A Distributionally Robust Resilience Enhancement Strategy for Distribution Grids Considering Decision-Dependent Contingencies

Abstract

When performing resilience enhancement for distribution grids, suboptimal strategies induced by misspecified contingency models may lead to unanticipated regrets in retrospective analyses.However, there are two obstacles for reliably modeling uncertain contingencies: 1) decision-dependent uncertainty (DDU) resulting from different line hardening decisions, and 2) distributional ambiguity due to limited outage information under extreme weather events (EWEs). To address these two challenges, this paper constructs scenario-wise decision-dependent ambiguity sets (SWDD-ASs), where the DDU and distributional ambiguity inherent in EWE-induced contingencies are simultaneously captured under each possible EWE scenario. Then, a two-stage trilevel decisiondependent 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. Then, the DD-DRRE model are equivalently recast to a MILP-based master problem and multiple scenario-wise subproblems, facilitating the utilization of a customized column-and-constraint generation (C&CG) algorithm. Finally, numerical tests 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, which can serve as a useful reference for planners with different budgets and risk-aversion levels.