基于Pair Copula的多维风电功率相关性分析及建模

大规模风电场的接入使风电相关性更加复杂,合理描述多风电场出力的随机性和相关性特性,对准确分析风电对电力系统运行的影响具有重要意义。现有的Copula等方法能较准确描述二元相关性,但对于更高维模型的相关性描述则不够准确。基于此,提出了基于C藤Pair Copula的风电功率高维相关性模型,以及相应的采样方法。Pair Copula能够描述风电功率两两之间不同的相关性结构,从而能较好描述复杂的多维相关性,且建模步骤简单,使用灵活,适用范围广。对澳大利亚多个风电场出力样本进行分析和建模,验证了所提方法的优越性。最后通过IEEE 118节点系统的概率潮流算例,说明了合理刻画风电功率相关性可以更准确地分析含风电接入的电力系统运行特性。

Correlation Analysis and Modeling of Multiple Wind Power Based on Pair Copula

Large-scale integration of wind farms leads to the complex dependence among wind power outputs. It is important to model the stochastic and dependent wind generation accurately to analyze the impact of wind generation on power system operation. Current methods, such as the Copula theory, are accurate enough for describing two dependent random variables. However, they are inadequate for modeling more random variables as accurately. Thus, a high-dimensional probabilistic model is proposed for dependent wind power outputs based on the canonical-vine Pair Copula theory. The corresponding sampling method is also introduced. Pair Copula can describe different patterns of dependence between pairs of wind power outputs. Hence high-dimensional wind power outputs with complex dependence can be modeled accurately. Moreover, The Pair Copula model can be easily constructed and has wide applicability as well as flexibility. The modeling and analysis of wind generation in a number of wind farms in Australia are implemented to prove the effectiveness of the proposed model. Finally, the probabilistic load flow of an IEEE 118-bus system is solved. Simulation results show that the operation characteristics of power systems incorporating wind farms can be analyzed more accurately if dependent wind power outputs are rationally described. This work is supported by National Natural Science Foundation of China (No. 51307107, No. 51477098), National Key Technologies R&D Program (No. 2015BAA01B02) and Ph.D. Programs Foundation of Ministry of Education of China (No. 20130073120034).