Distributed Power and Charging Infrastructure Carrying Capacity Evaluation Model Based on Big Data

This paper proposes a distributed power and charging infrastructure carrying capacity evaluation model based on big data, aiming to solve the growing power demand and charging infrastructure load pressure. The model is based on the concept of robust carrying capacity, and by integrating Wasserstein fuzzy set theory, it realizes the carrying capacity evaluation of distributed power and charging infrastructure in an uncertain environment. The algorithm in this paper first preprocesses large-scale data, constructs carrying capacity evaluation indicators through cluster analysis and fuzzy logic, and performs fuzzy set operations based on Wasserstein distance. The system simulation part uses real data to verify the model, and through multi-scenario simulation analysis, it shows the performance of the model under different load conditions. The simulation results show that the proposed model has good robustness in dealing with different charging demands and uneven distribution of distributed power sources, and the evaluation error is controlled within a reasonable range. The data analysis results show that compared with traditional methods, the model has significant advantages in improving evaluation accuracy and adaptability.