@article{Zhou2022, 
author = {Mike Zhou and Jianfeng Yan and Qianhong Wu},
title = {Graph Computing and Its Application in Power Grid Analysis},
year = {2022},
journal = {CSEE Journal of Power and Energy Systems},
volume = {8},
number = {6},
pages = {1550-1557},
keywords = {CIM model merge, contingency analysis, Graph computing, in-memory computing, network topology processing, power grid analysis, subgraph processing},
url = {https://www.sciopen.com/article/10.17775/CSEEJPES.2021.00430},
doi = {10.17775/CSEEJPES.2021.00430},
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.}
}