Evaluation for Risk of Cascading Failures in Power Grids by Inverse-Community Structure

Abstract

Recently, the development of the Internet of Things (IoT) enables more comprehensive and intelligent analysis and defense for cascading failures in power grids. This paper summarizes power grids as Temporal Weighted Networks (TWN) which are different from conventional temporal networks. For TWN, the topological structure is fixed but weight distribution is time-varying. Then it is noted that for different operating states represented by different weight (power flow) distribution at different time sections, the risks of cascading failures would be completely different. Inspired by analysis of inter-subnetworks power shifts in cascading failures, Inverse-Community (IC) structure is proposed in TWN to intuitively identify the risk of cascading failures. IC describes a structure in weighted networks with several communities in which the weighted interaction between communities is significantly stronger than that within the same community. Furthermore, the conventional modularity is upgraded as Inverse Modularity (IM) to quantify the characteristic of IC structure in power networks. Subsequently, considers the risk of cascading failures represented by IM and the cost of power network operation, a security/economic dispatch (SED) method is designed to handle the optimal power dispatch issues. Simulation results prove a positive correlation between IM of power flow distribution and risk of cascading failures. Furthermore, the results on the IEEE 118-bus system demonstrate the effectiveness of the proposed SED method in mitigating cascading failure risks.