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Substantial changes are occurring in electric distribution systems due to ambitious targets towards carbon-neutrality in many regions around the world. One of the key challenges is how to analyze the interactions of massive amount of energy end-users with the electric distribution grid operator. In this paper, we introduce a comprehensive simulation platform, AI4Dist, that is capable to perform a wide collection of distribution system studies that capture multiple timescales ranging from market planning to transient event analysis. AI4Dist is designed to effortlessly integrate with off-the-shelf machine learning packages and algorithm implementations. We envision that AI4Dist will serve as a platform to empower researchers with different expertise to contribute to the development of low carbon electricity sector.


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Towards an AI-friendly cross-timescale simulation and analysis platform for electric distribution systems

Show Author's information Dongqi WuRayan El HelouLe Xie( )
Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA

Abstract

Substantial changes are occurring in electric distribution systems due to ambitious targets towards carbon-neutrality in many regions around the world. One of the key challenges is how to analyze the interactions of massive amount of energy end-users with the electric distribution grid operator. In this paper, we introduce a comprehensive simulation platform, AI4Dist, that is capable to perform a wide collection of distribution system studies that capture multiple timescales ranging from market planning to transient event analysis. AI4Dist is designed to effortlessly integrate with off-the-shelf machine learning packages and algorithm implementations. We envision that AI4Dist will serve as a platform to empower researchers with different expertise to contribute to the development of low carbon electricity sector.

Keywords:

Power distribution system, machine learning, simulation platform
Received: 01 January 2022 Revised: 09 March 2022 Accepted: 10 March 2022 Published: 25 March 2022 Issue date: March 2022
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Publication history

Received: 01 January 2022
Revised: 09 March 2022
Accepted: 10 March 2022
Published: 25 March 2022
Issue date: March 2022

Copyright

© The author(s) 2022

Acknowledgements

Acknowledgements

This work is supported in part by the Breakthrough Energy Sciences, and in part by the Texas A&M Engineering Experiment Station Smart Grid Center.

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