Combined Gaussian Mixture Model and cumulants for probabilistic power flow calculation of integrated wind power network

With the large-scale wind power penetration, probabilistic power flow plays an important role in power system uncertainty analysis. This paper proposes a novel Gaussian Mixture Model to fit the probability density distribution of short-term wind power forecasting errors with the multimodal and asymmetric characteristics. Cumulants are used to calculate mean value and deviation of state variables for each random combination result of Gaussian components. Probabilistic power flow is acquired by summing up all the Gaussian probability density functions with weights counted by the product of Gaussian components in each random combination. Parallel probabilistic power flow computation by use of the Gaussian Mixture Model and cumulants could simplify the calculation procedure in large scale of integrated wind power network. Case studies are carried out in modified IEEE 57-bus test system to verify advantages of the novel approach. Results show that the computational efficiency and accuracy are well improved in the proposed method