Analysis of wind-solar complementary power generation at communication base stations

Studying the complementarity between wind and solar energy is crucial for optimizing the use of these renewable resources. Multi-energy compensation systems need to consider multiple metrics, and current research relies on the correlation of single metrics to study this complementarity.

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.

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.).

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

Joint wind and solar distributions were modeled with the Copula function. A coefficient quantifying wind-photovoltaic complementarity was established. Spatial and temporal patterns of wind-solar complementarity were investigated. Stronger wind-solar complementarity occurs in low-elevation plains.

Analysis of the matrix reveals that the 4th, 5th, 7th, and 8th clusters of wind power stations exhibit the weakest complementarity with the radiation of photovoltaic stations. In contrast, the 5th, 7th, 8th, and 10th clusters of photovoltaic stations similarly demonstrate poor complementarity with the wind speed of wind power stations.