摘要
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.
基金
supported by the National Key R&D Program of China(No.2022YFB2403400)。
