The role of clustering algorithm-based big data processing in information economy development

The purposes are to evaluate the Distributed Clustering Algorithm (DCA) applicability in the power system's big data processing and find the information economic dispatch strategy suitable for new energy consumption in power systems. A two-layer DCA algorithm is proposed based on K-Means Clustering (KMC) and Affinity Propagation (AP) clustering algorithms. Then the incentive Demand Response (DR) is introduced, and the DR flexibility of the user side is analyzed. Finally, the day-ahead dispatch and real-time dispatch schemes are combined, and a multi-period information economic dispatch model is constructed. The algorithm performance is analyzed according to case analyses of new energy consumption. Results demonstrate that the two-layer DCA's calculation time is 5.23s only, the number of iterations is small, and the classification accuracy rate reaches 0.991. Case 2 corresponding to the proposed model can consume the new energy, and the income of the aggregator can be maximized. In short, the multi-period information economic dispatch model can consume the new energy and meet the DR of the user side.

Automated summary

This study aims to evaluate the applicability of the Distributed Clustering Algorithm in big data processing in power systems. A two-layer DCA algorithm based on K-Means Clustering and Affinity Propagation is proposed, combined with the Incentive Demand Response and a multi-period information economic dispatch model. Results show that the model can effectively consume new energy and meet the demand of the user side.