Calculation of the area of ​​the wind-solar complementary ground network for communication base stations

Moreover, in 2018, Zhang et al. proposed a model to estimate the spatial and temporal complementarities of wind-solar energy. It adopted the ramp rate to evaluate the variability concisely, and used the synergy coefficient to express the mutual complementarity between wind and solar energy.

Previous studies have primarily used the Pearson correlation coefficient (CC) and similar metrics to evaluate the complementarity of wind and solar resources. For instance, Che et al. directly calculated Pearson CC to analyze the complementarity between wind and solar power and between wind and hydropower.

The Kendall CC, Spearman CC, and fluctuation coefficient are combined to construct a comprehensive measure of the complementarity between wind speed and radiation, which provides a reliable tool for quantitatively evaluating the complementary characteristics of wind and solar energy. 2. A copula-based wind-solar complementarity coefficient R

Utilizing the clustering outcomes, we computed the complementary coefficient R between the wind speed of wind power stations and the radiation of photovoltaic stations, resulting in the following complementary coefficient matrix (Fig. 17.).

By calculating the Kendall rank correlation coefficient between wind and solar energy in China, the study mapped the spatial distribution of wind-solar energy complementarity. Han et al. proposed a complementary evaluation framework for wind-solar-hydro multi-energy systems based on multi-criteria assessment and K-means clustering algorithms.

When optimizing the complementary wind and solar energy storage, cone optimization method is needed. The second-order cone programming model used is essentially a norm cone problem, represented by Eq. (8). In Eq. (8), the last digit of the sequence is t. I represents the identity matrix.