A Multilayer Resilience Assessment of Power Distribution Systems with Reliability Models, Service Restoration, and Dynamic Bayesian Networks

Abstract

Electrical energy is fundamental for contemporary society since failures directly impact other critical infrastructures such as water and gas distribution, hospitals, or banking services. Consequently, resilience, which is the capability of a system to handle high-impact low probability events, is a crucial aspect of such systems. Efficient resilience assessment methods are essential to achieving high-performance, resilient energy systems. This chapter introduces a multilayer method to address several factors of power distribution systems’ resilience. Reliability regressions model the failures’ instant and duration given a weather scenario, a dynamic Bayesian network (DBN) models how probabilities of failure propagate on the system’s physical connections, and a service restoration through switching operations, and field crew routing is obtained through an optimization algorithm for a given set of failures. Information related to these factors has the potential to be structured in a layered manner for a better understanding of the dynamic interaction among different information like weather, routes, power grid, and historical events logs. The ability to model these relationships enables the inference of the system resilience for different inputs during analysis. Resilience can also be inferred by considering the uncertainties associated with these layers due to DBN’s nature. A case study is presented to show the efficacy of this procedure. The findings showed its ability to evaluate the resilience of power distribution systems in the face of uncertainty and the considered aspects for different weather scenarios.