Modern energy systems are facing the risk of extreme events more frequently, making it essential to enhance their management under meteorological disasters. The tight interdependency across critical infrastructures, along with a diverse range of resources, provides unique opportunities for creating more resilient energy supply. To this end, it is necessary to establish mechanisms and scheduling methods for different types of resources to participate in load restoration, through coordination and making full use of idle resources in different systems. This chapter investigates resilience enhancement in interdependent systems including power distribution networks, natural gas systems, and urban transportation. Regarding the traffic-power interdependence, we focus on methods for integrating electric buses into resilience initiatives. In particular, for the pre-disaster allocation of electric buses, a two-stage robust optimization model that accounts for various damage scenarios is established, and an efficient solution method based on a column-and-constraint generation decomposition algorithm to ensure optimal placement of electric buses is proposed. To address the post-disaster scheduling of electric buses, we assume that some buses maintain their passenger transport function while others are redirected to provide load support. The corresponding mixed-integer programming model is developed based on a spatial-temporal network framework. Regarding the coupling of natural gas network and power distribution network, we focus on using long-tube trailers to transport liquefied natural gas to enhance resilience against extreme events such as snowstorms. For the pre-disaster deployment of long-tube trailers, we propose a two-stage stochastic programming model along with a solution method using a penalty-based Gauss-Seidel approach. For post-disaster real-time scheduling of long-tube trailers, we present an online scheduling strategy based on a finite-state machine, allowing for rapid adjustments to trailer schedules based on the system’s real-time status.