基于数据驱动不确定集的微电网两阶段鲁棒优化调度  

Two-stage Robust Optimization Scheduling of Microgrids Based on Data-driven Uncertain Sets

作　　者：魏斌 乔森 孟润泉[1] 李嘉庚 WEI Bin;QIAO Sen;MENG Runquan;LI Jiageng(Shanxi Key Province Laboratory of Power System Operation and Control,Taiyuan University of Technology,Taiyuan 030024,China)

机构地区：[1]电力系统运行与控制山西省重点实验室(太原理工大学),太原030024

出　　处：《高电压技术》2025年第2期852-863,I0028-I0030,共15页High Voltage Engineering

基　　金：山西省基础研究计划青年项目(202203021222109)。

摘　　要：鲁棒优化作为应对风电等新能源出力不确定性的重要工具,广泛应用于微电网优化调度中。传统的不确定集不够紧凑,无法准确刻画风电不确定性,同时不确定集包围的数据中可能存在部分异常值,导致调度结果过于保守。针对上述问题,提出了一种基于数据驱动不确定集的微电网两阶段鲁棒优化调度方法。首先,通过风电历史数据构建条件正态Copula(conditional normal copula,CNC)模型,再将日前风电预测值输入CNC模型生成次日风电功率样本。然后,通过支持向量聚类(support vector clustering,SVC)和维度分解构建考虑风电时间相关性的数据驱动不确定集。该不确定集可更为准确地刻画风电不确定性,并将风电数据中的异常值排除在外,从而在降低鲁棒优化保守性的同时具备异常值抵抗性。其次,提出了基于上述不确定集的两阶段鲁棒优化调度模型,并采用列约束生成(column and constraint generation,C&CG)算法求解。最后通过仿真证明了相较传统不确定集,本文构建的不确定集保守性更低,同时对风电数据异常值具有良好的抵抗性。Robust optimization,as an important tool for addressing the uncertainty of new energy output such as wind power,is widely used in microgrid optimization and scheduling.Traditional uncertain sets are not compact enough to ac-curately characterize wind power uncertainty,and there may be some outliers in the data enclosed by uncertain sets,resulting in overly conservative scheduling results.In response to the above issues,this paper proposes a two-stage robust optimization scheduling method for microgrids based on data-driven uncertain sets.Firstly,a conditional normal copula(CNC)model is constructed based on historical wind power data,and then the predicted values of wind power from the previous day are input into the CNC model to generate the next day’s wind power samples.Then,a data-driven uncer-tainty set considering the temporal correlation of wind power is constructed through support vector clustering(SVC)and dimension decomposition.This uncertain set can more accurately depict the uncertainty of wind power and exclude outli-ers in wind power data,thereby reducing the conservatism of robust optimization while possessing outlier resistance.Secondly,a two-stage robust optimization scheduling model based on the aforementioned uncertain set is proposed and solved using the column and constraint generation(C&CG)algorithm.Finally,simulations prove that the uncertainty set constructed in this paper has lower conservatism compared to traditional uncertainty sets,and has good resistance to out-liers in wind power data.

关 键 词：鲁棒优化 数据驱动 支持向量聚类 时间相关性 抵抗性 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]