Xie Le, Huang Tong, Zheng Xiangtian, Liu Yan, Wang Mengdi, Vittal Vijay, Kumar P R, Shakkottai Srinivas, Cui Yi
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
Department of Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USA.
Patterns (N Y). 2022 Dec 9;3(12):100640. doi: 10.1016/j.patter.2022.100640.
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.
向碳中和电力的转型是应对气候变化方面最大的游戏规则改变者之一，因为它应对了来自两个最大排放部门——电力和交通——消除碳排放的双重挑战。向碳中和电网的转型给现代电网规划和运营的传统模式带来了重大挑战。许多挑战源于决策的规模以及与能源供需相关的不确定性。人工智能（AI）可能会对加速碳中和转型的速度和规模产生变革性影响，因为电网中的许多决策过程都可以被视为经典的机器学习任务，尽管具有挑战性。我们指出，为了扩大人工智能对电能系统碳中和转型的影响，最初为其他应用开发的人工智能算法应在技术、市场和政策三个层面进行调整。
