Where is the address of the wind and solar complementary communication base station in Sudan

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.

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.).

An examination of PV power plants in Qinghai and Gansu indicates a concentration of wind power plants in Qinghai's central region, which are abundant in solar resources. Like wind power plants, this distribution can be attributed to the southwest region's complex topography, sparse population, and limited accessibility.

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 joint probability distributions reveal lower probabilities of low wind speed and high radiation or high wind speed and low radiation in Qinghai and Gansu provinces at the hourly scale, indicating poor complementarity between wind speed and radiation in both provinces at this scale.

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.