An adaptive robust optimization model for transmission expansion planning considering uncertain intervals

The degree of uncertainty level has been increasing in power grids due to the growing integration of renewable generation. Stochastic programming (SP) and robust optimization (RO) or a hybrid SP/RO have been employed to cope with uncertainty in power system planning studies. In particular, RO has attracted much attention in this context due to its effectiveness and reliable implementation. Hence, several RO-based models, methodologies, and solution algorithms have been presented to improve the preciseness when modeling uncertainties. To employ RO, it is required to construct an uncertainty set that can be predicted using historical data. The current literature assumes that this uncertainty set is fully known to the planner, which does not reflect a real issue. In this paper, we propose a new model where the intervals defining polyhedral uncertainty sets are supposed to be the uncertain parameters. These uncertain intervals are modeled using either SP or RO. This means that the lower and upper bounds of the intervals are supposed to follow a specific probability density function (PDF), or they lie in specific intervals. The numerical studies show the methodology's effectiveness and imply that it is crucial to model uncertain intervals in the TEP problem.