Massively Digitized Power Grid: Opportunities and Challenges of Use-Inspired AI

This article presents a use-inspired perspective of the opportunities and challenges in a massively digitized power grid. It argues that the intricate interplay of data availability, computing capability, and artificial intelligence (AI) algorithm development are the three key factors driving the ad...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings of the IEEE Vol. 111; no. 7; pp. 762 - 787
Main Authors: Xie, Le, Zheng, Xiangtian, Sun, Yannan, Huang, Tong, Bruton, Tony
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Subjects:
Algorithms
Artificial intelligence
Artificial intelligence (AI)
Computation
Data models
data-driven algorithms
decarbonization
Digitization
Electric power grids
industry use cases
Low-carbon economy
Machine learning
power grid
Power grids
Power systems
Reliability
Renewable energy sources
Security
ISSN:0018-9219, 1558-2256
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This article presents a use-inspired perspective of the opportunities and challenges in a massively digitized power grid. It argues that the intricate interplay of data availability, computing capability, and artificial intelligence (AI) algorithm development are the three key factors driving the adoption of digitized solutions in the power grid. The impact of these three factors on critical functions of power system operation and planning practices is reviewed and illustrated with industrial practice case studies. Open challenges and research opportunities for data, computing, and AI algorithms are articulated within the context of the power industry's tremendous decarbonization efforts.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
EE0009031
USDOE
ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2022.3175070