Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality

TL;DR

This paper discusses a three-layered AI-driven approach to transform energy systems towards carbon neutrality, addressing challenges in grid planning, operation, and decision-making under uncertainty.

Contribution

It introduces a novel three-layer framework integrating technology, markets, and policy to enhance AI's role in achieving a carbon-neutral electric grid.

Findings

AI can significantly accelerate the transition to carbon neutrality.

Tailoring AI algorithms across three layers improves decision-making in energy systems.

The approach addresses scale and uncertainty challenges in modern grid management.

Abstract

The transition towards 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.