Electricity Spot Market Risk Identification and Multi-Dimensional Early Warning Based on Improved Transformer
Main Article Content
Abstract
Reliable risk identification and early warning in electricity spot markets are increasingly important for intelligent power communication infrastructures and electromagnetic information systems that support real-time monitoring and dispatching. To address the challenges posed by high-frequency, heterogeneous, and strongly coupled market data, this study proposes an improved Transformer-based framework for electricity spot market risk identification and multidimensional early warning. The proposed method integrates multi-source operational data through temporal embedding and multi-channel feature encoding, enabling effective representation of electricity price fluctuations, dispatch instructions, transaction behaviors, and load responses. An enhanced self-attention mechanism is employed to capture long-range temporal dependencies and dynamic interactions, while a lightweight classifier performs hierarchical risk categorization. Furthermore, a multi-dimensional warning mechanism incorporating spatial location, subsystem characteristics, and temporal risk evolution is established to support adaptive operational decision-making. Experimental results demonstrate an overall risk identification accuracy of 93.2%, an average F1-score of 0.88, a dispatch subsystem localization accuracy of 97.3%, and a trend identification accuracy of 94.2% with only 3.1 minutes of response delay during risk escalation. The proposed framework provides an effective solution for intelligent risk perception and dynamic monitoring in complex electricity markets and offers methodological insights for communication-enabled energy systems and electromagnetic sensing environments requiring reliable real-time information processing.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
P. Guo, K. Xiao, X. Wang, and D. Li, “Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things,” Global energy interconnection, vol. 7, no. 1, pp. 94-105, 2024, doi: 10.1016/j.gloei.2024.01.009.
Z. Dou, C. Zhang, W. Wang, and D. Wang, “Review on key technologies and typical applications of multi-station integrated energy systems,” Global Energy Interconnection, vol. 5, no. 3, pp. 309-327, 2022, doi: 10.1016/j.gloei.2022.06.009.
J. Yang, Z. Y. Dong, F. Wen, Q. Chen, and B. Liang, “Spot electricity market design for a power system characterized by high penetration of renewable energy generation,” Energy Conversion and Economics, vol. 2, no. 2, pp. 67-78, 2021, doi: 10.1049/enc2.12031.
L. Luo, Y. Song, Y. Liu, and X. Jiang, “The research on intelligent safety risk assessment and early warning mechanisms for power systems based on big data and artificial intelligence technology,” Advances in Resources Research, vol. 5, no. 2, pp. 666-688, 2025, doi: 10.50908/arr.5.2_666.
F. Aminifar, M. Abedini, T. Amraee, P. Jafarian, M. H. Samimi, and M. Shahidehpour, “A review of power system protection and asset management with machine learning techniques,” Energy Systems, vol. 13, no. 4, pp. 855-892, 2022, doi: 10.1007/s12667-021-00448-6.
J. R. Feng, M. Zhao, G. Yu, N. Kang, J. Zhang, Y. Guo, et al., “Dynamic risk assessment framework for fire of power critical infrastructure: The case study of UHV converter transformer,” Quality and Reliability Engineering International, vol. 41, no. 1, pp. 71-97, 2025, doi: 10.1002/qre.3645.
L. Xie, X. Zheng, Y. Sun, H. Tong, and B. Tony, “Massively digitized power grid: Opportunities and challenges of use-inspired AI,” Proceedings of the IEEE, vol. 111, no. 7, pp. 762-787, 2022, doi: 10.1109/JPROC.2022.3175070.
Y. Li, S. Chen, K. Hwang, X. Ji, Z. Lei, Y. Zhu, et al., “Spatio-temporal data fusion techniques for modeling digital twin City,” Geo-Spatial Information Science, vol. 28, no. 2, pp. 541-564, 2025, doi: 10.1080/10095020.2024.2350175.
B. M. Ampel, S. Samtani, H. Zhu, ChenH, and J. F. Nunamaker Jr, “Improving threat mitigation through a cybersecurity risk management framework: A computational design science approach,” Journal of Management Information Systems, vol. 41, no. 1, pp. 236-265, 2024, doi: 10.1080/07421222.2023.2301178.
S. V. Rzayeva, N. M. Piriyeva, and S. I. Ismayilova, “High and low voltage coordination in electrical power systems,” International Journal on Technical and Physical Problems of Engineering (IJTPE), vol. 17, no. 1, pp. 19-31, 2025.
A. M. Stanković, K. L. Tomsovic, F. De Caro, M. Braun, J. H. Chow, and N. ˇCukalevski, “Methods for analysis and quantification of power system resilience,” IEEE Transactions on Power Systems, vol. 38, no. 5, pp. 4774-4787, 2022, doi: 10.1109/TPWRS.2022.3212688.
I. M. Dudurych, “The impact of renewables on operational security: Operating power systems that have extremely high penetrations of non-synchronous renewable sources,” IEEE Power and Energy Magazine, vol. 19, no. 2, pp. 37-45, 2021, doi: 10.1109/MPE.2020.3043614.
K. Yu, L. Tan, S. Mumtaz, A. AI-Dulaimi, A. K. Bashir, and F. A. Khan, “Securing critical infrastructures: Deep-learningbased threat detection in IIoT,” IEEE Communications Magazine, vol. 59, no. 10, pp. 76-82, 2021, doi: 10.1109/MCOM.101.2001126.
C. Guan, Z. Li, and D. Lin, “Power system stability: Concepts, trends, and future challenges,” Advances in Resources Research, vol. 4, no. 1, pp. 55-66, 2024, doi: 10.50908/arr.4.1_55.
K. Alexander, L. Yuri, and K. Andrei, “Principles of constructing artificial intelligence systems and their application in electrical power industry,” Energy Systems Research, vol. 4(4(16)), pp. 63-78, 2021, doi: 10.38028/esr.2021.04.0006.