Abstract
This article describes robust optimization, which involves modeling uncertain parameters within a pre-specified uncertainty set and allows identifying the worst-case uncertainty realization. An overview of basic concepts of robust optimization is provided, describing how uncertainty is characterized within a robust optimization framework, formulating a robust optimization problem without and with recourse, and describing the column-and-constraint generation algorithm that is applied to solve two-stage adjustable robust optimization problems with continuous recourse variables. A practical example in the field of power systems is presented, clarifying the theoretical concepts described.
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