An Uncertainty-Based Aggregation Control Optimization Method for Adjustable Resources Incorporating Air Conditioning Load

This article proposes a modeling method based on multi scenario generation and aggregation control optimization to address the uncertainty of the regulation potential of air conditioning loads and other emerging adjustable resources in the power system, such as data centers, 5 G base stations, electric vehicles, etc. Generate typical user behavior scenarios through Latin hypercube sampling and K-means clustering techniques to quantify the responsiveness differences of resources such as air conditioning loads; A comprehensive performance optimization model considering both structural and functional aspects was constructed by combining the electrical distance modularity index and flexibility balance index, and the Louvain algorithm was used to achieve dynamic partitioning of resource clusters. Taking the distribution network in Cangshan District, Fuzhou City as an example, the effectiveness of the model was verified. The results show that under the condition of weight balance (structural weight 0.5, functional weight 0.5), cluster partitioning balances topological compactness and regulatory complementarity, significantly improving the flexibility and robustness of the system.