Data-Driven Proactive Early Warning of Grid Congestion Probability Based on Multiple Time Scales

Haobo Fu (), Ruizhuo Wang, Bingxu Zhai, Yuanzhuo Li, Pengyuan Li, Rui Zhang, Haoyuan He () and Siyang Liao
Additional contact information
Haobo Fu: State Grid Jibei Electric Power Company of China, Beijing 100052, China
Ruizhuo Wang: State Grid Jibei Electric Power Company of China, Beijing 100052, China
Bingxu Zhai: State Grid Jibei Electric Power Company of China, Beijing 100052, China
Yuanzhuo Li: State Grid Jibei Electric Power Company of China, Beijing 100052, China
Pengyuan Li: State Grid Jibei Electric Power Company of China, Beijing 100052, China
Rui Zhang: State Grid Jibei Electric Power Company of China, Beijing 100052, China
Haoyuan He: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
Siyang Liao: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China

Energies, 2025, vol. 18, issue 10, 1-17

Abstract: With the development of new power systems, the increased interactive demand on the load side, and the high proportion of renewable energy sources on the power side, grid congestion problems due to increased system uncertainty are becoming more frequent. In this context, grid congestion problems have become more and more frequent. In order to solve the problem of a lack of accuracy and predictability of the current scheduling method based on “passive” prediction, a data-driven active warning method based on the probability of grid congestion at multiple time scales is proposed. First, a multi-stage joint optimization feature selection model is constructed to capture the 12 feature sets that are most conducive to grid congestion warning from the massive grid history data containing 622 features. Then, a multi-time-scale prediction model based on a convolutional neural network and a bi-directional long and short-term memory network is constructed to realize the active early warning of the power system in the face of grid congestion events. Finally, the proposed method and model are verified with the actual operation data of the power grid in a province in China, and the computational results verify that the proposed method and model can realize the active early warning, which can help the dispatchers sense the development of grid congestion in advance and take control measures in time.

Keywords: data-driven; feature selection; machine learning; probabilistic prediction; grid congestion warning; active regulation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/10/2530/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/10/2530/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:10:p:2530-:d:1655381

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().