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

arXiv 2211.04584 ↗First posted 2 Nov 2022cs.AI cs.SY eess.SYcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 45 references that are in the map. First posted on arXiv 2 Nov 2022.

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14 of 45 references.

References · 14

  1. 2019Tackling Climate Change with Machine Learningcited by 264
  2. 2017Model-Free Renewable Scenario Generation Using Generative Adversarial Networkscited by 51
  3. 2019Learning to Solve Large-Scale Security-Constrained Unit Commitment Problemscited by 38
  4. 2018Learning for Constrained Optimization: Identifying Optimal Active Constraint Setscited by 36
  5. 2021A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy Gridscited by 11
  6. 2018Optimal Tap Setting of Voltage Regulation Transformers Using Batch Reinforcement Learningcited by 8
  7. 2020A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sectorcited by 8
  8. 2022Massively Digitized Power Grid: Opportunities and Challenges of Use-inspired AIcited by 6
  9. 2020Fully Decentralized Reinforcement Learning-based Control of Photovoltaics in Distribution Grids for Joint Provision of Real and Reactive Powercited by 5
  10. 2019Nested Reinforcement Learning Based Control for Protective Relays in Power Distribution Systemscited by 4
  11. 2019Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approachcited by 4
  12. 2018Chance-Constrained Outage Scheduling using a Machine Learning Proxycited by 2
2 more
  1. 2019Ensemble Learning Based Convex Approximation of Three-Phase Power Flowcited by 2
  2. 2017Scenario-based Economic Dispatch with Uncertain Demand Responsecited by 1

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