Graph Computing and Its Application in Power Grid Analysis

Mike Zhou; Jianfeng Yan; Qianhong Wu

doi:10.17775/CSEEJPES.2021.00430

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Graph Computing and Its Application in Power Grid Analysis

Regular Papers | 更新时间：2026-10-15

- Graph Computing and Its Application in Power Grid Analysis
- CSEE Journal of Power and Energy Systems Vol. 8, Issue 6, Pages: 1550-1557(2022)
- 作者机构：
  
  China Electric Power Research Institute,Beijing,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2021.00430
  CLC：
- Published：01 November 2022
- 稿件说明：
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Mike Zhou, Jianfeng Yan, Qianhong Wu. Graph Computing and Its Application in Power Grid Analysis[J]. CSEE Journal of Power and Energy Systems, 2022, 8(6): 1550-1557.
DOI：

Mike Zhou, Jianfeng Yan, Qianhong Wu. Graph Computing and Its Application in Power Grid Analysis[J]. CSEE Journal of Power and Energy Systems, 2022, 8(6): 1550-1557. DOI： 10.17775/CSEEJPES.2021.00430.

摘要

Abstract

Approaches to apply graph computing to power grid analysis are systematically explained using real-world application examples. Through exploring the nature of the power grid and the characteristics of power grid analysis

the guidelines for selecting appropriate graph computing techniques for the application to power grid analysis are outlined. A custom graph model for representing the power grid for the analysis and simulation purpose and an in-memory computing (IMC) based graph-centric approach with a shared-everything architecture are introduced. Graph algorithms

including network topology processing and subgraph processing

and graph computing application scenarios

including in-memory computing

contingency analysis

and Common Information Model (CIM) model merge

are presented.

关键词

Keywords

references

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29. EPRI Report. CIM-based power system network model merge (Phase-1) . [Online ] . Available: https://www.researchgate.net/publication/345544860_CIM-based_Power_System_Network_Model_Merge_Phase-1.

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Related Institution

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