Pretoria Communications Base Station Wind and Solar Complementarity

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.).

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.

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.

Stronger wind-solar complementarity occurs in low-elevation plains. Studying the complementarity between wind and solar energy is crucial for optimizing the use of these renewable resources.

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.

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.