A Distributionally Robust Optimization Scheduling Considering Distribution of Tie-Line Endpoints

Abstract

As power systems scale up and uncertainties deepen, traditional centralized optimization approaches impose significant computation burdens on large-scale optimization problems, introducing new challenges for power system scheduling. To address these challenges, this study formulates a distributionally robust optimization (DRO) scheduling model that considers source-load uncertainty and is solved using a novel distributed approach that considers the distribution of tie-line endpoints. The proposed model includes a constraint related to the transmission interface, which consists of several tie-lines between two subsystems and is specifically designed to ensure technical operation security. In addition, we find that tie-line endpoints enhance the speed of distributed computation, leading to the development of a power system partitioning approach that considers the distribution of these endpoints. Further, this study proposes a distributed approach that employs an integrated algorithm of column-and-constraint generation (C&CG) and subgradient descent (IACS) to address the proposed model across multiple subsystems. A case study of two IEEE test systems and a practical provincial power system demonstrates that the proposed model effectively ensures system security. Finally, the scalability and effectiveness of the distributed approach in accelerating problem-solving are confirmed.