Contingency research to find optimal operations and post-contingency recovery plans in distribution networks has gained a major attention in recent years. To this end, we consider a multi-period optimal power flow (OPF) problem in distribution networks, subject to the N-1 contingency where a line or distributed energy resource fails. The contingency can be modeled as a stochastic disruption, an event with random magnitude and timing. On the other hand, we incorporate a demand uncertainty in a data-driven way using distributionally robust optimization. Assuming a specific recovery time, we formulate a multi-stage stochastic convex program and develop a decomposition algorithm based on stochastic dual dynamic programming (SDDP). Extensive computational results are shown and operational insights on battery utilization, component hardening, and length of recovery phase are obtained by performing analyses from stochastic disruption-aware solutions.
