Grid Resilience and Energy Storage: Leveraging Machine Learning for Grid Services and Ancillary

Zhengjie Yang

doi:10.20944/preprints202407.0132.v1

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 30 June 2024 / Approved: 1 July 2024 / Online: 2 July 2024 (08:16:07 CEST)

This paper reviews the multiple roles of machine learning in improving the resilience of power grids, especially in applying new energy storage technologies. Energy storage technologies, such as compressed air energy storage, flywheel energy storage, and superconducting coil energy storage, significantly improve the power grid's ability to respond to load fluctuations and emergencies through intelligent control and optimisation of machine learning algorithms. This not only helps to optimise energy dispatch and improve the operational efficiency and flexibility of the grid but also significantly improves the stability and reliability of the grid so that it is better able to meet future challenges and needs.

Grid Resilience; NYISO Market; Machine learning; Reinforcement learning; Data analysis

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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Grid Resilience and Energy Storage: Leveraging Machine Learning for Grid Services and Ancillary

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