Dynamic Bayesian Network Based Approach for Modeling and Assessing Resilience of Smart Grid System

Smart grid is an emerging technology in the field of power generation and linked to different critical infrastructures, including telecommunication, transportation, water supply, and fuel distribution. There exist many uncertainties in the complex smart grid systems because of rapidly increasing new technologies. Disruption in the smart grid may lead to uncertainties because of many reasons, including natural disasters, such as snowstorm, lightning, hurricane, as well as human errors to machinery failure. After the occurrence of failure, it is prudent to discover approaches for recovering from the damage instead of just focusing on preventing the failure before it occurs. The static Bayesian Network (BN) approach concentrates only on cases that are in equilibrium; however, the system may fluctuate with time because of disturbing events. Dynamic Bayesian network (DBN) based approach can be appropriate to consider temporal dimension for resilience assessment of such time changing circumstances. Most of the methods prevalent in the extant literature only design and assess resilience based on performance loss due to disruption but do not the evaluate the associated probabilities while the state of the system will go through different resilience capacities such as absorption, adaptation, restoration, learning to achieve its optimal functionality over time. This chapter adopts probabilistic assessment method leveraging dynamic Bayesian network based approach to assess the resilience of the smart grid system. It incorporates temporal analysis in absorption, adaptation, restoration, and learning for analysis of system functionality considering scenarios both during the disruption and after the disruption.