1.School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China;2.Changjiang Survey Planning, Design and Research Co., Ltd., Wuhan 430010, China;3.New Energy (Photovoltaic) Industry Research Center, Qinghai University, Xining 810016, China

The tight coupling between different electric-heat-gas energy supply systems makes it difficult to identify the weak nodes in the integrated energy system (IES). From a data-driven perspective, a weak node identification method of IES based on the big data from the unified power flow model is proposed. Firstly, a unified power flow model for the IES is established based on the physical characteristics of the power, heat, and natural gas systems and their coupling links. Secondly, the high-dimensional state matrix is constructed using historical and real-time data of unbalanced initial value of the power flow model. Thirdly, the M-P law and ring law are used to qualitatively analyze the operation states of the system. Finally, the average spectral radius (MSR), the maximum eigenvalue of the state matrix, and the node weakness index are calculated with the combination of the entropy theory. The proposed method maps the node weakness information to data changes, and the identification of the relative weakness of nodes in IES are realized by sorting the index values of the node weakness. The simulation results verify the effectiveness of the proposed method.