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iEnergy 2022, 1(1): 133-140 https://doi.org/10.23919/IEN.2022.0009
Article | Open Access | Issue | Published: 25 March 2022

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

Show Author's Information Dongqi Wu,  Rayan El Helou,  Le Xie( )
Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA
Keywords:
Power distribution system, machine learning, simulation platform
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Wu D, Helou RE, Xie L. Towards an AI-friendly cross-timescale simulation and analysis platform for electric distribution systems. iEnergy, 2022, 1(1): 133-140. https://doi.org/10.23919/IEN.2022.0009
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Abstract Full text About this article

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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About this article

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

Show Author's information Dongqi Wu, Rayan El Helou, Le 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

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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

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© 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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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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