What are the foundations of wind and solar complementary communication base stations

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.

Monforti et al. assessed the complementarity between wind and solar resources in Italy through Pearson correlation analysis and found that their complementarity can favourably support their integration into the energy system. Jurasz et al. simulated the operation of wind-solar HES for 86 locations in Poland.

To this end, we propose a novel variation-based complementarity metrics system based on the description of series' fluctuation characteristics from quantitative and contoured dimensions. From this, the complementarity between wind and solar resources in China is assessed, and the trend and persistence are tested.

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.).

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.

However, for the regions with relatively poor wind and solar resources, such as central Tibet, eastern Sichuan, western Yunnan, Chongqing, Guizhou, Zhejiang, Guangdong, and Guangxi, the complementarity is relatively weak.