Time-varying correlation analysis and modeling of distributed renewable energy joint output based on time-varying Copula

The uncertainty and correlation of distributed renewable energy, represented by wind power and photovoltaic, which are connected to the power grid on a large scale, are posing new difficulties for the operation and planning of the power grid. According to the limitation of conventional static Copula function which is unable to accurately describe the time-varying correlation of distributed renewable energy (DRE) joint output, this paper proposes a time-varying Copula-based modeling method for the time-varying correlation of the wind-solar power joint output, and studies time-varying correlation of the wind-solar joint output. Firstly, the marginal probability distributions of the output of PV and wind power are obtained separately using nonparametric kernel density estimation method. Secondly, the time-varying Copula function is used to construct the wind-solar power joint output model, the time-varying correlation coefficient is applied to describe correlation of joint output. The superiority of the time-varying Copula compared to the conventional static Copula is verified using the goodness-of-fit test method. Finally, the measured data of a distributed PV station and a distributed wind farm in a region of Jiangsu Province in China in 2020 are taken as an example to verify the effectiveness of the proposed method.