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

TL;DR

This paper proposes a novel distributionally robust optimization framework for enhancing the resilience of distribution networks against extreme weather events, explicitly accounting for decision-dependent uncertainties and limited outage data.

Contribution

It introduces scenario-wise decision-dependent ambiguity sets and a two-stage trilevel model, improving resilience strategies under worst-case contingencies with a MILP reformulation and specialized algorithm.

Findings

Significant improvement in out-of-sample performance over traditional methods

Quantitative estimation of the value of ambiguity and distributional information

Effective decision strategies for line hardening, DG allocation, and network reconfiguration

Abstract

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.