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Article | Open Access

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

Dongqi Wu, Rayan El Helou, Le Xie( )
Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA
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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

machine learningPower distribution systemsimulation platform

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iEnergy
Volume 1 Issue 1,
March 2022
Pages 133-140
DOI: 10.23919/IEN.2022.0009
Cite this article:
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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Received: 01 January 2022
Revised: 09 March 2022
Accepted: 10 March 2022
Published: 25 March 2022
© The author(s) 2022

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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