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Volume 13 Issue 4
Apr.  2026

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  >  13(4): 837-853
B. Zhang, D. Yue, C. Dou, D. Yuan, L. Xu, and H. Li, “Cyber-physical coordinated bi-level active power control for active distribution network considering transmission congestion,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 4, pp. 837–853, Apr. 2026. doi: 10.1109/JAS.2025.125555
Citation: B. Zhang, D. Yue, C. Dou, D. Yuan, L. Xu, and H. Li, “Cyber-physical coordinated bi-level active power control for active distribution network considering transmission congestion,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 4, pp. 837–853, Apr. 2026. doi: 10.1109/JAS.2025.125555
shu

Cyber-Physical Coordinated Bi-Level Active Power Control for Active Distribution Network Considering Transmission Congestion

doi: 10.1109/JAS.2025.125555
Funds:  This work was supported in part by the National Natural Science Foundation of China (62293504, 62293505, 62303242), the Natural Science Foundation of Jiangsu Province (BK20220395), the China Postdoctoral Science Foundation (2023M731780), the Young Elite Scientists Sponsorship Program (YESS20240325), and the Youth Talent Support Project of Jiangsu Province (JSTJ-2024-443)
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    Abstract
  • The degree of active power fluctuation is a key indicator for assessing the stability of active distribution networks. However, with the increasing clustering of distributed resources within these networks and the deepening integration of cyber-physical systems, uncertainties arising from cyber and physical domains, e.g., load variations and transmission congestion, will compound and exacerbate power fluctuations. Unlike existing methods that use cyber-physical cut-off control or firewall-based passive defenses, this paper proposes a bi-level active power control method based on a cyber-physical cooperation perspective to address these issues. At the upper level, which encompasses source-grid-storage clusters: in the physical layer, an active power support approach is proposed, which incorporates multi-factor matching while considering flow constraints to achieve multi-objective optimization regulation. In the cyber layer, we propose data sensitivity calculations along with demand-driven path planning techniques to ensure that planned paths align with regulatory requirements. At the lower level, focusing on in-cluster resources: in the physical layer, a multi-resource distributed control method based on fault-tolerance principles and a virtual leader-following consensus algorithm is proposed, which enables flexible responses to cluster commands while defending against light congestion interference. In the cyber layer, an event-triggered path reconstruction method is proposed to defend against heavy congestion interference. The proposed methodology effectively harnesses the aggregation control capabilities of massive resources and facilitates an active defense against network congestion issues. Case studies show that these methods can generate optimal control commands for aggregators and internal resources within seconds to mitigate power fluctuations while ensuring reliable network performance in both planning and operational dimensions.

     

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