The increasing uncertainty caused by volatile renewable generation and random electricity demand has always been a critical challenge in power system operations. Robust optimization (RO) is a powerful tool for effectively addressing this uncertainty. As the interplay between uncertain factors and decision-making becomes more prevalent

RO with decision-dependent uncertainty (DDU) has attracted increasing attention. DDU significantly changes how the uncertainty set in RO is modelled and how the problems are solved. This study provides a comprehensive overview of the recent developments in RO with DDU for power system problems. We begin by introducing various models of DDU

classified according to their underlying causes. Next

we summarize the state-of-the-art solution algorithms for RO with DDU

such as variants of the column-and-constraint generation (C&CG) algorithm

variants of Benders decomposition

and multiparametric programming. Furthermore

we explore the application of RO with DDU in power systems. Based on our findings

we propose several research directions that may be valuable for future studies.