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

Hongyan Ma1,2

  • 1School of Humanities and Social Sciences, Xi'an Jiaotong University, Xi'an, Shaanxi Province, 710049, China.

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Summary
This summary is machine-generated.

This study introduces a two-layer Distributed Clustering Algorithm (DCA) for efficient power system big data processing and economic dispatch strategies for new energy consumption. The DCA model effectively integrates demand response, maximizing aggregator income and new energy utilization.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Data Science

Background:

  • Power systems face challenges with big data processing and integrating new energy sources.
  • Efficient economic dispatch strategies are crucial for managing energy consumption and costs.

Purpose of the Study:

  • To evaluate the Distributed Clustering Algorithm (DCA) for power system big data processing.
  • To develop an economic dispatch strategy for new energy consumption in power systems.
  • To analyze the impact of incentive Demand Response (DR) on user flexibility.

Main Methods:

  • A two-layer DCA algorithm combining K-Means Clustering (KMC) and Affinity Propagation (AP).
  • Integration of incentive Demand Response (DR) and analysis of user-side flexibility.
  • Construction of a multi-period information economic dispatch model combining day-ahead and real-time schemes.

Main Results:

  • The two-layer DCA achieved a calculation time of 5.23s with high classification accuracy (0.991).
  • The proposed model demonstrated effective new energy consumption and maximized aggregator income.
  • The multi-period economic dispatch model successfully balanced new energy consumption with user-side DR requirements.

Conclusions:

  • The developed two-layer DCA is efficient for power system big data processing.
  • The multi-period information economic dispatch model optimizes new energy consumption and aggregator revenue.
  • The study validates the effectiveness of integrating DR for enhanced power system operation.