Research Progress on Computing Power Network for the Power Industry

With the development of 5G and artificial intelligence technologies, the computing power network aims to address the uneven distribution of computing centers in the power industry and the technical barriers on the supply side, promoting computing power as a social-level service. This article explores key technologies such as data center interconnection architecture, network and resource scheduling with quality of service awareness, collaborative optimization of computing networks, and energy efficiency optimization in computing centers. It emphasizes the importance of establishing computing resource standards and data interface standards. Additionally, the article delves into the the application of artificial intelligence technology in the power industry including power grid load forecasting, power dispatching, and monitoring of transmission. Finally, the article provides an outlook on the future development of the computing power network, highlighting its potential and challenges in the industry's digital transformation.

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Publication Details

Published
2024-08-21
DOI
https://doi.org/10.1109/icicn62625.2024.10761814
Citations
5
Primary Topic
Power Systems and Renewable Energy
Type
article
Field-Weighted Citation Impact
0.85

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article

Research Progress on Computing Power Network for the Power Industry

Xingping Wu, Congfeng Jiang, Jianing Niu, Chunpeng Wu et al.
5 citations
Power Systems and Renewable Energy
0.85
article

Research Progress on Computing Power Network for the Power Industry

Xingping Wu, Congfeng Jiang, Jianing Niu, Chunpeng Wu, Long Yan, Yun Chen, Yonghe Guo, Zhipeng Feng, Yue Wang
article en
5 citations

Abstract

With the development of 5G and artificial intelligence technologies, the computing power network aims to address the uneven distribution of computing centers in the power industry and the technical barriers on the supply side, promoting computing power as a social-level service. This article explores key technologies such as data center interconnection architecture, network and resource scheduling with quality of service awareness, collaborative optimization of computing networks, and energy efficiency optimization in computing centers. It emphasizes the importance of establishing computing resource standards and data interface standards. Additionally, the article delves into the the application of artificial intelligence technology in the power industry including power grid load forecasting, power dispatching, and monitoring of transmission. Finally, the article provides an outlook on the future development of the computing power network, highlighting its potential and challenges in the industry's digital transformation.

State Grid Corporation of China (China) (CN), Hangzhou Dianzi University (CN)
State Grid Corporation of China
Openalex Percentile: Top 23%
Power Systems and Renewable Energy
0.85
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Research Progress on Computing Power Network for the Power Industry — Xingping Wu, Congfeng Jiang, et al. · (2024) | TGRS Research Map | TGRS