation analysis method cannot accurately describe the multiscale and time-varying characteristics of the correlation. In this article, based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the time series of the wind and PV power generation was decomposed into multiscale components. Moreover, the time-dependent intrinsic correlation (TDIC) is introduced to excavate the local correlation of the power generation time series under the framework of a multi-time scale, the dynamic change of a correlation is captured by analyzing the TDIC plots. The analysis shows that the strength and nature of the association between wind and PV vary with time scales and time spells, reflecting rich, dynamic characteristics. The correlation variation of different scale components in local time is of great significance to power system operation, planning, and resource optimal allocation.