基于数据驱动多面体集合的交直流混合配电网鲁棒调度方法

针对现有配电网鲁棒调度方法缺乏对不确定参数相关性问题的考虑，提出了一种基于数据驱动多面体集合的交直流混合配电网鲁棒调度方法。首先，构建分布式光伏出力的传统多面体集合，利用历史数据驱动形成了相关性包络图，通过弯曲多面体集合边界，建立了相关性多面体集合模型。然后，在此基础上，针对相关性多面体集合存在鲁棒性差和保守性大的问题，建立了数据驱动的多面体集合模型。进一步，建立了基于数据驱动多面体集合的交直流混合配电网鲁棒调度模型，并采用列与约束生成(column and constraint generation, CCG)算法对鲁棒调度模型进行求解。最后，改进的IEEE33节点系统仿真结果表明，基于数据驱动多面体集合的交直流混合配电网鲁棒调度方法可以减少优化结果的保守性，提高其鲁棒性，证明了所提出方法的有效性。

Robust scheduling method for AC/DC hybrid distribution networks based on a data-driven polyhedral set

data-driven polyhedron-based robust scheduling method for AC/DC hybrid distribution networks is proposed to address the lack of consideration of uncertain parameter correlation in existing robust scheduling methods. First, a traditional polyhedral set of distributed photovoltaic output is constructed, and a correlation envelope graph is driven by historical data, and the correlation polyhedral set model is established by bending the polyhedral set boundary. Second, based on this, a data-driven polyhedral set model is established to address the issues of poor robustness and high conservatism in correlated polyhedral sets. Then, a robust scheduling model for AC/DC hybrid distribution networks based on data-driven polyhedral sets is established, and a column and constraint generation (CCG) algorithm is used to analyze the robust scheduling model. Finally, the simulation results of the improved IEEE33 node system show that the proposed scheduling method can reduce the conservatism of the optimization results and improve their robustness, proving the effectiveness of the method.