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摘要
As the expansion of the integrated power markets, the power hub nodes need to be set as the aggregation nodes where the unified transactions can be carried out in the spot markets. This is the cornerstone of the development and the stable operation of the power financial market and is of great significance to the construction of a unified power market system and the improvement of market functions. The difficulty of the hub node design lies in the amount of the hub nodes to be determined to ensure the accurate coverage of the pricing nodes in the electricity market, reflecting the value of the electricity space. This paper proposes a quantity selection based on the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm classification and the t-SNE (t-Distributed Stochastic Neighbor Embedding) dimensionality reduction analysis for the key problem of the hub node design. Firstly, by comparing with the KPCA (Kernel Principal Component Analysis), the UMAP (Uniform Manifold Approximation and Projection, UMAP) and the other typical dimensionality reduction methods, it is demonstrated that the t-SNE has better dimensionality reduction effect on the data crowded high-dimensional node price dataset. Its data visualization is consistent with the expectation of dividing the pricing nodes into as independent classes as possible through dimensionality reduction. Secondly, the DBSCAN algorithm is used to remove the outliers and the deviations, and classify them on the basis of their density. The optimal number of classifications is determined by effectively selecting the best DBSCAN domain value through the Cross-Entropy. Finally, the accuracy and validity of the method are proved by a series of internal validity evaluation indexes of the classification, which provide a reasonable basis for the further hub area classification. ? 2023 Power System Technology Press.
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