A Mixed-Integer Distributionally Robust Chance-Constrained Model for Optimal Topology Control in Power Grids with Uncertain Renewables

Abstract

This paper proposes a distributionally robust chance-constrained (DRCC) optimization model for optimal topology control in power grids overwhelmed with significant renewable uncertainties. A novel moment-based ambiguity set is characterized to capture the renewable uncertainties with no knowledge on the probability distributions of the random parameters. A distributionally robust optimization (DRO) formulation is proposed to guarantee the robustness of the network topology control plans against all uncertainty distributions defined within the moment-based ambiguity set. The proposed model minimizes the system operation cost by co-optimizing dispatch of the lower-cost generating units and network topology—i.e., dynamically harnessing the way how electricity flows through the system. In order to solve the problem, the DRCC problem are reformulated into a tractable mixed-integer second order cone programming problem (MISOCP) which can be efficiently solved by off-the-shelf solvers. Numerical results on the IEEE 118-bus test system verify the effectiveness of the proposed network reconfiguration methodology under uncertainties.