Vulnerability Assessment for Power Grids Based on Inverse-Community Structure

With the increasing complexity of power networks, the vulnerability assessment of power systems is a crucial issue to maintain the safe operation of power grids. This paper proposes the concept of inverse community (IC) to assess the vulnerability of power networks based on structural characteristic. 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. Additionally, the conventional modularity is upgraded as Inverse Modularity (IM) to quantify the characteristic of IC structure in power networks. Moreover, to find the state of a power network with the most significant IC feature (largest IM), the genetic algorithm (GA) is redesigned based on IM by adjusting the actual output power of generators and loads conditions as the decision variables. This largest IM is considered as a metric for network vulnerability which essentially depends on the network structure and static parameters. The capability of the proposed metric and method is demonstrated via the IEEE-118 and IEEE-300 bus systems. Simulation results prove that the IC structure can assess the network's vulnerability., i.e., the stronger IC feature of the power network represents that the network is more vulnerable.