  When performing the resilience enhancement for distribution networks, there
are two obstacles to reliably model the uncertain contingencies: 1)
decision-dependent uncertainty (DDU) due to various line hardening decisions,
and 2) distributional ambiguity due to limited outage information during
extreme weather events (EWEs). To address these two challenges, this paper
develops scenario-wise decision-dependent ambiguity sets (SWDD-ASs), where the
DDU and distributional ambiguity inherent in EWE-induced contingencies are
simultaneously captured for each possible EWE scenario. Then, a two-stage
trilevel decision-dependent distributionally robust resilient enhancement
(DD-DRRE) model is formulated, whose outputs include the optimal line
hardening, distributed generation (DG) allocation, and proactive network
reconfiguration strategy under the worst-case distributions in SWDD-ASs.
Subsequently, the DD-DRRE model is equivalently recast to a mixed-integer
linear programming (MILP)-based master problem and multiple scenario-wise
subproblems, facilitating the adoption of a customized column-and-constraint
generation (C&CG) algorithm. Finally, case studies demonstrate a remarkable
improvement in the out-of-sample performance of our model, compared to its
prevailing stochastic and robust counterparts. Moreover, the potential values
of incorporating the ambiguity and distributional information are
quantitatively estimated, providing a useful reference for planners with
different budgets and risk-aversion levels.
