Robust Distribution Network Reconfiguration Using Mapping-based Column-and-Constraint Generation

TL;DR

This paper presents a robust optimization approach for distribution network reconfiguration that incorporates renewable generator resizing and decision-dependent uncertainties, using a novel mapping-based C&CG algorithm to improve computational efficiency.

Contribution

It introduces a new two-stage robust optimization model with DDU for network reconfiguration and proposes a mapping-based C&CG algorithm to solve it efficiently.

Findings

The proposed algorithm reduces computational complexity.

The model effectively handles renewable uncertainties.

Case studies validate solution optimality and robustness.

Abstract

The integration of intermittent renewable energy sources into distribution networks introduces significant uncertainties and fluctuations, challenging their operational security, stability, and efficiency. This paper considers robust distribution network reconfiguration (RDNR) with renewable generator resizing, modeled as a two-stage robust optimization (RO) problem with decision-dependent uncertainty (DDU). Our model optimizes resizing decisions as the upper bounds of renewable generator outputs, while also optimizing the network topology. We design a mapping-based column-and-constraint generation (C&CG) algorithm to address the computational challenges raised by DDU. Sensitivity analyses further explore the impact of uncertainty set parameters on optimal solutions. Case studies demonstrate the effectiveness of the proposed algorithm in reducing computational complexity while ensuring solution optimality.