Distributionally Robust Co-Optimization of Transmission Network Expansion Planning and Penetration Level of Renewable Generation

Abstract: Expansion of transmission networks can significantly improve the penetration level of renewable generation. However, existing studies have not explicitly revealed and quantified the trade-off between the investment cost and the renewable penetration level. This paper proposes a distributionally robust optimization model to minimize transmission network expansion cost under uncertainty and maximize the penetration level of renewable generation. The proposed model includes distributionally robust joint chance constraints, which maximize the minimum expectation of the renewable utilization probability among a set of certain probability distributions within an ambiguity set. The proposed formulation yields a two-stage robust optimization model with variable bounds of the uncertain sets, which is hard to solve. By applying the affine decision rule, second-order conic reformulation, and duality, we reformulate it into a single-stage standard robust optimization model and solve it efficiently via commercial solvers. Case studies are carried on the Garver 6- and IEEE 118-bus systems to illustrate the validity of the proposed method.