TY - JOUR
T1 - Energy system digitization in the era of AI
T2 - A three-layered approach toward carbon neutrality
AU - Xie, Le
AU - Huang, Tong
AU - Zheng, Xiangtian
AU - Liu, Yan
AU - Wang, Mengdi
AU - Vittal, Vijay
AU - Kumar, P. R.
AU - Shakkottai, Srinivas
AU - Cui, Yi
N1 - Publisher Copyright:
© 2022 The Authors
PY - 2022/12/9
Y1 - 2022/12/9
N2 - 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.
AB - 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.
KW - DSML2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem
KW - artificial intelligence
KW - carbon neutrality
KW - electric energy systems
UR - http://www.scopus.com/inward/record.url?scp=85145586270&partnerID=8YFLogxK
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U2 - 10.1016/j.patter.2022.100640
DO - 10.1016/j.patter.2022.100640
M3 - Review article
C2 - 36569552
AN - SCOPUS:85145586270
SN - 2666-3899
VL - 3
JO - Patterns
JF - Patterns
IS - 12
M1 - 100640
ER -