A stochastic programming approach using multiple uncertainty sets for AC robust transmission expansion planning

Abstract

We present a transmission expansion planning model to find the most desirable configuration of transmission grid under the robust optimization. Unlike the common existing approaches where only one uncertainty set is considered, we propose to model multiple uncertainty sets each with its own probability. In this regard, we devise a method to construct the corresponding uncertainty sets from the historical data based on minimum covering circle. Moreover, we introduce a new criterion for uncertainty budget based on which the planner can efficiently control the conservative level of the optimization problem. Using this new criterion, the planner is capable to make a trade-off between the tractability and solution quality. The robust planning method is formulated as a tri-level min–max–min optimization model in which the classic column-and-constraint generation technique is used to solve the problem. The proposed strategy is implemented on the IEEE 118-bus power system to show the effectiveness of the model. The results indicate the short-term uncertainties in demand and intermittent renewable generation can be efficiently captured by the presented strategy. The comparable results also corroborate the superiority of the model over the single uncertainty-set-based models as well as over the pure stochastic models. • A robust transmission expansion planning scheme is presented. • Short-term demand and wind speed uncertainties are taken into account. • Multiple uncertainty sets are considered rather than a single one. • A linearized network model is utilized. • The robust trilevel approach is solved using KKT and CCG.