融合数据驱动的主配网分布式协同优化调度OA

With the large-scale integration of renewable energy into power grids,the uncertainty of power output poses new challenges to grid dispatch operations,intensifying the demand for coordinated optimization between the main grid and the distribution networks.To address the challenges of incomplete topological information in the distribution network and the limitations of conventional centralized optimization approaches,this paper proposes a distributed collaborative optimization strategy for the main grid and the distribution networks that integrate data-driven techniques and the analytical target cascading(ATC)method.Firstly,a power flow model based on data-driven approach is developed for the distribution network to explore implicit correlations between nodal voltages and power injections and overcome the restrictions of topological information missing.Subsequently,an ATC-based bi-level coordination framework is designed,the alternating correction mechanism for boundary parameters is used to decompose the global optimization dispatch model and the fitting result of power flow is embedded.Numerical experiments on an integrated grid composed of the IEEE 118 node transmission system and multiple IEEE 33 node distribution systems validate the effectiveness of the proposed method in distributed coordinated dispatch for transmission and distribution networks.