Towards an AI-friendly cross-timescale simulation and analysis platform for electric distribution systems
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2022-03-01
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| Series: | iEnergy |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.23919/IEN.2022.0009 |
| _version_ | 1811294891374804992 |
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| author | Dongqi Wu Rayan El Helou Le Xie |
| author_facet | Dongqi Wu Rayan El Helou Le Xie |
| author_sort | Dongqi Wu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T05:24:14Z |
| format | Article |
| id | doaj.art-de957b6b7e0a40d3bba30f18a8e3f1ff |
| institution | Directory Open Access Journal |
| issn | 2771-9197 |
| language | English |
| last_indexed | 2024-04-13T05:24:14Z |
| publishDate | 2022-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | iEnergy |
| spelling | doaj.art-de957b6b7e0a40d3bba30f18a8e3f1ff2022-12-22T03:00:39ZengTsinghua University PressiEnergy2771-91972022-03-011113314010.23919/IEN.2022.0009Towards an AI-friendly cross-timescale simulation and analysis platform for electric distribution systemsDongqi Wu0Rayan El Helou1Le Xie2Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USADepartment of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USADepartment of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USASubstantial 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.https://www.sciopen.com/article/10.23919/IEN.2022.0009power distribution systemmachine learningsimulation platform |
| spellingShingle | Dongqi Wu Rayan El Helou Le Xie Towards an AI-friendly cross-timescale simulation and analysis platform for electric distribution systems iEnergy power distribution system machine learning simulation platform |
| title | Towards an AI-friendly cross-timescale simulation and analysis platform for electric distribution systems |
| title_full | Towards an AI-friendly cross-timescale simulation and analysis platform for electric distribution systems |
| title_fullStr | Towards an AI-friendly cross-timescale simulation and analysis platform for electric distribution systems |
| title_full_unstemmed | Towards an AI-friendly cross-timescale simulation and analysis platform for electric distribution systems |
| title_short | Towards an AI-friendly cross-timescale simulation and analysis platform for electric distribution systems |
| title_sort | towards an ai friendly cross timescale simulation and analysis platform for electric distribution systems |
| topic | power distribution system machine learning simulation platform |
| url | https://www.sciopen.com/article/10.23919/IEN.2022.0009 |
| work_keys_str_mv | AT dongqiwu towardsanaifriendlycrosstimescalesimulationandanalysisplatformforelectricdistributionsystems AT rayanelhelou towardsanaifriendlycrosstimescalesimulationandanalysisplatformforelectricdistributionsystems AT lexie towardsanaifriendlycrosstimescalesimulationandanalysisplatformforelectricdistributionsystems |