考虑时变相关性的配电网超短期条件概率潮流预测  

Time-dependent Correlation-based Ultra-short Term Forecasting of Conditional Probabilistic Power Flow in Distribution Networks

作　　者：肖天颖 裴玮[1,2] 叶华 牛耕[1,2] 肖浩 齐智平[1] XIAO Tianying;PEI Wei;YE Hua;NIU Geng;XLAO Hao;QI Zhiping(Institute of Electrical Engineering,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院电工研究所,北京100190 [2]中国科学院大学,北京100049

出　　处：《高电压技术》2018年第7期2362-2371,共10页High Voltage Engineering

基　　金：国家自然科学基金(51377152;51607170);中科院前沿科学重点研究项目(QYZDB-SSW-JSC024);中科院青年创新促进项目(2016127);北京市科委专项课题(Z161100000416003)~~

摘　　要：为了提高概率潮流预测结果的准确性,提出了考虑时变相关性的配电网条件概率潮流预测方法。首先分析了风机和光伏发电功率预测误差与其波动性的相关性;然后采用基于动态相关系数的多变量广义条件异方差模型（DCC_MVGARCH）,分析了预测误差与功率波动值之间、风机与光伏之间条件相关系数的时变性;最后,以功率波动性为条件相关因素,基于时变条件相关系数,建立了风光联合条件概率分布,并采用基于直接抽样的蒙特卡洛模拟法对配电网的条件概率潮流进行求解。以IEEE37节点系统为仿真案例,在其中插入一定容量的风机和光伏,分别在5、15 min时间分辨率下对所提时变概率潮流求解方法进行仿真。结果表明考虑时变相关性的条件概率潮流能够在根据波动性的大小调整概率潮流的分布区间,使得潮流区间的分布更合理,与采用固定相关系数相比,减小了概率潮流预测的误差。We put forward a forecasting method of time-dependent correlation-based conditional probabilistic power flow for increasing the accuracy of power flow in distributed power network. Firstly, the correlation of fluctuations and forecast errors of distributed generation＇s（DG） power was analyzed. Secondly, a multiple-variable generalized autoregressive conditional heteroscedasticity model based on dynamic correlation coefficient（DCC_MVGARCH） was proposed to directly analyze the time-varying correlation between participants such as DG power fluctuations and its forecast error, wind power and photovoltaic（PV） power generation, etc. Finally, considering the power fluctuation as the conditional correlation factor, the joint conditional probability distribution of WT and PV was elaborated based on the time-varying correlation coefficient. By using the Monte-Carlo simulation method, the conditional power flow calculation was then implemented. A modified IEEE 37-bus power system with a certain penetration of WT and PV was employed to validate the proposed method. The simulation was performed every 5 minutes and 15 minutes, respectively. The results show that the conditional probabilistic power flow with time-varying correlation can adjust the distribution of probabilistic power flow according to the magnitude of volatility, which makes the distribution of power flow more reasonable, and improves the credibility and accuracy of probabilistic power flow forecasting results.

关 键 词：配电网 概率潮流 风光相关性 条件概率 时变条件相关系数 DCC_MVGARCH 

分 类 号：TM744[电气工程—电力系统及自动化]