Modeling and Application of Meteorological-Transportation-Power Coupling Complex Network
Zhiwei Wang, Wei Jiang, Xinyun Guo, Jiashun Lin, Kai Huang, Zhiyuan Liu · 2024
The transportation energy integration system comprises various interconnected monitoring variables that form a dynamic coupling relationship network, reflecting the system’s operational status. To address these issues, a coupled complex dynamic network integrating meteorological, transportation, and electricity factors is proposed. This approach first analyzes the causal relationship between meteorological conditions and traffic impedance using graph neural networks. Then, it converts the power consumption of electric vehicles into voltage sensitivity analysis of distribution power system nodes through an improved linear power-voltage model. Finally, the distribution power system nodes’ capacity to support the number of electric vehicles is determined. The results demonstrate that the proposed meteorological-transportation power coupling network effectively explores the interdependencies within these complex coupling networks, and its effectiveness is validated by practical cases.