Robust Operation of Distribution Networks: Generalized Uncertainty Modelling in Confidence-Level-Based Information Gap Decision

TL;DR

This paper introduces a generalized uncertainty modelling framework for distribution network operation, improving robustness and economic performance under renewable and load uncertainties using a confidence-level-based IGDT approach.

Contribution

It develops a novel CL-IGDT framework with enhanced uncertainty expressiveness, a Fibonacci-Parametric CCG algorithm, and a cut-recycling strategy for efficient robust optimization.

Findings

The framework effectively balances robustness and economic performance.

The proposed algorithm demonstrates superior computational efficiency.

Case studies validate the model's practical effectiveness.

Abstract

This paper studies the robust optimal operation of distribution networks (DNs) under renewable generation and load demand uncertainties, seeking an improved trade-off between robustness and economic performance. Building upon information gap decision theory (IGDT), a generalized uncertainty modelling is proposed to enhance the expressiveness of the uncertainty characterization. The proposed modelling captures both symmetric and asymmetric uncertainty features, and supports linear or nonlinear expansion of the uncertainty sets driven by confidence level. This advancement leads to the development of a confidence-level-based IGDT (CL-IGDT) framework for DN operation. To solve the resulting model, its equivalence to a family of two-stage robust optimization problems (TSROs) is established, enabling a Fibonacci search over the confidence level. To further improve computational efficiency, a cut-recycling strategy is proposed to exploit invariant information across TSROs. These techniques are integrated into a novel Fibonacci-Parametric Column-and-Constraint Generation algorithm with guaranteed asymptotic convergence. Case studies validate the effectiveness of the proposed framework and demonstrate the performance advantages of the proposed algorithm.