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).