Probabilistic power flow based on slice sampling for distribution network containing distributed generations

2018 International Conference on New Energy and Future Energy System

Zhang, Y.^1 ; Zhang, X.Y.^1 ; Wang, K.^2 ; Chen, W.^1 ; Wang, X.L.^1

College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou

730050, China^1

State Grid Gansu Electric Power Company Electric Power Research Institute, Lanzhou

730050, China^2

关键词: Computational accuracy;    Gibbs sampling;    Iterative operation;    Markov chain monte carlo simulation;    Monte carlo simulations (MCS);    Power flow calculations;    Probabilistic power flow;    Slice samplings;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/188/1/012088/pdf
DOI  :  10.1088/1755-1315/188/1/012088

来源: IOP

【 摘 要 】

With the wide application of distributed generations (DG) in power system, some problems appear to the power flow calculation of distribution network. In the methods of probabilistic power flow calculation based on Monte Carlo simulation (MCS), the Gibbs sampling algorithm needs a large number of complex iterative operations to get more accurate results. Aiming at the problem of the algorithm, a Markov Chain Monte Carlo (MCMC) simulation method based on slice sampling algorithm is proposed and applied to probabilistic power flow calculation of the distribution network containing distributed generation. Finally, the IEEE-33 node system is used for simulation. The results show that the slice sampling algorithm can significantly improve the computational accuracy of the traditional MCMC method. In the meantime, the slice sampling is faster and more stable than Gibbs sampling under the same number of sampling iterations.