# Effective Budget of Uncertainty for Classes of Robust Optimization

## TL;DR

This paper introduces a new robust optimization method that reduces conservatism by focusing on the effective parts of uncertainty, improving solutions in resource uncertainty problems like power dispatch with wind variability.

## Contribution

It proposes a tractable two-stage robust optimization approach that identifies and excludes ineffective uncertainty regions, leading to less conservative solutions.

## Key findings

- Less conservative solutions compared to traditional methods
- Effective identification of uncertainty regions
- Application demonstrated in power dispatch with wind uncertainty

## Abstract

Robust optimization (RO) tackles data uncertainty by optimizing for the worst-case scenario of an uncertain parameter and, in its basic form, is sometimes criticized for producing overly-conservative solutions. To reduce the level of conservatism in RO, one can use the well-known budget-of-uncertainty approach which limits the amount of uncertainty to be considered in the model. In this paper, we study a class of problems with resource uncertainty and propose a robust optimization methodology that produces solutions that are even less conservative than the conventional budget-of-uncertainty approach. We propose a new tractable two-stage robust optimization approach that identifies the "ineffective" parts of the uncertainty set and optimizes for the "effective" worst-case scenario only. In the first stage, we identify the effective range of the uncertain parameter, and in the second stage, we provide a formulation that eliminates the unnecessary protection for the ineffective parts, and hence, produces less conservative solutions and provides intuitive insights on the trade-off between robustness and solution conservatism. We demonstrate the applicability of the proposed approach using a power dispatch optimization problem with wind uncertainty. We also provide examples of other application areas that would benefit from the proposed approach.