Extraction of Typical Operating Scenarios of New Power System Based on Deep Time Series Aggregation
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916674323611648 |
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| author | Qu, Zhaoyang Zhang, Zhenming Qu, Nan Zhou, Yuguang Li, Yang Jiang, Tao Li, Min Long, Chao |
| author_facet | Qu, Zhaoyang Zhang, Zhenming Qu, Nan Zhou, Yuguang Li, Yang Jiang, Tao Li, Min Long, Chao |
| contents | Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system. This study proposed a novel deep time series aggregation scheme (DTSAs) to generate typical operational scenarios, considering the large amount of historical operational snapshot data. Specifically, DTSAs analyze the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios. A gramian angular summation field (GASF) based operational scenario image encoder was designed to convert operational scenario sequences into high-dimensional spaces. This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models. The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots. Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional featurescreening methods. In addition, experiments with different new energy access ratios were conducted to verify the robustness of the proposed method. DTSAs enables dispatchers to master the operation experience of the power system in advance, and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14493 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Extraction of Typical Operating Scenarios of New Power System Based on Deep Time Series Aggregation Qu, Zhaoyang Zhang, Zhenming Qu, Nan Zhou, Yuguang Li, Yang Jiang, Tao Li, Min Long, Chao Machine Learning Systems and Control Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system. This study proposed a novel deep time series aggregation scheme (DTSAs) to generate typical operational scenarios, considering the large amount of historical operational snapshot data. Specifically, DTSAs analyze the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios. A gramian angular summation field (GASF) based operational scenario image encoder was designed to convert operational scenario sequences into high-dimensional spaces. This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models. The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots. Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional featurescreening methods. In addition, experiments with different new energy access ratios were conducted to verify the robustness of the proposed method. DTSAs enables dispatchers to master the operation experience of the power system in advance, and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy. |
| title | Extraction of Typical Operating Scenarios of New Power System Based on Deep Time Series Aggregation |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2408.14493 |