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Power system low delay resource scheduling model based on edge computing node.

Ying Zhao1, Hua Ye2

  • 1Yunnan Electric Power Grid Company, Kunming, 650011, Yunnan, China. anxing483951@163.com.

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Summary
This summary is machine-generated.

This study introduces a two-phase heuristic algorithm using edge computing to manage massive smart grid data. The algorithm optimizes virtual machine placement and sequencing, significantly reducing power system computation time and enhancing resource utilization.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Data Science

Background:

  • The proliferation of intelligent devices in power systems leads to exponential growth in network nodes.
  • Effective processing of massive smart grid data is crucial for stable power system operation, especially with inter-area and inter-voltage level cooperation.
  • Current complex calculations in power systems face limitations in reducing operation time and improving execution efficiency.

Purpose of the Study:

  • To propose an efficient two-phase heuristic algorithm based on edge computing for power system distributed computing.
  • To minimize computation time and enhance execution efficiency in smart grid data processing.
  • To improve system resource utilization through optimized virtual machine configuration.

Main Methods:

  • A two-phase heuristic algorithm leveraging edge computing is proposed.
  • The critical path algorithm is employed for virtual machine sequencing in main and coordination partitions to minimize computing time.
  • The minimum cut algorithm is used for sub-partitions to reduce traffic interaction.
  • An improved best fit algorithm is utilized for virtual machine placement to prevent inefficient configurations.

Main Results:

  • The proposed algorithm demonstrates improved calculation efficiency, particularly when coordinated partition calculations are within the target partition.
  • Edge computing's proximity to data sources results in reduced data transmission time compared to cloud computing.
  • The algorithm effectively reduces power system computation time and enhances system resource utilization.

Conclusions:

  • The developed algorithm offers an effective solution for virtual machine configuration in edge computing for power systems.
  • This approach significantly reduces computation time and boosts system resource efficiency in smart grids.
  • Edge computing provides a viable and efficient alternative to cloud computing for processing large-scale power system data.