基于统一潮流大数据的综合能源系统薄弱点辨识方法

Identification Method for Weak Nodes of Integrated Energy System Based on Big Data of Unified Power Flow

电-热-气不同供能系统间的紧密耦合使得综合能源系统薄弱点辨识困难。从数据驱动的角度,提出一种基于统一潮流模型大数据的综合能源系统薄弱点辨识方法。首先,基于电力、热力、天然气系统及其耦合环节的物理特性,建立综合能源系统的统一潮流模型;然后,利用潮流模型中不平衡量初值的历史和实时数据构建高维状态矩阵;接着,运用M-P律和圆环律定性分析系统运行状况;最后,计算状态矩阵的平均谱半径和最大特征值,并结合熵理论构建节点薄弱性指标。该方法将节点薄弱信息映射至数据变化中,通过对节点薄弱性指标值的排序实现综合能源系统中节点相对薄弱性的辨识,并通过仿真结果验证了所提方法的有效性。

The tight coupling between different electricity-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.