Energy consumption in smart grid sy

Energy consumption in smart grid systems has shortcomings such as instability and high dispersion under big data technology and information economy development. A two-layer Distributed Clustering Algorithm (DCA) is proposed by improving and optimizing the K-Means Clustering (KMC) and Affinity Propagation (AP) algorithms, aiming to evaluate DCA’s applicability in power system’s big data processing and find the information economic dispatch strategy suitable for new energy consumption in the power system. Then, DCA’s clustering results are analyzed. Second, user-side Demand Response (DR) flexibility is analyzed, and the incentive DR is introduced. Finally, a multi-period information economic dispatch model is built based on DCA, day-ahead dispatch, and real-time dispatch. The new energy consumption is analyzed through cases. Results demonstrate that the proposed DCA’s calculation time is lessened, and the iterations are reduced. The calculation only requires 5.23s, and the classification accuracy reaches 0.991. Case 2, corresponding to the model proposed, can consume new energy; besides, its incentive electricity price is higher than Case 1. Aggregators have maximized the revenue goals in Case 2. For example, Aggregator 1 has increased its revenue by 139.36% in Case 2. This multi-period information economic dispatch model can consume new energy and meet the DR on the user side.