Abstract
With the steady annual growth of grid-connected photovoltaic (PV) power generation, the intermittent nature of this energy source has been increasingly drawing attention for its impact on grid stability. The output of photovoltaic power generation is highly influenced by weather factors and seasonal changes. The 24 solar terms are widely recognized as a reliable method for predicting weather conditions and seasonal shifts in China. Based on an analysis of the 24 solar terms, this work investigated their impact on PV power generation in China and established a correlation coefficient between PV output and solar terms. Subsequently, this paper proposed a grid connection method based on average values derived from the 24 solar terms and optimized it using a transfer learning model. The effectiveness of the proposed method was validated through a case study on a small-scale PV power station. It was validated that the proposed grid connection method increases power grid stability by 17.75%. The average grid connection method developed in this paper, which more accurately aligns with seasonal solar radiation variations, contributes to advancing grid-connected PV systems and provides a practical strategy for large-scale PV power integration.