Energy system digitization in the era of AI: A three-layered approach toward carbon neutrality

Le Xie; Tong Huang; Xiangtian Zheng; Yan Liu; Mengdi Wang; Vijay Vittal; P R Kumar; Srinivas Shakkottai; Yi Cui

doi:10.1016/j.patter.2022.100640

Energy system digitization in the era of AI: A three-layered approach toward carbon neutrality

Patterns (N Y). 2022 Dec 9;3(12):100640. doi: 10.1016/j.patter.2022.100640.

Authors

Le Xie¹, Tong Huang², Xiangtian Zheng¹, Yan Liu³, Mengdi Wang^{4

5

6}, Vijay Vittal⁷, P R Kumar¹, Srinivas Shakkottai¹, Yi Cui⁸

Affiliations

¹ Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
² Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA.
³ Computer Science Department, University of Southern California, Los Angeles, CA 90007, USA.
⁴ Department of Electrical Engineering, Princeton University, Princeton, NJ 08540, USA.
⁵ DeepMind, Mountain View, CA 94043, USA.
⁶ Center for Statistics and Machine Learning, Princeton University, Princeton, NJ 08540, USA.
⁷ School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85281, USA.
⁸ Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA.

Abstract

The transition toward carbon-neutral electricity is one of the biggest game changers in addressing climate change since it addresses the dual challenges of removing carbon emissions from the two largest sectors of emitters: electricity and transportation. The transition to a carbon-neutral electric grid poses significant challenges to conventional paradigms of modern grid planning and operation. Much of the challenge arises from the scale of the decision-making and the uncertainty associated with the energy supply and demand. Artificial intelligence (AI) could potentially have a transformative impact on accelerating the speed and scale of carbon-neutral transition, as many decision-making processes in the power grid can be cast as classic, though challenging, machine-learning tasks. We point out that to amplify AI's impact on carbon-neutral transition of the electric energy systems, the AI algorithms originally developed for other applications should be tailored in three layers of technology, markets, and policy.

Keywords: artificial intelligence; carbon neutrality; electric energy systems.

Publication types

Review