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

arXiv: https://arxiv.org/abs/2211.04584 · HTML: https://mlsanity.com/paper/2211.04584/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 14 of 14)

- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Model-Free Renewable Scenario Generation Using Generative Adversarial Networks](https://mlsanity.com/paper/1707.09676/) (2017)
- [Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems](https://mlsanity.com/paper/1902.01697/) (2019)
- [Learning for Constrained Optimization: Identifying Optimal Active Constraint Sets](https://mlsanity.com/paper/1802.09639/) (2018)
- [A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Grids](https://mlsanity.com/paper/2110.06324/) (2021)
- [A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector](https://mlsanity.com/paper/2005.06631/) (2020)
- [Optimal Tap Setting of Voltage Regulation Transformers Using Batch Reinforcement Learning](https://mlsanity.com/paper/1807.10997/) (2018)
- [Fully Decentralized Reinforcement Learning-based Control of Photovoltaics in Distribution Grids for Joint Provision of Real and Reactive Power](https://mlsanity.com/paper/2008.01231/) (2020)
- [Massively Digitized Power Grid: Opportunities and Challenges of Use-inspired AI](https://mlsanity.com/paper/2205.05180/) (2022)
- [Nested Reinforcement Learning Based Control for Protective Relays in Power Distribution Systems](https://mlsanity.com/paper/1906.10815/) (2019)
- [Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach](https://mlsanity.com/paper/1911.04894/) (2019)
- [Chance-Constrained Outage Scheduling using a Machine Learning Proxy](https://mlsanity.com/paper/1801.00500/) (2018)
- [Ensemble Learning Based Convex Approximation of Three-Phase Power Flow](https://mlsanity.com/paper/1909.05748/) (2019)
- [Scenario-based Economic Dispatch with Uncertain Demand Response](https://mlsanity.com/paper/1711.11441/) (2017)

## Abstract (arXiv)

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.