Distributionally Robust Distribution Network Configuration Under Random Contingency

TL;DR

This paper introduces a distributionally robust optimization model for designing distribution network topologies that effectively handle uncertain contingencies, improving reliability and reducing load shedding compared to traditional methods.

Contribution

It develops a novel DRO model that explicitly accounts for contingency uncertainty using moment-based ambiguity sets, enhancing network design robustness.

Findings

DRO model outperforms classical robust optimization in out-of-sample tests.

The approach effectively minimizes expected load shedding under contingency uncertainty.

Numerical case studies validate the model's improved performance.

Abstract

Topology design is a critical task for the reliability, economic operation, and resilience of distribution systems. This paper proposes a distributionally robust optimization (DRO) model for designing the topology of a new distribution system facing random contingencies (e.g., imposed by natural disasters). The proposed DRO model optimally configures the network topology and integrates distributed generation to effectively meet the loads. Moreover, we take into account the uncertainty of contingency. Using the moment information of distribution line failures, we construct an ambiguity set of the contingency probability distribution, and minimize the expected amount of load shedding with regard to the worst-case distribution within the ambiguity set. As compared with a classical robust optimization model, the DRO model explicitly considers the contingency uncertainty and so provides a less conservative configuration, yielding a better out-of-sample performance. We recast the proposed model to facilitate the column-and-constraint generation algorithm. We demonstrate the out-of-sample performance of the proposed approach in numerical case studies.