*4.1. Robust Optimization Algorithm*

RO is designed to deal with uncertainty problems. Unlike stochastic optimization, it represents uncertain variables in interval form. It can be said that the uncertainty of variables is fully considered in modeling. The result of RO is the most conservative result. Any value in the set of uncertain variables can be satisfied. Therefore, it is especially suitable for microgrid systems that are very important for security.

RO first models the uncertain variables, then transforms the uncertain variables into deterministic variables according to the robust equal conversion, and finally solves the robust equal conversion model to obtain the robust optimal solution [28,29].
